No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Weighted Service Broker Algorithm in Cloud Environment
... The brokerage problem is viewed in some research studies as a resource provisioning and management problem, which can be summed up as deciding which resources should be set aside for the user and then distributing the load among the resources that the service provider has available [15]. Thus, numerous studies focused on load balancing and efficient resource allocation such as [16][17][18][19], Methodology-wise, many techniques were employed for the brokerage service, such as game theory [20], reinforcement learning [21,22], weighted algorithm [23,24], ontology [25], Analytic Hierarchy Process (AHP) in combination with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS ) [26] and fuzzy logic [27][28][29]. ...
Due to the rapid increase in cloud service providers, users are finding it challenging to select a cloud service that suits their needs and budget. Thus, it is more crucial than ever to have an intermediate entity between the two in the form of cloud brokerage services. This broker is responsible for meeting the user's needs while considering the interests of cloud service providers. To accomplish these goals, the cloud broker should be thoughtfully designed. In this paper, we present a fuzzy logic-based cloud brokerage technique that helps users select the right cloud service instance, considering both their needs and the service characteristics. To demonstrate the feasibility of the suggested technique, we investigate multiple scenarios and closely examine the outcomes. The simulation results prove that our approach outperforms conventional methods in guaranteeing better service quality at a reduced cost for immobile users, and can offer mobile users affordable and consistent quality services when service migration is implemented.
Multi-criteria decision making (MCDM) is a technique used to achieve better outcomes for some complex business-related problems, whereby the selection of the best alternative can be made in as many cases as possible. This paper proposes a model, the multi-criteria decision support method, that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling. The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider. Each consumer seeks a service provider based on various preferences, such as price, service quality, and time to complete the tasks. In the literature, the problem is viewed from different perspectives, such as investigating how to enhance task scheduling and the resource allocation process, improve consumers’ trust, and deal with network problems. This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer. First, the model adopts the best-worst method (BWM) to gather and prioritize tasks based on consumers’ and service providers’ preferences. Then, the model calculates and matches similarities between the sets of tasks from the consumer’s side with the sets of tasks from the provider’s side to select the best service provider for each consumer using the two proposed algorithms. The complexity of the two algorithms is found to be O(n³).
The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients' requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.
In the Cloud computing, trust management has become a key requirement for its security as it has an important role where service-interactions take place in an anonymous environment. Trust assessment is an essential part of trust management technology for making any authorization decisions in the Cloud-based trust authorization system. The critical concern in trust assessment is the optimal assignment of weights to different factors that are involved in the trust assessment of the Cloud computing. The paper proposes a weighted averaging method for the Cloud computing paradigm wherein multiple factors are assigned weights dynamically by WMA-OWA functions. The proposed work overcomes the influence of the inflexibility of subjective weight assignment methods wherein weights are assigned manually or subjectively by experts based on their preferences such as random allocation, expert opinion, and, average weight. The experimental result shows that the proposed method can achieve greater flexibility, adaptability, and dynamic adjustment capability in the Cloud computing.
Cloud providers shares their resources and services through collaboration in order to increase resource utilization, profit and quality of services. The offered services with different access patterns, similar characteristics, varied performance levels and cost models create a heterogeneous service environment. It becomes a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service management to help both cloud providers and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users’ feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon’s Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.
Cloud computing offers diverse services such as servers, storage and applications to the end users as per the requirement with pay per use concept. These services are provided by the cloud service provider through datacenters with high configured servers. However, Datacenters which are providing these services are geographically distributed gets overloaded if multiple requests arrive simultaneously from the same location. The complexity involved in guaranteeing these services to achieve reliable operation under peak loads is a challenging task. Many requests with variable workloads may affects the performance of data centers. Cloud Service provider has to make decisions for allocating resources for these requests by choosing appropriate Datacenter. In this regard, the Service broker act as mediator between the service provider and cloud user for choosing the best datacenter. Certain policies are used by a service broker to direct the user requests to appropriate data center. Therefore, a heuristic based approach called dynamic cost-load aware service brokering policy is proposed to reduce overall processing time, Cost of Virtual Machine and response time by assigning user requests in efficient method.
Cloud is basically a service oriented model as everything in cloud is treated as services in cloud that to on demand. Paper directs in the area of implementing services broker policies for better utilizing the resources in cloud computing environment. It also address in the area of cloud computing and its resources. Cloud computing is an evolutionary technology changing the way for accessing computational resources over internet with extreme powerful, usable and functional model. Although cloud computing is services oriented model but resources in cloud are basic building blocks for constructing different cloud model. This paper focus on the resources in cloud computing with implementing broker polices for higher outcomes. The paper also defines cloud and some of its application.
Cloud computing depends on sharing distributed computing resources to handle different services such as servers, storage and applications. The applications and infrastructures are provided as pay per use services through data center to the end user. The data centers are located at different geographic locations. However, these data centers can get overloaded with the increase number of client applications being serviced at the same time and location; this will degrade the overall QoS of the distributed services. Since different user applications may require different configuration and requirements, measuring the user applications performance of various resources is challenging. The service provider cannot make decisions for the right level of resources. Therefore, we propose a Variable Service Broker Routing Policy-VSBRP, which is a heuristic-based technique that aims to achieve minimum response time through considering the communication channel bandwidth, latency and the size of the job. The proposed service broker policy will also reduce the overloading of the data centers by redirecting the user requests to the next data center that yields better response and processing time. The simulation shows promising results in terms of response and processing time compared to other known broker policies from the literature.
