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Cloud Computing Based Resource Allocation by Random Load Balancing Technique
Abstract
In this paper, present Cloud-fog computing platform which provide efficiently their services via the internet by using remote servers to the residential areas. The increasing number of Internet of Things (IoT) devices and applications cause large data traffic on the cloud system which increase the response time and cost. To overcome this situation, fog computing concept is introduced in this paper. It also reduce the load of cloud and the latency rate of response time to the energy consumption side. Fogs have less storage capacity as compare to cloud, however have all the services available as in cloud side. The Smart Grid (SG) is a modern electric grid like smart meters and smart appliances which efficiently manage the resources allocation. In this work, consider a large geographical residential area divided into six regions and each region has a fog server to manage the energy requests coming from the end users. Each fog has a number of Virtual Machines (VMs) to efficiently manage the different user requests in minimum time and cost. The Micro Grids (MG's) are the small scale power grid which manage the energy consumption by reducing the time and cost of end users and are connected to the fog edges. Different load balancing and optimized techniques are used in cloud computing for the efficient resources allocation to the smart residential areas. In this paper an algorithm Random load balancing is used for reliable and efficient task scheduling to overcome the latency rate and cost of user in cloud computing environment.
... 2 With the virtualization technology, users can execute server, memory, networks, and storage resources in a much appropriate way. 3,4 Due to the innovative expansion of cloud computing, there are several organizations and persons who are permissible to subcontract the substantial data to the cloud as an alternative of constructing and preserving limited data centers. ...
... The method failed to utilize energy consumption and networking resources in the upper-level controller. Bano et al. 4 devised a Cloud-fog computing platform that offered the services through the internet using remote servers from residential areas. Here, fog computing was utilized for reducing the load and latency rates. ...
Cloud computing provides a quick, simple, and gainful means for configuring and assigning the resources for a web-based appliance, like medical records systems, smart grid applications, and security management infrastructures. The optimization of resource allocation in the cloud is a major task for meeting customer demands and maximizing profit. This paper devises an optimization method for allocating resources in cloud infrastructures. The main contribution is to provide a framework for allocating the available resource to the deserving tasks. Here, the dynamic resource allocation is performed to allocate the resources dynamically as requested by users without affecting the system performance. Here, the ant lion-based auto-regressive optimization (ALAO) strategy is employed to allocate the cloud resources. ALAO is designed by integrating Ant Lion Optimizer (ALO) in auto-regression model for further enhancing the allocation of resources, and a new fitness function is adapted, which considers certain parameters, such as cost, speed, and load. The results proved that the proposed ALAO algorithm attained enhanced performance with a maximal profit of 0.153, a minimum load of 0.028, and a minimal task assignment cost of 0.094.
... • Energy consumption: Electric power consumed by the fog nodes can be considered as energy consumption in fog computing. There are various devices in the fog environment, i.e. servers, gateways, routers which consume energy while performing operations [15]. Load balancing mainly is done to reduce the overall energy consumption in the fog nodes. ...
Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.
With the development of science and technology, cloud computing technology has changed people's office methods. People begin to use cloud services to realize remote collaborative office, which can save costs and improve work efficiency. However, users’ demand for cloud office is increasing, and their requirements for cloud office services are becoming higher. Aiming at the problem of uneven resource allocation of cloud office services, this paper proposes a cloud resource allocation algorithm CWPSO based on weight correction. This algorithm improves the problem of fixed weight of PSO algorithm in the process of particle update, so that the algorithm can search the target faster. Simulation results show that the performance of the resource allocation strategy obtained by the proposed algorithm is improved by 6.08% in terms of processing time.KeywordsCloud officePSOResource allocation
Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.
