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CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms
Abstract
Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
... Despite their extensibility, these simulators not only scale very poorly (making it problematic to evaluate more elaborate IaaS scenarios where sometimes thousands of physical machines collaborate), but they also require complex setup procedures to be precise (e.g., one should model every possible application in the system to receive realistic results). Finally, there are simulators that introduce some assumptions in the system that reduce the precision of the simulations but reach unprecedented speeds [5,9,10]. Unfortunately, despite having clear advantages, they are too specific to allow investigations on internal IaaS changes (e.g., GroudSim only models external interfaces of clouds, SimGrid merely focuses on virtualisation, and CloudSim has conflicting extensions -e.g., power modelling is not available while using networking). ...
... According to the findings of this article, the system's simulated behaviour matches real-life experiments with negligible error (in terms of application execution time, larger scale network transfers and energy consumption). For larger scale experiments, DISSECT-CF was validated with proven results from two other simulators that are close to the new simulator's functionality (namely CloudSim [9] and GroudSim [10]). Then, performance of these two simulators was compared to the newly proposed one. ...
... CloudSim [29] is amongst the most popular IaaS cloud simulators. It was initially based on GridSim (a widely used grid simulator developed by the same research institute - [30]) but, after some performance and reliability issues, it was completely rewritten so it uses only some concepts (e.g., Cloudlet -Gridlet analogy) from its predecessor [9]. CloudSim introduced the simulation of virtualised data centres mostly focusing on computational intensive tasks and data interchanges between data centres. ...
Infrastructure as a service (IaaS) systems offer on demand virtual infrastructures so reliably and flexibly that users expect a high service level. Therefore, even with regards to internal IaaS behaviour, production clouds only adopt novel ideas that are proven not to hinder established service levels. To analyse their expected behaviour, new ideas are often evaluated with simulators in production IaaS system-like scenarios. For instance, new research could enable collaboration amongst several layers of schedulers or could consider new optimisation objectives such as energy consumption. Unfortunately, current cloud simulators are hard to employ and they often have performance issues when several layers of schedulers interact in them. To target these issues, a new IaaS simulation framework (called DISSECT-CF) was designed. The new simulator's foundation has the following goals: easy extensibility, support energy evaluation of IaaSs and to enable fast evaluation of many scheduling and IaaS internal behaviour related scenarios. In response to the requirements of such scenarios, the new simulator introduces concepts such as: a unified model for resource sharing and a new energy metering framework with hierarchical and indirect metering options. Then, the comparison of several simulated situations to real-life IaaS behaviour is used to validate the simulator's functionality. Finally, a performance comparison is presented between DISSECT-CF and some currently available simulators.
... Therefore, NetworkCloudSim simulator provides different features which are needed for most research directions [15]. [16] CloudSim simulator is the most used simulator because of its simplicity and flexibility. It is implemented using Java language without graphical user interface. ...
... Therefore, CloudSim becomes one of the most used simulators. Figure 1 shows the CloudSim architecture [16]. ...
... It has an provisioning policy for bandwidth and memory to allocate and divide the whole bandwidth and memory across host's virtual machine. It has virtual machine scheduling algorithm (e.g., time share -space share -and any customized algorithm) that responsible for allocating processing element to virtual machines [16]. The networked data center architecture is illustrated in Figure 4. ...
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment, and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the three-tire data center as a case study with considering the data locality feature.
... where ( ) is the fitness value of the mass m at iteration t , ( ) is the global worst fitness in iteration t, and ( ) is the best one at that iteration. Further, ( ) is the current mass value where ( ) is a vector that holds the mass values of neighbor masses' objects at iteration t as shown in (23). This is because ∑ ( ) is the summation of other masses' values. ...
... - (23) Second, the relevance gravitational forces exerted on mass m by another mass b is calculated based on (24): Then, the total result gravitational forces exerted on mass m on the d th direction at time (t) are as in (26): ...
... The performance analysis of the proposed algorithm is carried out in a cloud simulator. The simulator CloudSim [23] is one of the best simulators for experimental purposes. This simulator is a generalized simulation framework that allows modeling, simulation, and experimenting with cloud computing infrastructure and application services. ...
In cloud environments, load balancing task scheduling is an important issue that directly affects resource utilization. Unquestionably, load balancing scheduling is a serious aspect that must be considered in the cloud research field due to the significant impact on both the back end and front end. Whenever an effective load balance has been achieved in the cloud, then good resource utilization will also be achieved. An effective load balance means distributing the submitted workload over cloud VMs in a balanced way, leading to high resource utilization and high user satisfaction. In this paper, we propose a load balancing algorithm, Binary Load Balancing-Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (Bin-LB-PSOGSA), which is a bio-inspired load balancing scheduling algorithm that efficiently enables the scheduling process to improve load balance level on VMs. The proposed algorithm finds the best Task-to-Virtual machine mapping that is influenced by the length of submitted workload and VM processing speed. Results show that the proposed Bin-LB-PSOGSA achieves better VM load average than the pure Bin-LB-PSO and other benchmark algorithms in terms of load balance level.
... In one study [31], PlanetSim was integrated with GridSim for evaluating the performance of decentralized and coordinated scheduling of scientific applications across multiple computational sites (clusters, supercomputers, etc.). Cloud computing model were simulated in GreenCloud [32], iCanCloud [33], Cloudsim [34] and its variants (CloudAnalyst [35], NetworkCloudsim [36], EMUSIM [37], MDCSim [38]) has been described and compared [39]. -GreenCloud, which is a packet-level simulator (developed by extending NS-2) is capable of modelling behaviours of network links, switches, gateways, and other hardware resources (CPU and storage) in a cloud datacentre. ...
