Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

作     者：Bezdan, Timea Zivkovic, Miodrag Bacanin, Nebojsa Strumberger, Ivana Tuba, Eva Tuba, Milan 

作者机构：Singidunum Univ Danijelova 32 Belgrade 11000 Serbia 

出 版 物：《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)

年 卷 期：2022年第42卷第1期

页      面：411-423页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR, (III-44006) Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR 

主　　题：Cloud computing task scheduling multi-objective optimization bat algorithm hybridization 

摘      要：Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.