Abstract
High energy consumption and serious reduction in the number of virtual machine (VM) migrations in cloud data centres have become increasingly urgent challenges. Finding an efficient VM mapping method is vital in dealing with these challenges. Server consolidation is a well-known NP-hard problem. Moreover, efficient resource mapping and VM migration should consider multiple factors synthetically, including quality of service, energy consumption, resource utilisation, and migration overheads, which are multi-objective optimisation problems. This letter aims to address these issues using a novel bio-inspired mapping algorithm. Also, this letter revisits the existing locust-inspired resource scheduling algorithm employed in cloud data centres with a real workload as well as an analogy and model and presents a novel algorithm. Critical analysis of the locust approach has shown that it opens new opportunities for future research, suggestions for which have been offered. Such analysis ensures the hardware reliability of an algorithm and the algorithm’s quality of performance. The results show that the proposed algorithm outperforms state-of-the-art bio-inspired algorithms. We compared our algorithm with heuristic and meta-heuristic algorithms. The experimental results show that compared with these algorithms, our algorithm efficiently reduces performance degradation due to migration (PDM), energy consumption, and the number of migrations along with improving server utilisation.
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Ahmad Z, Jehangiri AI, Ala’anzy MA, Othman M, Latip R, Zaman SK, Umar AI (2021) Scientific workflows management and scheduling in cloud computing: taxonomy, prospects, and challenges. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3070785
Alanzy M, Latip R, Muhammed A (2018) Range wise busy checking 2-way imbalanced algorithm for cloudlet allocation in cloud environment. In: Journal of Physics: Conference Series, vol. 1018, p. 012018. IOP Publishing
Ala’anzy M, Othman M (2019) Load balancing and server consolidation in cloud computing environments: a meta-study. IEEE Access 7:141868–141887
Apostu A, Puican F, Ularu G, Suciu G, Todoran G, et al (2013) Study on advantages and disadvantages of cloud computing–the advantages of telemetry applications in the cloud. Recent Advances in Applied Computer Science and Digital Services 2103
Ariel G, Ayali A (2015) Locust collective motion and its modeling. PLoS Comput Biol 11(12):e1004522
Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420
Bhatt A, Dimri P, Aggarwal A (2020) Self-adaptive brainstorming for jobshop scheduling in multicloud environment. Softw Pract Exp 50(8):1381–1398
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
Dasgupta K, Mandal B, Dutta P, Mandal JK, Dam S (2013) A genetic algorithm (ga) based load balancing strategy for cloud computing. Proc Technol 10:340–347
Farid M, Latip R, Hussin M, Abdul Hamid NAW (2020) A survey on GOS requirements based on particle swarm optimization scheduling techniques for workflow scheduling in cloud computing. Symmetry 12(4):551
Farid M, Latip R, Hussin M, Hamid NAWA (2020) Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment. IEEE Access 8:24309–24322
Feller E, Rilling L, Morin C (2011) Energy-aware ant colony based workload placement in clouds. In: Proceedings of the 2011 IEEE/ACM 12th international conference on grid computing, pp. 26–33. IEEE Computer Society
Garg M, Kaur A, Dhiman G (2021) A novel resource allocation and scheduling based on priority using metaheuristic for cloud computing environment. In: Impacts and challenges of cloud business intelligence, pp. 113–134. IGI Global
Guttal V, Romanczuk P, Simpson SJ, Sword GA, Couzin ID (2012) Cannibalism can drive the evolution of behavioural phase polyphenism in locusts. Ecol Lett 15(10):1158–1166
Kansal NJ, Chana I (2016) Energy-aware virtual machine migration for cloud computing-a firefly optimization approach. J Grid Comput 14(2):327–345
Kirchoff DF, Xavier M, Mastella J, De Rose CA (2019) A preliminary study of machine learning workload prediction techniques for cloud applications. In: Proceedings of the 2019 27th Euromicro international conference on parallel, Distributed and Network-Based Processing (PDP), pp 222–227. IEEE
Kumar KP, Kousalya K (2020) Amelioration of task scheduling in cloud computing using crow search algorithm. Neural Comput Appl 32(10):5901–5907
Kumar R, Kumar A, Sharma A (2016) A bio-inspired approach for power and performance aware resource allocation in clouds. In: MATEC Web of Conferences, vol. 57, p. 02008. EDP Sciences
Kurdi HA, Alismail SM, Hassan MM (2018) Lace: a locust-inspired scheduling algorithm to reduce energy consumption in cloud datacenters. IEEE Access 6:35435–35448
Li L, Dong J, Zuo D, Wu J (2019) Sla-aware and energy-efficient vm consolidation in cloud data centers using robust linear regression prediction model. IEEE Access 7:9490–9500
Liu F, Ma Z, Wang B, Lin W (2019) A virtual machine consolidation algorithm based on ant colony system and extreme learning machine for cloud data center. IEEE Access 8:53–67
Masdari M, Khezri H (2020) Efficient vm migrations using forecasting techniques in cloud computing: a comprehensive review. Cluster Comput 1:30
Meng X, Isci C, Kephart J, Zhang L, Bouillet E, Pendarakis D (2010) Efficient resource provisioning in compute clouds via vm multiplexing. In: Proceedings of the 7th international conference on Autonomic computing, pp. 11–20
Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Operat Syst Rev 40(1):65–74
Rahman AU, Khan FG, Jadoon W (2016) Energy efficiency techniques in cloud computing. Int J Comput Sci Inform Sec 14(6):317
Rehman AU, Ahmad Z, Jehangiri AI, AlaAnzy MA, Othman M, Umar AI, Ahmad J (2020) Dynamic energy efficient resource allocation strategy for load balancing in fog environment. IEEE Access 8:199829–199839
Sharma M, Garg R (2020) Higa: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers. Eng Sci Technol Int J 23(1):211–224
Shehabi A, Smith SJ, Masanet E, Koomey J (2018) Data center growth in the united states: decoupling the demand for services from electricity use. Environ Res Lett 13(12):124030
Simão J, Veiga L (2014) Partial utility-driven scheduling for flexible sla and pricing arbitration in clouds. IEEE Trans Cloud Comput 4(4):467–480
Talwani S, Singla J (2021) Enhanced bee colony approach for reducing the energy consumption during vm migration in cloud computing environment. In: IOP Conference Series: Materials Science and Engineering, vol. 1022, p. 012069. IOP Publishing
Vrbsky SV, Lei M, Smith K, Byrd J (2010) Data replication and power consumption in data grids. In: Proceedings of the 2010 IEEE second international conference on cloud computing technology and science, pp. 288–295. IEEE
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This work is supported in part by the Malaysian Ministry of Education under Research Management Centre, Universiti Putra Malaysia, Putra Grant scheme with High Impact Factor under Grant Number UPM/700-2/1/GPB/2017/9557900.
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Ala’anzy, M.A., Othman, M. Mapping and Consolidation of VMs Using Locust-Inspired Algorithms for Green Cloud Computing. Neural Process Lett 54, 405–421 (2022). https://doi.org/10.1007/s11063-021-10637-0
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DOI: https://doi.org/10.1007/s11063-021-10637-0