A load balancing algorithm for virtual machines scheduling in cloud computing

Liu, Li; Qiu, Zhe; Dong, Jie

doi:10.1109/icmic.2017.8321690

Cited by 6 publications

(3 citation statements)

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“…Rajput et al [20] proposed Load Balanced Min-Min (ILBMM) algorithm, which uses genetic algorithm to minimize the makespan and increasing the resource utilization. Liu et al [21]'s improved genetic algorithm by improving the selection and crossover operators in the genetic process yields a better optimization accuracy compared to the traditional algorithms such as FIFO, Round Robing scheduling algorithm, and genetic algorithm for load balancing on virtual machines (VMs). Xiaoqing et al [22] proposed a virtual machine (VM) resource provision genetic algorithm based on load balance, called LB-GA.…”

Section: Fig 1 Load Balancing Modelmentioning

confidence: 99%

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Jayswal*¹,

Saxena²

2020

IJITEE

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Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.

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Section: Fig 1 Load Balancing Modelmentioning

confidence: 99%

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Jayswal*¹,

Saxena²

2020

IJITEE

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“…Balancing the load between computing cluster, where one cluster contains high load and other cluster may have no load or little load on the system. The improved DEM for load balancing is quite suitable for computing clusters that are quite far apart, the problem of having computing clusters of various sizes [4]. In Heterogeneous Computing clusters, the Dimension Exchange Method is used for balancing the load between the hypercube architecture.…”

Section: ) Dimension Exchange Algorithm: Some Organizationmentioning

confidence: 99%

Load Balancing in Cluster System using Entropy Value

Yalagi¹,

Mohire²,

Apte³

2016

IJCA

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In a clustered system, the general problems in load balancing are: ill-planned task allocation, poor performance, long response time, and low throughput. The work focuses on a novel algorithm of load balancing, which is based on the entropy value for both wired and wireless connections. To improve the performance of a system the scheduling and migration policy are calculated on entropy value. The factors like the configuration of server and client, benchmark performance, time of communication between client and server systems and the system speed are considered. This novel algorithm is compared with traditional algorithm, like Round robin and the results shows that the time of execution reduces and the system performance is increased.

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“…These parameters decide the proposed scheduling algorithm is good on cloud computing or not. The algorithm like Shortest job first SJF, Genetic task scheduling algorithm and round robin scheduling algorithm are scheduling the resources to furnish the very important parameter [7]. The parameter is 'Makespan Time' is very important is the total length of schedule or total length of time that is when the all task have finished processing.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

Patil¹,

Gade²,

Rewatkar³

2019

JESA

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The task scheduling in multicloud computing environment is complicated task. The cloud computing has increase tremendous popularity both in academia and business because of its on demand service over internet to the various customer. The task scheduling problem is N-P (Non deterministic polynomial) completeness problem. Task scheduling in cloud computing is a best-known problem that has been paid attention. This is again more challenging, specially for multicloud computing environment. This survey paper presents the Shortest Job First algorithm, Round Robin algorithm and Genetic Algorithm for task scheduling in multicloud computing. The round robin algorithm provides the fair allocation and square allocation of resource to the task. It provides the accurate result in finding solutions to large scale optimization problems, by Genetic algorithms such as task scheduling and it is also helpful. A good task and resource scheduling mechanism must satisfy the QoS requirement of the user and at the same time make an efficient utilization of resources. This survey paper presents an algorithm which tries to achieve application high availability, and minimum makespan, minimum response time and completion time.

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A load balancing algorithm for virtual machines scheduling in cloud computing

Cited by 6 publications

References 0 publications

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Load Balancing in Cluster System using Entropy Value

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

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