Abstract

Cloud computing has rapidly emerged as a burgeoning research field in recent times. However, despite this growth, a comprehensive examination of this domain reveals persistent issues in the application of cloud-based systems concerning workload distribution. The abundance of resources and virtual machines (VMs) within cloud computing underscores the importance of efficient task allocation as a critical process. Within the infrastructure as a service (IaaS) architecture, load balancing (LB) remains a pivotal but challenging task. The occurrence of overloaded or underloaded hosts/servers during cloud access is undesirable, as it leads to operational delays and system performance degradation. To address LB issues effectively, it is imperative to deploy a proficient access scheduling algorithm capable of distributing tasks across the available resources. A novel approach was introduced by combining the Harris hawk’s optimization and cuckoo search algorithm (HHO-CSA), with a specific focus on critical service level agreement (SLA) parameters, particularly deadlines, to uphold LB in a cloud environment. The primary objective of the hybrid HHO-CSA methodology is to provide task attributes, resource allocation, VMs prioritization, and quality of service (QoS) to clients within cloud computing applications. The outcome analysis reveals that the proposed hybrid HHO-CSA algorithm results in a resource utilization reduction of 52%, with an execution time of 529.84 ms and a makespan of 638.88 ms. These values outperform those of existing SLA-based LB algorithms. Effective task scheduling plays a pivotal role in ensuring the seamless execution of tasks within a cloud system, while LB significantly aligns with the SLAs available to users. Drawing insights from the existing literature, the suggested hybrid HHO-CSA method addresses the research gap by effectively mitigating the challenges.

Keyword

Cloud computing, Hybrid Harris hawks optimization-cuckoo search algorithm, Load balancing, Quality of service, Service level agreement, Task scheduling.

Cite this article

Pani AK, Manohar M, Thomas M, Kumar P

Refference

[1][1]Wu Z, Sun J, Zhang Y, Zhu Y, Li J, Plaza A, et al. Scheduling-guided automatic processing of massive hyperspectral image classification on cloud computing architectures. IEEE Transactions on Cybernetics. 2020; 51(7):3588-601.

[2][2]Chen X, Cheng L, Liu C, Liu Q, Liu J, Mao Y et al. A WOA-based optimization approach for task scheduling in cloud computing systems. IEEE Systems Journal. 2020; 14(3):3117-28.

[3][3]Manikandan N, Gobalakrishnan N, Pradeep K. Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Computer Communications. 2022; 187:35-44.

[4][4]Seyfollahi A, Ghaffari A. Reliable data dissemination for the internet of things using Harris hawks optimization. Peer-to-Peer Networking and Applications. 2020; 13:1886-902.

[5][5]Parida BR, Rath AK, Mohapatra H. Binary self-adaptive salp swarm optimization-based dynamic load balancing in cloud computing. International Journal of Information Technology and Web Engineering. 2022; 17(1):1-25.

[6][6]Wei X. Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Journal of Ambient Intelligence and Humanized Computing. 2020:1-2.

[7][7]Ismayilov G, Topcuoglu HR. Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Generation Computer Systems. 2020; 102:307-22.

[8][8]Iranmanesh A, Naji HR. DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Cluster Computing. 2021; 24:667-81.

[9][9]Velliangiri S, Karthikeyan P, Xavier VA, Baswaraj D. Hybrid electro search with genetic algorithm for task scheduling in cloud computing. Ain Shams Engineering Journal. 2021; 12(1):631-9.

[10][10]Gohil BN, Patel DR. Load balancing in cloud using improved gray wolf optimizer. Concurrency and Computation: Practice and Experience. 2022; 34(11):e6888.

[11][11]Gharehchopogh FS, Abdollahzadeh B. An efficient harris hawk optimization algorithm for solving the travelling salesman problem. Cluster Computing. 2022; 25(3):1981-2005.

[12][12]Kheradmand B, Ghaffari A, Gharehchopogh FS, Masdari M. Cluster-based routing schema using Harris hawks optimization in the vehicular Ad Hoc networks. Wireless Communications and Mobile Computing. 2022; 2022:1-15.

[13][13]Seyfollahi A, Abeshloo H, Ghaffari A. Enhancing mobile crowdsensing in fog-based internet of things utilizing Harris hawks optimization. Journal of Ambient Intelligence and Humanized Computing. 2021:1-6.

[14][14]Xu X, Chen Y, Yuan Y, Huang T, Zhang X, Qi L. Blockchain-based cloudlet management for multimedia workflow in mobile cloud computing. Multimedia Tools and Applications. 2020; 79:9819-44.

[15][15]Imene L, Sihem S, Okba K, Mohamed B. A third generation genetic algorithm NSGAIII for task scheduling in cloud computing. Journal of King Saud University-Computer and Information Sciences. 2022; 34(9):7515-29.

[16][16]Shafiq DA, Jhanjhi NZ, Abdullah A, Alzain MA. A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access. 2021; 9:41731-44.

[17][17]Zhu Z, Tan L, Li Y, Ji C. PHDFS: optimizing I/O performance of HDFS in deep learning cloud computing platform. Journal of Systems Architecture. 2020; 109:101810.

[18][18]Abualigah L, Diabat A. A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing. 2021; 24:205-23.