... It is able to simulate different type of MapReduce applications with the ability to study with good accuracy the effect of dozens of job configuration parameters on the job performance. However, it was modelled and simulated using SimJava discrete event engine that has intrinsic weakness such as increased kernel complexity [44] and lack of support of some advanced operations [34]. Because of this, the SimJava layer has been removed from Cloudsim 2.0 onwards. ...
... support for simulation of IoT big data processing using MapReduce model or steam model in cloud computing environment support for modelling and simulation of large scale multiple IoT applications to run simultaneously in a shared cloud data centres support for modelling network and storage delays existing in the processing of IoT applications 4. DESIGN AND IMPLEMENTATION OF IOTSIM Cloudsim [34], is an extensible simulation toolkit that enables modelling and simulation of cloud computing environments and application provisioning. It has many features, which make us choose it for building our simulator for analysing Iota Application. ...
A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.
... For private clouds, some simulators do not consider energy consumption [4]- [6], [9]; others do not take hardware heterogeneity into account [6], [8], [9]. Most simulators do not allow modeling applications composed of multiple functions with data dependencies [2], [3], [5]- [7], [9], [10]. In certain studies, QoS cannot be enforced at the granularity of a single-user request [2]- [4], [6]- [8], [10], [11]. ...
... Most simulators do not allow modeling applications composed of multiple functions with data dependencies [2], [3], [5]- [7], [9], [10]. In certain studies, QoS cannot be enforced at the granularity of a single-user request [2]- [4], [6]- [8], [10], [11]. ...
... CloudSim [2] is the ubiquitous tool for large-scale cloud deployment experiments. It targets the different service models in traditional cloud computing. ...
Optimizing a serverless platform for Quality of Service is known to be a hard problem that requires experimenting with production data. For this sake, we propose HeROsim, a Heterogeneous Resources Orchestration simulator for the serverless service model. HeROsim allows evaluating resource allocation and task placement policies. HeROsim is an open source, fine-grained discrete-event simulator that considers data and temporal dependencies between functions deployed on heterogeneous resources. It replays execution traces from previously characterized workloads, extracts Quality of Service metrics from the scenarios and presents them to the user by generating summary charts.
... where T cpu total (h j ) denotes the total runtime of host h j and T cpu over (h j ) denotes the overload time of h j in T cpu total (h j ). The above values can be calculated directly with the help of the platform CloudSim [38]. ...
... The experiments described herein were conducted using CloudSim [38] to simulate a cloud data center comprising 800 heterogeneous PMs. The specific types of PMs and VMs utilized within the cloud data center are listted in Tables 3 and 4, respectively. ...
In cloud data centers, excessive or insufficient resource utilization of physical machines(PMs) can have adverse effects. Resource utilization should be controlled reasonably to achieve an optimal balance among energy consumption, resource waste rate, and quality of service(QoS). To address this issue, the virtual machine placement problem is abstracted as a multi-objective optimization problem, with the optimization objective of minimizing the energy consumption of cloud data centers, resource waste rate, and probability of host overload. A novel multi-objective flower pollination algorithm based on decomposition(MOFPA/D) is proposed by applying a discrete approach to the flower pollination algorithm (FPA) and then integrating with the well-established multi-objective evolutionary algorithm based on decomposition optimization framework(MOEA/D). Subsequently, the aforementioned optimization problem is solved by using the proposed algorithm, which results in a globally optimal virtual machine placement algorithm. Moreover, the integration of this algorithm with the proposed host overload-detection algorithm, a virtual-machine-selection algorithm, and a low-load host-detection algorithm enables the development of a virtual machine consolidation method, named EUQ-VMC, which aims to achieve efficient resource utilization and service-quality perception. Simulation results show that the EUQ-VMC method significantly reduces energy consumption and enhances resource utilization and QoS compared with other methods.
... Dsouza et al. [20] describe the research challenges in policy management for fog computing and propose a policy-driven security management approach including policy analysis and its integration with fog computing paradigm. Such an approach is critical for supporting secure sharing, and data reuse in heterogeneous Fog environments. ...
... The simulation environment was implemented in CloudSim [20] by extending the basic entities in the original simulator. Fog devices were realized by extending the Datacenter class, while stream operators were modeled as a VM in CloudSim. ...
The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in centralized cloud does not scale to requirements of such environment. This is because, there are applications such as health monitoring and emergency response that require low latency and delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides background and motivations on emergence of Fog computing and defines its key characteristics. In addition, a reference architecture for Fog computing is presented and recent related development and applications are discussed.
... leasing operation-qualified hardware configurations or operating system versions). On the other hand, a local emulation or simulation environment [8][9][10] can be used as a sandbox to test the service. This is likely to be less performant, but can be a cheaper and easier alternative for quickly checking basic functionality, to try configuration settings or generate test traffic in (parts of) the network service. ...
... Distinct open-source tools implement a specific part of the envisioned SDK environment, such as monitor data analysis [12,13]. Adapters for light-weight or specialized environments [8][9][10] can deploy chained virtual machines or containers for testing or emulate the execution of service control functions on large scale data center topologies. A federated testbed uniting different technologies and administrators, is described in [14]. ...
