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Enhancement in performance of cloud computing task scheduling using optimization strategies

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Abstract

Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing.

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Abbreviations

ACO:

Ant Colony Optimization

BA:

Bat Algorithm

CSSA-DE:

Sparrow Search Algorithm-Differential Evolution

DA:

Dragonfly Algorithm

ELHHO:

Elite Learning Harris hawks optimizer

FPA:

Flower Pollination Algorithm

GA:

Genetic Algorithms

GGWO:

Genetic Gray Wolves Optimization

GWO:

Grey Wolf Optimizer

HEFT:

Heterogeneous Earliest Finish Time

HHO:

Harris Hawks Optimizer

HS:

Harmony Search

LPGWO:

Local Pollination-based Gray Wolf Optimizer

MCT:

Minimum Completion Time Algorithm

PGWO:

Pareto-based multi-objective GWO

PSO:

Particle Swarm Optimization

SFLA:

Shuffled Frog Leaping Algorithm

SSA:

Salp Swarm Algorithm

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Authors and Affiliations

  1. School of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, 144001, India

    Ramandeep Sandhu & Harpreet Kaur

  2. Department of Computer Engineering & Applications, GLA University, Mathura, U.P., 281406, India

    Ashish Srivastava

  3. School of Computing Science & Engineering, Galgotias University, Greater Noida, U.P., 203201, India

    Mohammad Faiz & Vipul Narayan

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  1. Ramandeep Sandhu
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Correspondence to Ashish Srivastava.

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Sandhu, R., Faiz, M., Kaur, H. et al. Enhancement in performance of cloud computing task scheduling using optimization strategies. Cluster Comput (2024). https://doi.org/10.1007/s10586-023-04254-w

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