Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)
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Abstract
Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.
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References
Pang, S., Zhang, W., Ma, T., &Gao, Q. (2017). Ant colony optimization algorithm to dynamic energy management in cloud data center. Mathematical Problems in Engineering, 2017.
Abdulredha MN, Baraa AA, Jabir AJ (2020) Heuristic and meta- heuristic optimization models for task scheduling in cloud-fog systems: A review. Al-Magˇallat Al-’ira ̄qiyyat Al-Handasat Al- Kahraba ̄iyyatwa-Al-Ilikttru& ̄niyyat 16(2):103–112
Alangaram S, Balakannan SP (2022) A taxonomy on strategic viewpoint and insight towards multi-cloud environments. In: Computational vision and bio-inspired computing. Springer, Singapore, pp 713–719
AlangaramS,SPBalakannan,S.Kayalvizhi(2023):Effective Resource sharing among Edge and Cloud based on optimal sharing Algorithm Springer conference Icon – AIDTT 2023 ppid:056
Annie Poornima Princess G, Radhamani AS (2021) A hybrid meta- heuristic for optimal load balancing in cloud computing. J Grid Comput 19(2):1–22
Aron R, Chana I, Abraham A (2015) A hyper-heuristic approach for resource provisioning-based scheduling in grid environment. J Supercomput 71(4):1427–1450
Beegom AS, Rajasree MS (2014) A particle swarm optimization based Pareto optimal task scheduling in cloud computing. In: International conference in swarm intelligence, Oct 2014. Springer, Cham, pp 79–86
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future GenerComputSyst 28(5):755–768
Ben Alla H, Ben Alla S, Touhafi A, Ezzati A (2018) A novel task scheduling approach based on dynamic queues and hybrid meta- heuristic algorithms for cloud computing environment. ClustComput 21(4):1797–1820
Bindu GB, Ramani K, Bindu CS (2020) Optimized resourcescheduling using the meta heuristic algorithm in cloud comput-ing. IAENG Int J ComputSci 47(3):360–366
Butt AA, Khan S, Ashfaq T, Javaid S, Sattar NA, Javaid N (2019) A cloud and fog based architecture for energy management of smart city by using meta-heuristic techniques. In: 2019 15th international wireless communications and mobile computing conference (IWCMC), June 2019. IEEE. , pp 1588–1593
Chhabra A, Huang KC, Bacanin N, Rashid TA (2022) Optimizing bag-of-tasks scheduling on cloud data centers using hybrid swarm-intelligence meta-heuristic. J Supercomput.https://doi. org/10.1007/s11227-021-04199-0
Guo L, Zhao S, Shen S, Jiang C (2012) Task scheduling optimization in cloud computing based on heuristic algorithm. J Netw 7(3):547
Hemasian-Etefagh F, Safi-Esfahani F (2019) Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing. J Supercomput 75(10):6386–6450
Izakian H, TorkLadani B, Zamanifar K, Abraham A (2009) A novel particle swarm optimization approach for grid job scheduling. In: International conference on information systems, technology and management, Mar 2009. Springer, Berlin, pp 100–109 Jena UK, Das PK, Kabat MR (2020) Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment.
J King Saud UnivComputInfSci 34(6):2332–2342 Karuppasamy M, Balakannan SP (2019) An improving data delivery method using EEDD algorithm for energy conservation in green cloud network. Soft Comput.https://doi.org/10.1007/s00500-019-04027-x
Karuppasamy M, Balakannan SP, Jansirani M (2020) Energy efficient resource allocation for a sustainable computing environment. Mater Today: Proc. https://doi.org/10.1016/j.matpr.2020.10.963
Kaur A, Kaur B, Singh D (2019) Meta-heuristic based framework for workflow load balancing in cloud environment. Int J Inf Technology 11(1):119–125
Kennedy J, Eberhart R (1995) Particle swarm optimization. In:Proceedings of ICNN’95-international conference on neural networks, Nov 1995. IEEE, vol 4, pp 1942–1948
Krishnasamy K (2013) Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment.JTheorApplInfTechnol 55(1):33–38
Kumar M, Sharma SC, Goel S, Mishra SK, Husain A (2020) Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm. Neural ComputAppl 32(24):18285–18303
Liu Z, Wang X (2012) A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In: Interna- tional conference in swarm intelligence, June 2012. Springer, Berlin, pp 142–147
Mishra SK, Manjula R (2020) A meta-heuristic based multi objective optimization for load distribution in cloud data center under varying workloads. ClustComput 23(4):3079–3093
Natesha BV, Sharma NK, Domanal S, Guddeti RMR (2018). GWOTS: Grey wolf optimization based task scheduling at the green cloud data center. In: 2018 14th international conference on semantics, knowledge and grids (SKG), Sept 2018. IEEE, pp 181–187
Pacini E, Mateos C, Garcı ́a Garino C (2014) Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron J 17(1):3–3