Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment

Jena, Uttam Kumar; Das, Pradipta Kumar; Kabat, Manas Ranjan

doi:10.1016/j.jksuci.2020.01.012

Cited by 117 publications

(80 citation statements)

References 17 publications

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“…The algorithm lacks a multiobjective approach using only one factor into account [29]. QMPSO: QMPSO is a new hybrid metaheuristic algorithm combining modified PSO and improved Q-learning algorithms used for load balancing in a cloud environment [30]. CSO: CSO is a metaheuristic algorithm that belongs to a swarm intelligence family and is based on the natural behavior of cats [31].…”

Section: Results and Analysismentioning

confidence: 99%

“…The proposed approach not only focuses on achieving the best classification accuracy among baselines but also efficiently performs scheduling over competitor baselines such as ACOPS [28], CPSO [29], QMPSO [30], CSO [31] and D-ACOELB [65]. All these algorithms are used for achieving load balancing and have performed reasonably well in many approaches.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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“…For instance, initially, use task [2,[4][5][6]. It is rather communicated by [2,4,11,5,6,12,2,13,4], where 11, 12, 13 are the end marker. The components before 11, that is, Tasks one and four are allotted to Resource 1; Tasks five and six which are between the end marker 11 and 12 are allotted to Resource 2; Task 2 somewhere in the range of 12 and 13 is allotted to Resource 3, and Task 4 is given to Resource 4.…”

Section: Permutation Functionmentioning

confidence: 99%

“…In any case, it is difficult for clients to figure out which cloud service providers that they ought to transfer with, and the other way around, to meet their goals under powerful and unpredictable resource requests, supplies, and costs. 6,7 In a complex multi-layer cloud system, a main challenging task is load balancing. Load balancing is a complicated problem in cloud computing.…”

Section: Introductionmentioning

confidence: 99%

An efficient load balancing using seven stone game optimization in cloud computing

Karthikeyan

2021

Softw Pract Exp

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Cloud computing offers massive processing power to cloud client to solve the scientific, financial forecasting, and weather forecasting applications. The process of distributing to the load to the different cloud service providers is a complex problem. Cloud service providers have different types of virtual machines with different computing power types in multi-layered architectures. Various optimization works have been proposed to tackle the load balancing problem in cloud service providers. Improving performance in load balancing is a cumbersome task. Seven stone game optimization (SSGO) is designed based on the south Indian seven stone game workflow. The proposed method's foremost ambition is to reduce makespan time and maximize cloud service providers' utilization. The proposed method was simulated, and results demonstrate that minimizes the makespan time and maximizes the resource utilization than the particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). The experimental results show that the SSGO provides 4% more resource utilization than PSO, 5% more than GA, and 7% more than SA and 10% more than TS.

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“…Buyya et al [17] proposed a scheduling algorithm to reduce the response time of the cloud resource with minimum cost. Several scheduling schemes were proposed to balance the workload based on the amount of available resources [9], [18]- [21]. All these schemes are non-preemptive and do not consider priority based on the deadlines of the submitted jobs.…”

Section: Introductionmentioning

confidence: 99%

LSTPD: Least Slack Time-Based Preemptive Deadline Constraint Scheduler for Hadoop Clusters

et al. 2020

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Big data refers to numerous forms of complex and large datasets which need distinctive computational platforms in order to be analyzed. Hadoop is one of the popular frameworks for analytics of big data. In Hadoop, a big job is split into multiple small tasks and then they are distributed to worker nodes in a parallel way using MapReduce to speed up computational processes. In this aspect, it is important how to improve throughput performance. MapReduce jobs require quick responses from the worker nodes to complete them under their deadlines. The existing scheduling schemes for Hadoop such as FIFO, fair, and capacity schedulers cannot guarantee the quick response requirement satisfying a prior deadline. Thus, Hadoop system needs to improve response time and completion time for the heterogeneous MapReduce jobs. In this paper, we propose an efficient preemptive deadline constraint scheduler based on least slack time and data locality. In order for better allocation of tasks and load balancing, we first analyze the task scheduling behaviors of the Hadoop platform. Based on that, we propose a novel preemptive approach which considers the remaining execution time of the job being executed in deciding preemption. The experimental results show that the proposed scheme significantly reduces the job execution time and queue waiting time, compared to existing schemes.

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Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment

Cited by 117 publications

References 17 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

An efficient load balancing using seven stone game optimization in cloud computing

LSTPD: Least Slack Time-Based Preemptive Deadline Constraint Scheduler for Hadoop Clusters

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