Network virtualization and softwarizing network functions are trends aiming at higher network efficiency, cost reduction and agility. They are driven by the evolution in Software Defined Networking (SDN) and Network Function Virtualization (NFV). This shows that software will play an increasingly important role within telecommunication services, which were previously dominated by hardware appliances. Service providers can benefit from this, as it enables faster introduction of new telecom services, combined with an agile set of possibilities to optimize and fine-tune their operations. However, the provided telecom services can only evolve if the adequate software tools are available. In this article, we explain how the development, deployment and maintenance of such an SDN/NFV-based telecom service puts specific requirements on the platform providing it. A Software Development Kit (SDK) is introduced, allowing service providers to adequately design, test and evaluate services before they are deployed in production and also update them during their lifetime. This continuous cycle between development and operations, a concept known as DevOps, is a well known strategy in software development. To extend its context further to SDN/NFV-based services, the functionalities provided by traditional cloud platforms are not yet sufficient. By giving an overview of the currently available tools and their limitations, the gaps in DevOps for SDN/NFV services are highlighted. The benefit of such an SDK is illustrated by a secure content delivery network service (enhanced with deep packet inspection and elastic routing capabilities). With this use-case, the dynamics between developing and deploying a service are further illustrated.
... The experiment has been designed to evaluate the proposed PRU based VM allocation policy. The simulation experiment has been conducted on a single computer using Cloudsim -3.0.3 on Eclipse SDK [11]. The hardware configuration of the computer is shown as follows: Intel(R) Core(TM) i5-3770 CPU @ 3.40 GHz, the OS is Windows 8, RAM is 8 GB and its system architecture is 64bit. ...
... The hardware configuration of the computer is shown as follows: Intel(R) Core(TM) i5-3770 CPU @ 3.40 GHz, the OS is Windows 8, RAM is 8 GB and its system architecture is 64bit. The cloud scenario that was created for experimentation consists of one data center [11]. The workload traces consist of CPU utilization of thousands of virtual machines from hosts located at different places in different geographical areas of the world which have features like large data volume, various data types, low value density and fast processing speed ...
... It enables computers to understand natural language [15]. Google, with its future Gemini language model, will likely bring support for images, audio, and more [8,16]. ...
... SysML4IoT is an extension of the SysML profile based on well-defined IoT concepts. It provides modeling elements to represent various types of hardware devices (Tag, Sensors, Actuator), software services (Human, Digital Artefact), etc. Calheiros et al. [16] proposed a CloudSim Framework, which allows for simulating the Cloud Computing environment and its services. CloudSim supports modeling and simulating different types of infrastructures, including data centers, servers, network switches, routers, etc. ...
The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.
... Alternatively, a sandboxed deployment with a simpler variant of production applications may be preferred. Synthetic and emulated systems [11,14] can also help evaluate large-scale what-if scenarios. ...
Significance of the research paper:
With the rapid expansion and complexity of cloud computing applications, managing and troubleshooting cloud operations has become increasingly human-intensive. This research aims to enhance the efficiency of cloud operations using AI agents that can automatically identify and resolve errors, reducing human intervention and ensuring better service continuity.
Solution components:
AlOpsLab -1 Framework:
• It represents a modular architecture that includes load and error generators that simulate production environments.
• It supports interaction with agents through an "Agent Cloud Interface" that organizes operations and provides the necessary data.
2- Flexibility and scalability:
• The framework supports the easy integration of new components, and works in different environments such as production and testing.
• It can adapt to different workloads and introduce complex errors inspired by real-world incidents.
3- Full monitoring:
• The framework provides an advanced monitoring layer that includes logs, metrics, and traces to track system performance and troubleshoot errors.
4-Practical Study:
• The prototype of the framework was tested using the DeathStarBench application from SocialNetwork.
• The 4-GPT agent was able to detect and correct errors quickly and efficiently, which proved the effectiveness of the proposed framework.
Results:
• The paper provided important insights into the importance of comprehensive monitoring, the importance of designing interactive interfaces for agents, and the ability of the framework to evaluate and improve performance in realistic cloud environments.
• The proposed framework represents a major step towards achieving self-managed cloud computing.
Conclusion: This research represents a cornerstone for building highly efficient AI agents to manage cloud operations, paving the way for developing more intelligent and autonomous systems.
... To overcome the physical and economical challenges of implementing a real scenario, the study uses a simulator based on WorkflowSimDFVS to evaluate the three methodologies [12]. Testing involves diverse scenarios with varying complexities using a simulator that combines CloudSim [13] and WorkflowSim [14] features for efficient workload allocation across multiple datacenters. The cloud simulator incorporates Pegasus workflow structures, like Montage or CyberShake, using real workflow traces to replicate a cloud system [15]. ...
In recent years, the utilization of Cloud computing services has significantly increased, placing a heightened demand on the workload, computational infrastructure, storage, and communication networks managed by datacenters. This surge has prompted researchers to enhance Cloud performance, focusing on minimizing execution time and computational costs. To address this challenge, efficient scheduling strategies involve the allocation of Cloud workload across different datacenters through the migration of virtual machines. This article examines and compares three methodologies to tackle this issue: one based on an Adaptive Neuro-Fuzzy Inference System, another utilizing a Swarm Fuzzy System, and a third one employing a Genetic Algorithm. The study evaluates their effectiveness for workflow scheduling using a CloudSim-based simulator in terms of makespan and computational costs. Results reveal that the neuro-fuzzy system outperforms the fuzzy and genetic systems regarding makespan in Montage and CyberShake environments. It demonstrates a computational cost advantage, achieving reductions of 7.01 % and 6.33 % for KASIA and 10.74 % and 8.86 % for Pittsburgh in Montage and Cyber-Shake, respectively. Furthermore, it surpasses the KASIA system by 50 % and Pittsburgh by 37.5 % in terms of the number of rule base evaluations.
