Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-04-01 Epub Date: 2023-03-03 DOI:10.1089/big.2022.0095
Dipesh Kumar, Nirupama Mandal, Yugal Kumar
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引用次数: 0

Abstract

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

基于云的任务调度高级洗牌蛙跳算法。
近年来,全球在线活动不断增加,云服务器中的数据量也因此呈指数级增长。随着数据量的快速增长,云计算环境中云服务器的负载也随之增加。随着技术的快速发展,各种基于云的系统应运而生,以提升用户体验。但是,全球在线活动的增加也增加了云计算系统的数据负载。为了保持云服务器托管应用程序的效率和性能,任务调度变得非常重要。任务调度过程通过将任务调度到虚拟机(VM),有助于缩短运行时间和降低平均成本。任务调度取决于向虚拟机分配任务,以处理接收到的任务。任务调度应遵循某种算法将任务分配给虚拟机。许多研究人员为云计算环境中的任务调度提出了不同的调度算法。本文提出了一种高级形式的洗牌青蛙优化算法,该算法基于青蛙寻找食物的性质和行为。作者引入了一种新算法,对 memeplex 中青蛙的位置进行洗牌,以获得最佳结果。通过使用这种优化技术,计算出了中央处理单元的成本函数、makespan 和适应度函数。合适度函数是预算成本函数和间隔时间之和。通过有效地将任务调度到虚拟机上,所提出的方法有助于减少正常运行时间和平均成本。最后,将所提出的高级洗牌蛙优化方法的性能与现有的任务调度方法进行了比较,如基于鲸鱼优化的调度器(W-Scheduler)、切片粒子群优化(SPSO-SA)、倒置蚁群优化算法和静态学习粒子群优化(SLPSO-SA)在平均成本和度量间隔方面的性能。实验结果表明,与其他调度方法相比,所提出的高级蛙群优化算法能更有效地将任务调度到虚拟机上,其makespan为6,平均成本为4,适合度为10。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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