Cloud computing is one of the most promising computing field, which has given the new vision to the computing field. Cloud computing has opened a door as a new model for hosting and delivering services over the Internet. The main aim of cloud computing is to provide the resources as a services to the client. The new concept of Federated Cloud Computing in which multiple datacenters are distributed over different regions. Since the evolution of Cloud Computing: load balancing, energy management, VM migration, brokerage policies, cost modelling and security issues are popular research topics in the field. Deployment of real cloud environment for testing or for commercial use is very costly. Cloud simulators help to model various cloud applications and it is very easy to analyse. In this survey, two cloud simulators: CloudSim and CloudAnalyst, with their overview are presented so it can be easily decided which one is suitable for particular research topic. And also the survey on the service broker policy, its issues and available solutions are presented. Because there is always been the requirement to select appropriate datacenter so that further tasks for processing the request should be carried out with efficiency in least response time. So the issue of selecting appropriate datacenter which is known as service broker policy is kind of important.
Optimal scheduling of workflows in cloud computing environments is an essential element to maximize the utilization of Virtual Machines (VMs). In practice, scheduling of dependent tasks in a workflow requires distributing the tasks to the available VMs on the cloud. This paper introduces a discrete variation of the Distributed Grey Wolf Optimizer (DGWO) for scheduling dependent tasks to VMs. The scheduling process in DGWO is modeled as a minimization problem for two objectives: computation and data transmission costs. DGWO uses the largest order value (LOV) method to convert the continuous candidate solutions produced by DGWO to discrete candidate solutions. DGWO was experimentally tested and compared to well-known optimization-based scheduling algorithms (Particle Swarm Optimization (PSO), Grey Wolf Optimizer). The experimental results suggest that DGWO distributes tasks to VMs faster than the other tested algorithms. Besides, DGWO was compared to PSO and Binary PSO (BPSO) using WorkflowSim and scientific workflows of different sizes. The obtained simulation results suggest that DGWO provides the best makespan compared to the other algorithms.
Cloud computing is an area that is rapidly gaining popularity in both academia and industry. Cloud-Analyst is useful tool to model and analyze cloud computing environment and applications before actual deployment of cloud products. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. Service proximity based routing policy selects closest data center to route the user request. If there are more than one data centers within the same region, it is selected randomly without considering workload, cost, processing time or other parameters. Randomly selected data center is prone to give unsatisfactory results in term of response time, resource utilization, cost or other parameters. In this paper we propose a priority based Round-Robin service broker algorithm which distributes the requests based on the priority of data centers and gives better performance than the conventional Random selection algorithm.
To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogenous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which leads to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. We propose a Resource Intensity Aware Load balancing method (RIAL). For each PM, RIAL dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrate VMs to PMs with minimum VM performance degradation. Our extensive trace-driven simulation results and real-world experimental results show the superior performance of RIAL compared to other load balancing methods.
The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.
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
Proximity-Based Routing Policy for Service Brokering in Cloud Computing
- A S Ahmed
Ahmed, A.S., 2012. Proximity-Based Routing Policy
for Service Brokering in Cloud Computing.
International Journal of Engineering Research and
Applications. 2(2) 453, 12,
DOI={10.5220/0003908000760081}.
Improved round robin algorithm for data center selection in cloud computing
- D Kapgate
Kapgate, D., (2014). Improved round robin
algorithm for data center selection in cloud
computing. International Journal of Engineering
Sciences & Research Technology 3, 686-691.
An Efficient Service Broker Policy for Cloud Computing Environment
- K Kishor
- V Thapar
Kishor, K., & Thapar, V. (2014). An Efficient
Service Broker Policy for Cloud Computing
Environment.
https://api.semanticscholar.org/CorpusID:16760843
A proposed service broker policy for data center selection in cloud environment with implementation
- D Limbani
- B Oza
Limbani, D., Oza, B., (2012a). A proposed service
broker policy for data center selection in cloud
environment with implementation In International
Journal of Computer Technology
Service broker algorithm for cloud-analyst
- R K Mishra
- S N Bhukya
Mishra, R.K., Bhukya, S.N., 2014. Service broker
algorithm for cloud-analyst. International Journal
of Computer Science and Information Technologies
5, 3957-3962.
https://api.semanticscholar.org/CorpusID:15390326
Round robin selection of datacenter simulation technique cloudsim and cloud analsyt architecture and making it efficient by using load balancing technique
- L S Nishad
- S Kumar
- S K Bola
- S Beniwal
- A Pareek
Nishad, L.S., Kumar, S., Bola, S.K., Beniwal, S.,
Pareek, A., (2016). Round robin selection of
datacenter simulation technique cloudsim and cloud
analsyt architecture and making it efficient by using
load balancing technique In 2016 3rd International
Conference on Computing for Sustainable Global
Development (INDIACom), pp. 2901-2905.
Efficient service broker policy for large-scale cloud environments
- M Radi
Radi, M., (2015). Efficient service broker policy
for large-scale cloud environments. arXiv preprint
arXiv:1503.03460.
https://api.semanticscholar.org/CorpusID:1542236
5