... The method suggested in this paper is evaluated using the CloudSim simulator in order to determine if it is effective. The CloudSim simulator is open-source, programmable and extendable software, developed by Calheiros et al. in 2011 [60]. Through the use of this flexible simulator, developers are able to model largescale virtualized systems such as CSS in a simple and efficient manner. ...
The rapid growth and increasing popularity of cloud services have made effective resource management and energy consumption in data centers crucial. Virtual Machine (VM) consolidation is a widely adopted strategy to reduce energy consumption and minimize Service Level Agreement (SLA) violations. A key challenge in this process is the placement of VMs, which significantly impacts data center efficiency. Despite substantial progress in VM placement techniques, challenges remain, particularly in accurately identifying and managing underloaded and overloaded physical machines. To address these challenges, this paper proposes a novel stochastic process-based method for VM placement. The proposed approach uses a stochastic process-based prediction technique to estimate the probabilities of overload and underload in physical machines. By strategically placing VMs in machines that are predicted not to be underloaded or overloaded in the near future, our method optimizes resource allocation and reduces the frequency of migrations, energy consumption, and SLA violations. The effectiveness of the proposed method is validated using both the CloudSim simulator and the real-world PlanetLab dataset. Simulation results demonstrate that our approach outperforms existing methods in achieving a balance between energy efficiency and SLA compliance, while also minimizing VM migration overhead.
... The tool, which is primarily developed in Java, is freely available under the LGPL license. A comprehensive discussion regarding cloud computing architectures can be found in [38][39][40]. ...
CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently.
... CloudSim: CloudSim is an event driven simulator implemented in Java. Because of its objectoriented programming feature, CloudSim allows extensions and definition of policies in all the components of the software stack, thereby making it a suitable research tool that can mimic the complexities arising from the environments [50]. ...
The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.
... Implementation of proposed method and analyzing the results lead to better understanding about the efficiency of this method. In order to evaluate the performance of the proposed algorithm, we implement it by the CloudSim toolkit [23]. Each task is submitted according to Poisson distribution after its previous tasks, the length of each task is considered as a random number within [100000,200000], the number of tasks are considered between [100,1000], while the number of resources is between [30,50], the deadline d i of task t i is set according to (10), and the budget bi of task t i is set according to(11) [21]. ...
Recent studies in different fields of science caused emergence of needs for high performance computing systems like Cloud. A critical issue in design and implementation of such systems is resource allocation which is directly affected by internal and external factors like the number of nodes, geographical distance and communication latencies. Many optimizations took place in resource allocation methods in order to achieve better performance by concentrating on computing, network and energy resources. Communication latencies as a limitation of network resources have always been playing an important role in parallel processing (especially in fine-grained programs). In this paper, we are going to have a survey on the resource allocation issue in Cloud and then do an optimization on common resource allocation method based on the latencies of communications. Due to it, we added a table to Resource Agent (entity that allocates resources to the applicants) to hold the history of previous allocations. Then, a probability matrix was constructed for allocation of resources partially based on the history of latencies. Response time was considered as a metric for evaluation of proposed method. Results indicated the better response time, especially by increasing the number of tasks. Besides, the proposed method is inherently capable for detecting the unavailable resources through measuring the communication latencies. It assists other issues in cloud systems like migration, resource replication and fault tolerance.
... The expected completion time matrix is denoted by CT ij which is sum of expected execution time (ET ij ) and ready time (RT j ) of resource R j . The experiment work for the proposed algorithm is done using CloudSim [21], a simulator to simulate and model Cloud Computing system and application environment. CloudSim provide both system and working modelling of Cloud infrastructures such as Cloud data centers, Cloud resources (VMs), cloudlets and resource provisioning and scheduling policies. ...
Resource allocation (RA) is a significant aspect in Cloud Computing which facilitates the Cloud resources to Cloud consumers as a metered service. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, reduce makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Min-Min and Max-Min Task Scheduling Algorithms are available, but these algorithms are not able to produce better makespan and effective resource utilization. This paper proposed a Resource-Aware Min-Min (RAMM) Algorithm based on classic Min-Min Algorithm. The RAMM Algorithm selects shortest execution time task and assign it to the resource which takes shortest completion time. If minimum completion time resource is busy then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of task and better resource utilization. The experiment results show that the RAMM Algorithm produces better makespan and load balance than standard Min-Min, Max-Min and improved Max-Min Algorithms.
... represent server's baseline power and . Where, represent energy consumed when the server reaches its highest utilization [10] [19]. ...
Network virtualization has caught the attention of many researchers in recent years. It facilitates the process of creating several virtual networks over a single physical network. Despite this advantage, however, network virtualization suffers from the problem of mapping virtual links and nodes to physical network in most efficient way. This problem is called virtual network embedding ("VNE"). Many researches have been proposed in an attempt to solve this problem, which have many optimization aspects, such as improving embedding strategies in a way that preserves energy, reducing embedding cost and increasing embedding revenue. Moreover, some researchers have extended their algorithms to be more compatible with the distributed clouds instead of a single infrastructure provider ("ISP"). This paper proposes energy aware particle swarm optimization algorithm for distributed clouds. This algorithm aims to partition each virtual network request ("VNR") to subgraphs, using the Heavy Clique Matching technique ("HCM") to generate a coarsened graph. Each coarsened node in the coarsened graph is assigned to a suitable data center ("DC"). Inside each DC, a modified particle swarm optimization algorithm is initiated to find the near optimal solution for the VNE problem. The proposed algorithm was tested and evaluated against existing algorithms using extensive simulations, which shows that the proposed algorithm outperforms other algorithms.
