Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms

被引:2
作者
Li, Peiyu [1 ,2 ]
Wang, Hui [1 ,2 ]
Tian, Guo [1 ]
Fan, Zhihui [1 ,2 ]
机构
[1] Henan Univ Sci & Technol, Network & Informatizat Off, Luoyang 471023, Peoples R China
[2] Henan Engn Lab Cloud Comp Data Ctr Network Key Tec, Luoyang 471023, Peoples R China
基金
中国国家自然科学基金;
关键词
cloud computing; meta-heuristic algorithms; resource utilization; load balancing; quality of service; COLONY ALGORITHM;
D O I
10.3390/electronics13132578
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.
引用
收藏
页数:28
相关论文
共 77 条
[1]   Load balancing in cloud computing - A hierarchical taxonomical classification [J].
Afzal, Shahbaz ;
Kavitha, G. .
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2019, 8 (01)
[2]   Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges [J].
Agrawal, Neha ;
Tapaswi, Shashikala .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2019, 21 (04) :3769-3795
[3]   PSO-based Load Balancing Method in Cloud Computing [J].
Alguliyev, R. M. ;
Imamverdiyev, Y. N. ;
Abdullayeva, F. J. .
AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2019, 53 (01) :45-55
[4]   An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing [J].
Amer, Dina A. ;
Attiya, Gamal ;
Ziedan, Ibrahim .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (02) :1799-1819
[5]   Multiobjective Simulated Annealing: Principles and Algorithm Variants [J].
Amine, Khalil .
ADVANCES IN OPERATIONS RESEARCH, 2019, 2019
[6]  
Bouhank Asma, 2022, Intelligent Computing: Proceedings of the 2021 Computing Conference. Lecture Notes in Networks and Systems, P423, DOI 10.1007/978-3-030-80119-9_25
[7]   Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors [J].
Camacho-Villalon, Christian L. ;
Dorigo, Marco ;
Stuetzle, Thomas .
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, 2023, 30 (06) :2945-2971
[8]   Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments [J].
Devaraj, A. Francis Saviour ;
Elhoseny, Mohamed ;
Dhanasekaran, S. ;
Lydia, E. Laxmi ;
Shankar, K. .
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2020, 142 :36-45
[9]   Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach [J].
Ebrahim, Maad ;
Hafid, Abdelhakim .
MICROPROCESSORS AND MICROSYSTEMS, 2023, 101
[10]   A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing [J].
Gabhane, Jyotsna P. P. ;
Pathak, Sunil ;
Thakare, Nita M. M. .
INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING, 2023, 19 (01) :81-90