Load Balancing in Cloud Environment using Self-Governing Agent
12 Jun 2019-pp 480-483
TL;DR: The crux of this work makes use of a Self-Governing Agent Based Load Balancing Algorithm (SGA_LB), where an autonomous migration agent provides dynamic load balancing by efficiently balancing the workload.
Abstract: Cloud computing is the revolutionizing technology that initiates a new paradigm shift towards an enabling technology for providing ubiquitous and on-demand access with rapid resource provisioning feature. In the present scenarios, the cloud environment is overloaded with increasing tenant requests. The large data centers of the cloud environment are emerging as the highly responsible complex structures for satisfying multiple tenant requirements and assigning job requests to appropriate servers and thereby uncompromising client resource demands without overloading the server. This constraint is overcome by the tremendous need for an autonomous load balancing algorithm in the cloud environment. The prime objective of load balancing is to reassign the total load of the overall system to individual nodes or sub-nodes to achieve effective resource utilization and thereby improve the overall response time of the job, amidst balancing overloaded nodes. The crux of this work makes use of a Self-Governing Agent Based Load Balancing Algorithm (SGA_LB), where an autonomous migration agent provides dynamic load balancing by efficiently balancing the workload. Highly dynamic and robust load balancing considers the present behavior of the system to balance the load among the nodes. The load characteristics of the nodes is considered based on the CPU load, memory used and network load. The overall performance of the proposed load balancing algorithm is measured considering some factors like reliability, throughput and fault tolerance, which thereby efficiently balances the overall load.
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TL;DR: In this article, the authors used various optimization algorithms such as particle swarm optimization, cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment.
Abstract: Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.
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TL;DR: In this article , the authors comprehensively review several energy-efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing, and identify the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems.
Abstract: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems.
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References
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Journal Article•
28,685 citations
"Load Balancing in Cloud Environment..." refers background in this paper
...Load balancing is an important criterion where the incoming job requests are handled efficiently and ensure even distribution of load across VMs that reside in Datacenters despite having fluctuating needs and resources [6]....
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01 Jan 2015
12,972 citations
"Load Balancing in Cloud Environment..." refers background in this paper
...weight directly implies that resource has high computational power [3]....
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18 Dec 2010
TL;DR: Experimental results prove that this scheduling strategy on load balancing of VM resources based on genetic algorithm is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
Abstract: The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
328 citations
"Load Balancing in Cloud Environment..." refers methods in this paper
...allocated to this low-utilized virtual machine, thereby reducing the waiting time of the tasks in overall [4]....
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...This algorithm outperforms round robin algorithm [4]....
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09 Jul 2010
TL;DR: A two-phase scheduling algorithm under a three-level cloud computing network is advanced that combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.
Abstract: Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the Internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an Internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant issue. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. However, in this study, a two-phase scheduling algorithm under a three-level cloud computing network is advanced. The proposed scheduling algorithm combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.
310 citations
30 May 2010
TL;DR: A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation is proposed in this paper, it improves many aspects of the related Ant Colony algorithms which proposed to realize load balancing in distributed system.
Abstract: Although cloud computing is generally recognized as a technology which will has a significant impact on IT in the future. However, Cloud computing is still in its infancy, many crucial problems need to be solved for the realization of the fine scenery which theoretically depicted by cloud computing. Load balancing is one of these problems; it plays a very important role in the realization of Open Cloud Computing Federation. We proposal a load balancing mechanism based on ant colony and complex network theory in open cloud computing federation in this paper, it improves many aspects of the related Ant Colony algorithms which proposed to realize load balancing in distributed system, Furthermore, this mechanism take the characteristic of Complex Network into consideration. Finally, the performance of this mechanism is qualitatively analyzed, and a prototype is developed to enable the quantitative analysis, simulation results manifest the analysis.
204 citations