... The CloudSim toolkit [9] ...
Cloud computing has penetrated the Information Technology industry deep enough to influence major companies to adopt it into their mainstream business. A strong thrust on the use of virtualization technology to realize Infrastructure-as-a-Service (IaaS) has led enterprises to leverage subscription-oriented computing capabilities of public Clouds for hosting their application services. In parallel, research in academia has been investigating transversal aspects such as security, software frameworks, quality of service, and standardization. We believe that the complete realization of the Cloud computing vision will lead to the introduction of a virtual market where Cloud brokers, on behalf of end users, are in charge of selecting and composing the services advertised by different Cloud vendors. In order to make this happen, existing solutions and technologies have to be redesigned and extended from a market-oriented perspective and integrated together, giving rise to what we term Market-Oriented Cloud Computing. In this paper, we will assess the current status of Cloud computing by providing a reference model, discuss the challenges that researchers and IT practitioners are facing and will encounter in the near future, and present the approach for solving them from the perspective of the Cloudbus toolkit, which comprises of a set of technologies geared towards the realization of Market Oriented Cloud Computing vision. We provide experimental results demonstrating market-oriented resource provisioning and brokering within a Cloud and across multiple distributed resources. We also include an application illustrating the hosting of ECG analysis as SaaS on Amazon IaaS (EC2 and S3) services.
... Another efficient toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms is developed in [13]. It is an extensible simulation toolkit that enables modeling and simulation of cloud computing systems and application provisioning environments. ...
Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.
... For implementing functionalities of iFogSim architecture, we leveraged basic event simulation functionalities found in CloudSim [4]. Entities in CloudSim, like data centers, communicate between each other by message passing operations (sending events, to be more precise). ...
Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.
... In the surveyed articles, the most common method used for validating a model or a proposed algorithm is to use an analytical tool (e.g. a solver and/or an optimization engine). Another common approach is to use a simulator, either a generic network simulator such as OMNeT++ 5 , or one designed for regular cloud environments such as CloudSim [94], most often with some custom extensions. There also exists a dedicated 5. https://omnetpp.org/ ...
Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.
... To evaluate and compare the proposed algorithms against baselines and other existing state-of-the-art procedures, the CloudSim toolkit developed by Calheiros et al. [28] is used. Two current scientific workflows: Montage and LIGO generated by the Pegasus Workflow Generator [29] are adopted to analyze the proposed algorithms. ...
Scientific ensemble workflows are commonly executed in Infrastructure-as-a-Service clouds for high-performance computing. The dynamic pricing of spot instances offers a cost-effective way for users to rent cloud resources. However, these instances are subject to out-of-bid failures when their prices exceed the user’s bid, leading to task termination and disruptions in workflow execution. It is a great challenge to reduce costs while ensuring the quality of task completion. This paper addresses the problem of scheduling prioritized ensemble workflows using on-demand and spot instances, with the objective of maximizing the number of high-priority workflows completed while minimizing total cost. We propose a rules-based scheduling heuristic with hybrid provisioning, which includes task scheduling, dynamic provisioning, and spot monitoring processes. The proposed algorithm is evaluated by comparing it to existing algorithms for similar problems over two classic scientific workflow datasets, Montage and LIGO. The score for completing as many high-priority workflows as possible is calculated within the given deadline D. The results reveal that our proposed algorithm achieves an average 30% improvement in the RPD value at different deadline levels and task sizes than other baseline algorithms.
... There are many benefits in this method. [30] Primarily, it eases the pressure of the cloud controller inside the cloud environment. While the services within the cloud could consist of a variety of microservices that are spread out across different physical nodes, all of them run on the cloud controller. ...
Efficient data transfer among CSPs is essential for the performance of cloud computing's basic operations, such as migration and disaster recovery. In this research, a novel approach to enhance data transmission performance using DTNs is presented. Using DTNs wisely, the proposed method enhances the speed and reliability of sending huge volumes of data across several CSPs. Coordinating local and remote copy operations is also a part of the process as well as a mechanism for DTN-to-DTN transfers. Optimized network settings with NFS connections maximized data transfer speed. In order to optimize the network, we carried out a battery of experiments to determine the best setting by varying network buffer size, synchronous and asynchronous modes, among others. With asynchronous modes and the best possible NFS settings, results showed a dramatic improvement in data transmission speed. The method is scalable and reliable when looking to address the challenge of inter-cloud improvements with regard to data transfer within a multi-cloud environment.
... To provide meaningful insights into the performance of the proposed IBoT-FC scheduling algorithm, a series of experiments considering a realistic simulation environment. Therefore, the functionality of the CloudSim [60] toolkit has been extended to mimic the characteristics of the fog-cloud architecture. ...
