A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing

Dasgupta, Kousik; Mandal, Brototi; Dutta, Paramartha; Mandal, Jyotsna Kumar; Dam, Santanu

doi:10.1016/j.protcy.2013.12.369

Cited by 316 publications

(121 citation statements)

References 8 publications

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“…It helps other tasks in choosing their virtual machine based on load and priorities. Dasgupta et al (2013) proposed a genetic algorithm based load balancing technique for improving the response time. The three major operations involved are selection, genetic operation and replacement.…”

Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

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Abstract:As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

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Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

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“…Dasgupta and B. Mandal in [23] have proposed an idea to balance the load of the cloud infrastructure while minimizing span of a job. To distribute load effectively in system genetic based approaches follows some rules and optimization.…”

Section: Genetic Algorithm Based Load Balancingmentioning

confidence: 99%

Cloud Load Balancing: A Perspective Study

Pandey¹

2017

ijecs

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Cloud computing has boomed its horizon with large pace as commercial infrastructure in the IT industries meeting the vast requirements of computing resources. There are several issues such as load balancing, virtual machine migration, automated service provisioning, algorithm complexity, etc., demanding to be resolved. Each of these issues needs load balancing to be resolved. This aims for distributing the unwanted dynamic workload between the nodes residing in cloud and this desires of every computing resource must be assigned on proficient and reasonable ground.Load balancing has become crucial for efficient performance in distributed environments. Cloud computing is an emerging technology demanding more services and better results. Thus load balancing for the cloud is very interesting and important research area. Many algorithms are proposed to provide efficient techniques for assigning the client's requests to available cloud nodes.This paper studies cloud computing along with research challenges in load balancing. Load balancing has been a major issue for cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by providing the resources to cloud on-demand of users' basis. By implementing appropriate scheduling criteria load balancing may prioritize users. The aim of this study is to peep in various load balancing algorithms to address its challenges in variety of cloud environment. This study provides a perspective view of the latest approaches in load balancing that will certainly help the future researchers in this field

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“…Single Data centre DC1 uses one physical machine having no of processor=4 with time-shared policy for virtual machine allocation. Main configuration parameters are shown in table 1 in section v [15]. In simulation setup for application advanced Configuration parameters includes user grouping factor= 1000, request grouping factor=100, executable instruction length per request=100 Byte and to balance the load across the virtual machine in a single data centre round robin policy is used.…”

Section: Case Study 41 Simulating the Scenario Of "Internet Banking"mentioning

confidence: 99%

“…UserBase. The parameters used in the main configuration are taken into consideration as a data set for Internet banking application of international bank [15]. Main configuration parameters cover the node associated along with geogrphic area of the entire world.…”

Section: Case Study 41 Simulating the Scenario Of "Internet Banking"mentioning

confidence: 99%

A Load Balancing Analysis of Cloud Base Application with different Service Broker Policies

Rawat¹,

Dimri²,

Saroha³

et al. 2016

IJCA

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Resource provisioning and resource optimization are the key issues in cloud computing. To balance the load in across virtual machine load balancing algorithms are classified into two categories i.e. static, dynamic. For homogeneous and stable environment we prefer static load balancing algorithms. For heterogeneous, dynamic environment we prefer dynamic load balancing algorithms. Load balancing may take place in the public, private or hybrid cloud. In this paper, we focus on a load balancing policy i.e. Closest data Center with different no of virtual machines. The evaluation metrics is the response time and data center processing time. Cloud Environment is simulated for the scenario of "Internet banking" of an international bank in simulation toolkit CloudAnalyst. Using these two evaluation metrics we identify that for real deployment of such customers application what should be a threshold value of key parameters which are supported by the Cluster of users across the Globe.

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A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing

Cited by 316 publications

References 8 publications

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Cloud Load Balancing: A Perspective Study

A Load Balancing Analysis of Cloud Base Application with different Service Broker Policies

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