Smart cities are becoming a reality. Various aspects of modern cities are being automated and integrated with information and communication technologies (ICT) to achieve higher functionality, optimized resources utilization and management and improved quality of life for the residents. Smart cities rely heavily on utilizing various software, hardware and communication technologies to improve the operations in areas ike healthcare, transportation, energy, education, logistics and many others, while reducing costs and resources consumption. One of the promising technologies to support such efforts is the Cloud of Things (CoT). CoT provides a platform for linking the cyber parts of a smart city that are executed on the cloud with the physical parts of the smart city including residents, vehicles, power grids, buildings, water networks, hospitals and other resources. Another useful technology is Fog Computing, which extends the traditional Cloud Computing paradigm to the edge of the network to enable localized and real time support for operating enhanced smart city services. However, proper integration and efficient utilization of CoT and Fog Computing is not an easy task. The paper discusses how the service-oriented middleware (SOM) approach can help resolve some of the challenges of developing and operating smart city services using CoT and Fog Computing. We propose a SOM called SmartCityWare for effective integration and utilization of CoT and Fog Computing. SmartCityWare abstracts services and components involved in smart city applications as services accessible through the service-oriented model. This enhances integration and allows for flexible inclusion and utilization of the various services needed in a smart city application. In addition, we discuss the implementation and experimental issues of SmartCityWare and demonstrate its use through examples of smart city applications.
Cloud Computing is a new paradigm where data and services of Information Technology are provided via the Internet by using remote servers. It represents a new way of delivering computing resources allowing access to the network on demand. Cloud computing consists of several services, each of which can hold several tasks. As the problem of scheduling tasks is an NP-complete problem, the task management can be an important element in the technology of cloud computing. To optimize the performance of virtual machines hosted in cloud computing, several algorithms of scheduling tasks have been proposed. In this paper, we present an approach allowing to solve the problem optimally and to take into account the QoS constraints based on the different user requests. This technique, based on the Branch and Bound algorithm, allows to assign tasks to different virtual machines while ensuring load balance and a better distribution of resources. The experimental results show that our approach gives very promising results for an effective tasks planning.
The applications of Cloud computing has penetrated all services recently. Enormous data processing in cloud is done by distributing the data among the virtual machines. The virtual machines load capacities are dynamically changing based on the request sent by the client. Genetic Algorithm based Load Balancing Technique (GALBT) has been proposed to equally distribute the load among the virtual machines and for rapid processing. Virtual machine scheduler (Vm scheduler) has been designed to estimate the time and resource requirements of the task processing. Based on estimation, the task is forwarded to virtual machines for processing. This project is developed using cloud simulator and results have been compared with round robin and throttled algorithms to show our strategy performs better for application processing.
Objective: To minimize the Peak to Average load Ratio (PAR) per day of end users so that the smart grids(SG) efficiency is increased. Set of appliances differentiated as elastic and fixed are considered for optimal scheduling at the user end. Methods: Demand side management (DSM) in smart grid is one which permits customers to reach determinations affecting their energy consumption, and reduces the peak hour demand of the energy providers and reshapes the load profile. Genetic algorithm (GA) is a powerful technique to obtain near optimal solution. Hence GA is used for this load rescheduling problem for a sample test system to minimize the cost of end user. Findings: Economical and environmental advantages can be obtained by time based pricing model compared to currently existing scenario. Especially, the electricity expenditures of the end user can be reduced by responding to pricing which changes with different hours of a day in SG. Improvement: In this work rescheduling of the load curve of a consumer from the existing load curve is performed. The former is done based on time based pricing method that would reduce the PAR of end-user. This scheduling is also based on categorizing the devices used by the user as shiftable and non-shiftable loads.
Cloud services are widely used in manufacturing, logistics, digital applications, and document processing. Cloud services must be able to handle tens of thousands of concurrent requests and to enable servers to seamlessly provide the amount of load balancing capacity required to respond to incoming application traffic in addition to allowing users to obtain information quickly and accurately. In the past, researchers have proposed the use of static load balancing or server response times to evaluate load balancing capacity, a lack of which may cause a server to load unevenly. In this study, a dynamic annexed balance method is used to solve this problem. Cloud load balancing (CLB) takes into consideration both server processing power and computer loading, thus making it less likely that a server will be unable to handle excessive computational requirements. Finally, two algorithms in CLB are also addressed with experiments to prove the proposed approach is innovative.
By introducing microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g., in residential microgrids and homes. Energy management may also help us to reach zero net energy (ZNE) for the residential domain. Improvement in technology, cost, and feature size has enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, require powerful computing resources, which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog computing is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management (HEM) and microgrid-level energy management have been implemented and experimented.
Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization.
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