The Internet of Things (IoT) technology has become a transformative force in both information and industrial sectors, enabling devices to collect and exchange data. The exponential growth of IoT devices has led to increased big data generation on the one hand and processing demands on the other. Efficient and scalable solutions are needed to handle the complexities associated with IoT systems’ deployments. In this context, scheduling IoT applications that have a special sensitivity to latency pose greater challenges. This research paper focuses on scheduling IoT Bag of Task (BoT) applications in a hybrid fog-cloud environment, with a particular emphasis on hard deadline constraints in latency-sensitive systems (such as IoT healthcare and monitoring systems). The proposed scheduling algorithm dynamically allocates BoTs on the fog platform, forwarding them to the cloud layer if the fog layer cannot meet the application’s deadline. The algorithm considers multiple types of cloud instances to meet processing requirements and minimize execution costs, while ensuring compliance with deadlines. The experimental results reveal that the proposed scheduling algorithm prevents any deadline violations. Additionally, the proposed algorithm maximizes the utilization of processing resources within fog-cloud environments, resulting in minimized execution costs.
... This subsection presents three of the most popular cloud computing simulators: CloudSim [54], CloudSim Plus [53] and DISSECT-CF [55]. While these tools are not intended to simulate heterogeneous edge environments, they often serve as the foundation for building edge simulators (see Figure 4). ...
... The IDE used for development is the IntelliJ Idea 2022 version. CloudSim [39] and iFogSim [40] libraries are used to have a simulation environment. Scheduling intervals are considered equal to be compatible with other existing works [4,7,41]. ...
With The advent of the Internet of Things (IoT) and its use cases there is a necessity for improved latency which has led to edgecomputing technologies. IoT applications need a cloud environment and appropriate scheduling based on the underlying requirements of a given workload. Due to the mobility nature of IoT devices and resource constraints and resource heterogeneity, IoT application tasks need more efficient scheduling which is a challenging problem. The existing conventional and deep learning scheduling techniques have limitations such as lack of adaptability, issues with synchronous nature and inability to deal with temporal patterns in the workloads. To address these issues, we proposed a learning-based framework known as the Deep Reinforcement Learning Framework (DRLF). This is designed in such a way that it exploits Deep Reinforcement Learning (DRL) with underlying mechanisms and enhanced deep network architecture based on Recurrent Neural Network (RNN). We also proposed an algorithm named Reinforcement Learning Dynamic Scheduling (RLbDS) which exploits different hyperparameters and DRL-based decision-making for efficient scheduling. Real-time traces of edge-cloud infrastructure are used for empirical study. We implemented our framework by defining new classes for CloudSim and iFogSim simulation frameworks. Our empirical study has revealed that RLbDS out performs many existing scheduling methods.
Cloud-fog computing frameworks represent emerging paradigms designed to enhance existing Internet of Things (IoT) architectures. In these frameworks, task scheduling is crucial for optimizing task allocation and execution within the cloud-fog computing environment. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complex nature and variable aspects of the tasks and resources involved. Metaheuristic algorithms are the ones which can overcome this problem and also offers a variety according to the nature of problem. However, they frequently encounter issues such as getting trapped in local optima, which delays their ability to attain the global optimal solution. So, as to eliminate this issue, we employed a hybrid approach known as the “Hybrid Particle Swarm Optimization Algorithm-Grasshopper Optimization Algorithm” (HPSO-GOA). By leveraging the characteristics of both PSO and the GOA, the proposed algorithm adeptly addresses the issue of becoming trapped in local optima. The objective of this work is to minimize the Total Execution Time (TET), Total Execution Cost (TEC), and Energy Consumption (EC). Based on the evaluation metrics, our Multiobjective optimization algorithm outperforms in terms of TET achieving an overall average reduction of 6.69% for PSO and 20.60% for the GOA and 1.65% for Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO). When compared for TEC and EC it outperforms PSO by 3.29%, 10.84%, GOA by 16.46%, 17.85% and HGA-PSO by 4.40%, 3.27% respectively. To evaluate the effectiveness of the proposed algorithm, we conducted comparative analyses with state-of-the-art algorithms (PSO, GOA, HGA-PSO) across five diverse scientific workflows. These comparative analyses and statistical analysis (Wilcoxon and Friedman) highlighted the effectiveness of the HPSO-GO algorithm in enhancing workflow scheduling performance.
Edge and fog computing are critical advancements in cloud ecosystems, offering enhanced real-time processing, reduced latency, and improved bandwidth efficiency. These technologies are essential for the future of cloud services and the Internet of Things (IoT). However, the decentralized nature of edge and fog computing introduces unique cybersecurity challenges that need addressing. Current literature highlights several unresolved issues, such as the increased attack surface, data privacy concerns, and the resource constraints of edge and fog nodes. Our research proposes a novel security framework that integrates lightweight encryption, decentralized authentication via blockchain, and adaptive machine learning-based intrusion detection. This innovative approach is designed to address the specific security needs of edge and fog environments for the first time. Our simulation results demonstrate significant improvements in threat detection accuracy and system efficiency compared to existing methods.
In this paper, we propose a novel approach to load balancing in cloud computing environments using the Deep Deterministic Policy Gradient (DDPG) algorithm, a model-free, off-policy reinforcement learning method. The growing complexity and dynamic nature of cloud services demand efficient and adaptive load balancing techniques to optimize resource utilization and minimize response time. Traditional load balancing methods often fall short in addressing these requirements due to their static policies and inability to adapt to changing conditions. We address these challenges by implementing a DDPG-based framework that dynamically adjusts its strategies according to the state of the system. The DDPG algorithm, which combines the strengths of Deep Learning and Reinforcement Learning, enables our model to continuously learn and refine its policy based on the reward feedback from the environment. This allows for a more flexible and efficient distribution of computing resources in real time. To evaluate the effectiveness of our approach, we conduct extensive simulations in a simulated cloud computing environment. Our results demonstrate significant improvements in load distribution, reduced latency, and enhanced overall system performance compared to conventional load balancing strategies. Furthermore, our approach exhibits robust adaptability to various operational scenarios, indicating its potential for practical deployment in real-world cloud systems. This study not only advances the application of deep reinforcement learning in cloud computing but also provides a scalable solution to the challenges posed by the ever-increasing demand for cloud services. The implications of our findings are discussed in terms of both technical performance and strategic deployment, paving the way for future research in this promising area.