[19][19]Abualigah L, Alkhrabsheh M. Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The Journal of Supercomputing. 2022; 78(1):740-65.

[20][20]Zhang L, Zhou L, Salah A. Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments. Information Sciences. 2020; 531:31-46.

[21][21]Sanaj MS, Prathap PJ. Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Engineering Science and Technology, an International Journal. 2020; 23(4):891-902.

[22][22]Praveenchandar J, Tamilarasi A. Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. Journal of Ambient Intelligence and Humanized Computing. 2021; 12:4147-59.

[23][23]Sefati S, Mousavinasab M, Zareh FR. Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation. The Journal of Supercomputing. 2022; 78(1):18-42.

[24][24]Talaat FM, Ali HA, Saraya MS, Saleh AI. Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO. Knowledge and Information Systems. 2022; 64(3):773-97.

[25][25]Singh P, Kaur R, Rashid J, Juneja S, Dhiman G, Kim J, et al. A fog-cluster based load-balancing technique. Sustainability. 2022; 14(13):1-14.

[26][26]Nabi S, Ahmad M, Ibrahim M, Hamam H. AdPSO: adaptive PSO-based task scheduling approach for cloud computing. Sensors. 2022; 22(3):1-22.

[27][27]Rana N, Abd LMS, Abdulhamid SI, Misra S. A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing. Engineering Optimization. 2022; 54(12):1999-2016.

[28][28]Gupta P, Bhagat S, Saini DK, Kumar A, Alahmadi M, Sharma PC. Hybrid whale optimization algorithm for resource optimization in cloud E-healthcare applications. Computers, Materials & Continua. 2022; 71(3):5659-76.

[29][29]Latchoumi TP, Parthiban L. Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wireless Personal Communications. 2022; 122(3):2639-56.

[30][30]Annie PPG, Radhamani AS. A hybrid meta-heuristic for optimal load balancing in cloud computing. Journal of Grid Computing. 2021; 19(2):21.

[31][31]Kruekaew B, Kimpan W. Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access. 2022; 10:17803-18.

[32][32]Thapliyal N, Dimri P. Load balancing in cloud computing based on honey bee foraging behavior and load balance min-min scheduling algorithm. International Journal of Electrical and Electronics Research. 2022; 10(1):1-6.

[33][33]Nazir J, Iqbal MW, Alyas T, Hamid M, Saleem M, Malik S, et al. Load balancing framework for cross-region tasks in cloud computing. Computers, Materials & Continua. 2022; 70(1):1479-90.

[34][34]Shekhar CA, Sharvani GS. MTLBP: a novel framework to assess multi-tenant load balance in cloud computing for cost-effective resource allocation. Wireless Personal Communications. 2021; 120:1873-93.

[35][35]Hung LH, Wu CH, Tsai CH, Huang HC. Migration-based load balance of virtual machine servers in cloud computing by load prediction using genetic-based methods. IEEE Access. 2021; 9:49760-73.

[36][36]Saif MA, Niranjan SK, Murshed BA, Ghanem FA, Ahmed AA. CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment. The Journal of Supercomputing. 2023; 79(1):1111-55.

[37][37]Abedi S, Ghobaei-arani M, Khorami E, Mojarad M. Dynamic resource allocation using improved firefly optimization algorithm in cloud environment. Applied Artificial Intelligence. 2022; 36(1):2055394.

[38][38]Adil M, Nabi S, Aleem M, Diaz VG, Lin JC. CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment. Expert Systems. 2023; 40(4):e13150.

[39][39]Zhang AN, Chu SC, Song PC, Wang H, Pan JS. Task scheduling in cloud computing environment using advanced phasmatodea population evolution algorithms. Electronics. 2022; 11(9):1-16.

[40][40]Al-yarimi FA, Althahabi S, Eltayeb MM. Optimal load balancing in cloud environment of virtual machines. Computer Systems Science & Engineering. 2022; 41(3):919-32.

[41][41]Iqbal N, Khan AN, Rizwan A, Qayyum F, Malik S, Ahmad R, et al. Enhanced time-constraint aware tasks scheduling mechanism based on predictive optimization for efficient load balancing in smart manufacturing. Journal of Manufacturing Systems. 2022; 64:19-39.

[42][42]Murad SS, Badeel RO, Salih N, Alsandi A, Faraj R, Ahmed AR, et al. Optimized Min-Min task scheduling algorithm for scientific workflows in a cloud environment. Journal of Theoretical and Applied Information Technology. 2022; 100(2):480-506.

[43][43]Bal PK, Mohapatra SK, Das TK, Srinivasan K, Hu YC. A joint resource allocation, security with efficient task scheduling in cloud computing using hybrid machine learning techniques. Sensors. 2022; 22(3):1-16.

[44][44]Ahmed S, and Omara FA. A modified workflow scheduling algorithm for cloud computing environment. International Journal of Intelligent Engineering and Systems. 2022; 15(5):336-52.

[45][45]Nan Z, Wenjing L, Zhu L, Zhi L, Yumin L, Nahar N. A new task scheduling scheme based on genetic algorithm for edge computing. Computers, Materials & Continua. 2022; 71(1):843-54.