A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing

Rekha, K. Smruti; Mohanty, Subhadarshini

doi:10.5120/11586-6922

Cited by 30 publications

(8 citation statements)

References 13 publications

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“…Authors in [25] discussed a performance comparison for different load balancing algorithms of virtual machine and policies in cloud computing. In this study four well known load balancing algorithms have been considered.…”

Section: Related Workmentioning

confidence: 99%

“…One of the simplest and well known scheduling and load balancing algorithms which utilize the principle of time slices is round robin algorithm [25]. Default load balancing algorithms on CloudAnalyst is round robin that allocates all incoming requests to the available virtual machines in round robin fashion without considering the current load on each virtual machine.…”

Section: Vm Load Balancing Algorithmsmentioning

confidence: 99%

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Performance evaluation and analysis of load balancing algorithms in cloud computing environments

Mesbahi

Hashemi

Rahmani

2016

2016 Second International Conference on Web Research (ICWR)

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Distributing the system workload and balancing all incoming requests among all processing nodes in cloud computing environments is one of the important challenges in today cloud computing world. Many load balancing algorithms and approaches have been proposed for distributed and cloud computing systems. In addition the broker policy for distributing the workload among different datacenters in a cloud environment is one of the important factors for improving the system performance. In this paper we present an analytical comparison for the combinations of VM load balancing algorithms and different broker policies. We evaluate these approaches by simulating on CloudAnalyst simulator and the final results are presented based on different parameters. The results of this research specify the best possible combinations.

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Section: Related Workmentioning

confidence: 99%

Section: Vm Load Balancing Algorithmsmentioning

confidence: 99%

Performance evaluation and analysis of load balancing algorithms in cloud computing environments

Mesbahi

Hashemi

Rahmani

2016

2016 Second International Conference on Web Research (ICWR)

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“…The first is the delivery of data by the way a typical cloud provider operates. This explains why three primary SaaS, PaaS and IaaS forms are widely used [5,6]. The other is the scale, relation, management and complexity and visibility of the cloud model.…”

Section: Introductionmentioning

confidence: 99%

The mechanism for Predictive Load Control in the Implementation Framework through Genetic Intelligence

Pushpalatha¹,

Nagaprasad²

2020

IJMLNCE

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loud Storage is a pay-per-use range of resources. The consumer wants to ensure that all requirements met in a limited time for optimal performance in cloud applications that are every day. Load balancing is also crucial, and one of the essential cloud computing issues. It is also called the NP-full load balancing problem since load balancing is harder with increasing demand. This paper provides a genetic algorithm (GA) framework for cloud load. Depending on population initialization duration, the urgent need for the proposal considered. The idea behind the emphasis is to think about the present world. Real-World Scenario structures have other targets that our algorithms can combine. Cloud Analyst models the suggested method. A load-balancing algorithm based on the forecasts of the end -to -end Cicada method given in this paper. The result indicates the possibility of offering a quantitative workload balancing approach that can help manage workloads through the usage of computer resources. The next generation of cloud computing would make the network scalable and use available resources effectively. This article introduces a new approach to genetic algorithm (GA) power loads. When trying to reduce the complexity of a particular task, the algorithm handles the cloud computing fee. A software analyst model evaluated the proposed method of load balancing. Results from simulations for a standard sample program show that the suggested algorithms outperform current methods like FCFS, Round Robbing (RR), and local search algorithms Stochastic Hill Climbing (SHC).

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“…Mohapatra et al [7] analyzed the four widely used task scheduling approaches -Round robin, Throttled, Equally spread current execution and First Come First Serve (FCFS) task for cloud computing environment -and found Round Robin (RR) approach is best among these approaches. M. Randle et al in [8] discussed the three distributed task scheduling approaches Honey bee based, Biased random walk based and Clustering based task scheduling approach for cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Critical Analysis of Different Min-Min Task Scheduling Approach for Cloud Environment

Nath¹

2017

IJARCSSE

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Cloud computing is a revolution in information technology industry because of its performance, accessibility, low cost and many other services compared to traditional online computing. Users are offered different level of services based on the quality levels mentioned in service level agreement. So cost of service also depends upon the type of resources provided to user so that there should be no violation of service level agreement. To provide the services as per service level agreement, there is need of efficient load balancing approach. The basic Min-Min algorithm was very easy and gives better plan and keeps the completion time of task very small relative to other algorithms found in the literature. But the major limitation of Min-Min algorithm is that it causes unbalancing of system. In the present work, authors have proposed a deadline and priority aware Min-Min load balancing algorithm which not only consider the deadlines but also consider the tasks priority. It has been analyzed and tested critically using MATLAB simulator on the various relevant parameters. Through simulation results, it has been found that deadline and priority aware Min-Min approach outperforms the basic Min-Min approach on all the relevant parameters.Keywords: Task Scheduling, Min-Min, Priority, Deadline, Load Balancing, Cloud Computing, Matlab I. INTRODUCTIONCloud computing is a new way of providing computing resources and services in real time environment. Basis of this technology is virtualization, elasticity and scalability. Cloud model provides on demand services to the user. User demands for different services at different time from different regions. Cloud is subject to user requirements, load balance, and other constraints that have direct effect on user consumption of resources controlled by cloud provider. So it is very difficult to conclude how many resources will be sufficient to provide the services as per service level agreement [1,2]. It becomes increasingly difficult to predict the demand requirements and load coming to resources. Load scheduling algorithm is a way to decide that which tasks are mapped to which resource. It is a way through which it is decided that which tasks will be mapped with which resource [3]. Load balancing is a hard problem as it involves the multiple tasks and resource combinations and among that best combination satisfying different constraints is selected. The prime objective of load balancing approach in cloud is to minimize the task completion time, task waiting time, makespan, optimize the utilization of resources and provide the quality of services as per service level agreement. It is also desired that load balancing approach will handles the higher priority tasks more efficiently relative to lower priority task. Unfortunately different techniques found in literature fail to satisfy all these objectives collectively [4,5]. In this paper, authors improvise the basic Min-Min algorithm by using priority-aware and deadlines for critical tasks. Proposed approach has been simulate...

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A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing

Cited by 30 publications

References 13 publications

Performance evaluation and analysis of load balancing algorithms in cloud computing environments

Performance evaluation and analysis of load balancing algorithms in cloud computing environments

The mechanism for Predictive Load Control in the Implementation Framework through Genetic Intelligence

Critical Analysis of Different Min-Min Task Scheduling Approach for Cloud Environment

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