Cloud computing has revolutionized the way organizations deploy and manage their IT infrastructure. However, the increasing complexity and scale of cloud environments pose significant challenges in resource management and orchestration. This paper explores the application of Artificial Intelligence (AI) techniques to enhance cloud resource management and orchestration. We present a comprehensive review of existing AI-driven approaches, propose a novel framework for intelligent cloud resource management, and evaluate its performance through extensive simulations and real-world case studies. Our results demonstrate that AI-driven techniques can significantly improve resource utilization, reduce costs, and enhance overall system performance in cloud environments.
In this paper, we are proposing a data model to help improve the quality of service and the satisfaction of cloud service consumers. To do this, we proposed 2 sub-models: A 1st sub -model for identifying users consuming cloud services, cloud services and the different consumptions that are made according to the context of the users. A 2nd sub-model for adapting the services consumed, while evaluating the level of satisfaction based on the metrics per service. Also differents formalizations associated with these sub-models are proposed. Implementing the final data model could allow the cloud provider to ensure that the adaptation activities of the different services take into account users and their service consumption context.
This report was prepared in response to the request from Congress stated in Public Law 109-431 (H.R. 5646),"An Act to Study and Promote the Use of Energy Efficient Computer Servers in the United States." This report assesses current trends in energy use and energy costs of data centers and servers in the U.S. (especially Federal government facilities) and outlines existing and emerging opportunities for improved energy efficiency. It also makes recommendations for pursuing these energy-efficiency opportunities broadly across the country through the use of information and incentive-based programs.
Computational Grids normally deal with large computation- ally intensive problems on small data sets. In contrast, Data Grids mostly deal with large computational problems that in turn require evaluating and mining large amounts of data. Replication is regarded as one of the major optimisation techniques for providing fast data access. Within this paper, several replication algorithms are studied. This is achieved using the Grid simulator: OptorSim. OptorSim provides a mod- ular framework within which optimisation strategies can be studied un- der dieren t Grid congurations. The goal is to explore the stability and transient behaviour of selected optimisation techniques.
Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.
CLOUD COMPUTING, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1,000 servers for one hour costs no more than using one server for 1,000.
Aneka is a platform for deploying Clouds developing applications on top of it. It provides a runtime environment and a set of APIs that allow developers to build .NET applications that leverage their computation on either public or private clouds. One of the key features of Aneka is the ability of supporting multiple programming models that are ways of expressing the execution logic of applications by using specific abstractions. This is accomplished by creating a customizable and extensible service oriented runtime environment represented by a collection of software containers connected together. By leveraging on these architecture advanced services including resource reservation, persistence, storage management, security, and performance monitoring have been implemented. On top of this infrastructure different programming models can be plugged to provide support for different scenarios as demonstrated by the engineering, life science, and industry applications. Comment: 30 pages, 10 figures
Since the advent of distributed computer systems an active field of research has been the investigation of scheduling strategies for parallel applications. The common approach is to employ scheduling heuristics that approximate an optimal schedule. Unfortunately, it is often impossible to obtain analytical results to compare the efficacy of these heuristics. One possibility is to conducts large numbers of back-to-back experiments on real platforms. While this is possible on tightly-coupled platforms, it is infeasible on modern distributed platforms (i.e. Grids) as it is labor-intensive and does not enable repeatable results. The solution is to resort to simulations. Simulations not only enables repeatable results but also make it possible to explore wide ranges of platform and application scenarios. In this paper we present the SimGrid framework which enables the simulation of distributed applications in distributed computing environments for the specific purpose of developing and evaluating scheduling algorithms. This paper focuses on SimGrid v2, which greatly improves on the first version of the software with more realistic network models and topologies. SimGrid v2 also enables the simulation of distributed scheduling agents, which has become critical for current scheduling research in large-scale platforms. After describing and validating these features, we present a case study by which we demonstrate the usefulness of SimGrid for conducting scheduling research.
Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number of topology models, it is an open question as to how representative the generated topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness, and interoperability. Representativeness leads to synthetic topologies that accurately reflect many aspects of the actual Internet topology (e.g. hierarchical structure, node degree distribution, etc.). Inclusiveness combines the strengths of as many generation models as possible in a single generation tool. Interoperability provides interfaces to widely-used simulation applications such as ns and SSF and visualization tools like otter. We call such a tool a universal topology generator
Computational Grids normally deal with large computationally intensive problems on small data sets. In contrast, Data Grids mostly deal with large computational problems that in turn require evaluating and mining large amounts of data. Replication is regarded as one of the major optimisation techniques for providing fast data access.
Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number of topology models, it is an open question as to how representative the generated topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness, and interoperability. Representativeness leads to synthetic topologies that accurately reflect many aspects of the actual Internet topology (e.g. hierarchical structure, node degree distribution, etc.). Inclusiveness combines the strengths of as many generation models as possible in a single generation tool. Interoperability provides interfaces to widely-used simulation applications such as ns and SSF and visualization tools like otter. We call such a tool a universal topology generator. Keywords: topology generation, graph models, network topology, growth models, annotated topologies, simulation environments. 1
Aneka is a platform for deploying Clouds developing applications on top of it. It provides a runtime environment and a set of APIs that allow developers to build.NET applications that leverage their computation on either public or private clouds. One of the key features of Aneka is the ability of supporting multiple programming models that are ways of expressing the execution logic of applications by using specific abstractions. This is accomplished by creating a customizable and extensible service oriented runtime environment represented by a collection of software containers connected together. By leveraging on these architecture advanced services including resource reservation, persistence, storage management, security, and performance monitoring have been implemented. On top of this infrastructure different programming models can be plugged to provide support for different scenarios as demonstrated by the engineering, life science, and industry applications.
Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately. there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations. workloads, and system design choices.
Powerful services and applications are being integrated and packaged on the Web in what the industry now calls "cloud computing"
Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.
This paper explores autonomic approaches for optimizing provisioning for heterogeneous workloads on enterprise grids and clouds. Specifically, this paper presents a decentralized, robust online clustering approach that addresses the distributed nature of these environments, and can be used to detect patterns and trends, and use this information to optimize provisioning of virtual (VM) resources. It then presents a model-based approach for estimating application service time using long-term application performance monitoring, to provide feedback about the appropriateness of requested resources as well as the system's ability to meet QoS constraints and SLAs. Specifically for high-performance computing workloads, the use of a quadratic response surface model (QRSM) is justified with respect to traditional models, demonstrating the need for application-specific modeling. The proposed approaches are evaluated using a real computing center workload trace and the results demonstrate both their effectiveness and cost-efficiency.
With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This computing utility, like all other four existing utilities, will provide the basic level of computing service that is considered essential to meet the everyday needs of the general community. To deliver this vision, a number of computing paradigms have been proposed, of which the latest one is known as Cloud computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries, along with our current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload. We also describe a meta-negotiation infrastructure to establish global Cloud exchanges and markets, and illustrate a case study of harnessing ‘Storage Clouds’ for high performance content delivery. Finally, we conclude with the need for convergence of competing IT paradigms to deliver our 21st century vision.
Abstract—Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main,concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-per- use flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications
Computing is being transformed to a model consisting of services that are commoditised and delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted. Several computing paradigms have promised to deliver this utility computing vision and they include Grid computing, P2P computing, and more recently Cloud computing. The latter term denotes the infrastructure as a ldquoCloudrdquo in which businesses and users are able to access applications from anywhere in the world on demand. Hence, Cloud computing can be classed as a new paradigm for the dynamic creation of next-generation Data Centers by assembling services of networked Virtual Machines (VMs). Thus, the computing world is rapidly transforming towards developing software for millions to consume as a service rather than creating software for millions to run on their PCs.
Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.
Large distributed grid systems pose new challenges in job scheduling due to complex workload characteristics and system characteristics. Due to the numerous parameters that must be considered and the complex interactions that can occur between different resource allocation policies, analytical modeling of system behavior appears impractical. Thus, we have developed the GangSim simulator to support studies of scheduling strategies in grid environments, with a particular focus on investigations of the interactions between local and community resource allocation policies. The GangSim implementation is derived in part from the Ganglia distributed monitoring framework, an implementation approach that permits mixing of simulated and real grid components. We present examples of the studies that GangSim permits, showing in particular how we can use GangSim to study the behavior of VO schedulers as a function of scheduling policy, resource usage policies, and workloads. We also present the results of experiments conducted on an operational Grid, Grid3, to evaluate GangSim's accuracy. These latter studies point to the need for more accurate modeling of various aspects of local site behavior.
Clusters, grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large-scale problems in science, engineering, and commerce. In grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide-area networks or the Internet.
simjava is a toolkit for building working models of complex systems. It is based around a discrete event simulation kernel and includes facilities for representing simulation objects as animated icons on screen.simjava simulations may be incorporated as "live diagrams" into web documents. This paper describes the design, component model, applications and future of simjava . 1 Introduction Our motivation for writing simulations in Java was to allow "live diagrams" to be incorporated into web pages. We had been developing C++ based visual simulations of computer architectures and parallel software systems as part of the HASE package (Ibbett, Heywood and Howell 1995), and saw the emergence of Java as an opportunity to make simulation models more widely available and easily accessible. Like many of the other groups who have written Java simulation libraries, we based the system on an existing C++ simulation package. The design aim was for simjava to be a set of "simulation foundation c...
An approach to universal topology generation Proceedings of the Ninth International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
- A Medina
- A Lakhina
- I Matta
- J Byer
- Brite
Medina A, Lakhina A, Matta I, Byer J. BRITE: An approach to universal topology generation. Proceedings of the Ninth International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2001), Cincinnati, OH, U.S.A., 15–18 August 2001; 346–353.
Introducing the Azure services platform. White Paper
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Chappell D. Introducing the Azure services platform. White Paper, October 2008.
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- Google App
Google App Engine. Available at: http://appengine.google.com [18 April 2010].
A CloudSim-based visual modeller for analysing cloud computing environments and applications
- R Wickremasinghe B Calheiros R Buyya
- Cloudanalyst
Introducing the Azure services platform
- Chappelld
SimJava: A discrete event simulation library for java
- Howellf Mcnabr
No ‘power’ struggles coordinated multi-level power management for the data center
- Raghavendrar Ranganathanp Talwarv Wangz Zhux