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An Approach to Enhance Quality of Services Aware Resource Allocation in Cloud Computing

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International Conference on Information Systems and Intelligent Applications (ICISIA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 550))

Abstract

A new technology called cloud computing has revolutionized the way services are delivered to businesses and consumers. As an online service, it offers a variety of options to registered users. Quality of service (QoS) requirements must be reached in order for the customer to be completely satisfied. As a result of its impact on other issues faced by cloud users and providers alike, QoS-aware resource allocation is the most essential issue in resource allocation. There is no effective solution that meets both the needs of the service provider and the consumer, yet it is still regarded a difficulty by many. This research aims to reduce the amount of time needed to assign cloud resources, improving overall performance. The social spider algorithm (SSA) is presented to map resources with the suitable job in order to fulfill the specified objectives and handle the complexity of the resource allocation issue. In order to simulate spider foraging behavior, SSA created an algorithm. It focuses on the spider, its prey, and the strength of its vibrations. This is how a victim gets out of the spider web: by attempting to release itself from the web, which creates vibrations in the web. At that point, every spider in that web was able to pick up on the vibration. The more fit the sufferer is, the greater the strength of the vibrations. Vibration intensity created on the web determines the victim’s potential. In the cloud, the job is the spider, and the resource is the prey. In terms of resource fitness, task fitness is seen as the ability to make effective use of available resources. Using DEV-C++ to construct the suggested technique, tests have shown that it saves execution time by up to 10% while simultaneously improving service quality. In terms of execution time, the SSA algorithm with first fit exceeds the SSA algorithm with best fit, while the best fit excels in terms of utilization. Furthermore, when the SSA algorithm is compared to the SSCWA method, the SSA algorithm performs better in terms of execution time, usage, and throughput. The SSA results in improved resource allocation, which results in higher QoS parameters and performance. Additional QoS considerations, such as resource dependability, are a conceivable possibility in the future. Additional research may be done to speed up the execution time even more.

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Author information

Authors and Affiliations

  1. College of Business, Alburaimi University, MIS, Al Buraimi, Oman

    Yasir Abdelgadir Mohamed

  2. University of Gezira/Computer Engineering, Medani, Sudan

    Amna Omer Mohamed

Authors
  1. Yasir Abdelgadir Mohamed
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  2. Amna Omer Mohamed
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Corresponding author

Correspondence to Yasir Abdelgadir Mohamed .

Editor information

Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  2. Sunway University, Selangor, Malaysia

    Mohammed A. Al-Sharafi

  3. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Mohamed, Y.A., Mohamed, A.O. (2023). An Approach to Enhance Quality of Services Aware Resource Allocation in Cloud Computing. In: Al-Emran, M., Al-Sharafi, M.A., Shaalan, K. (eds) International Conference on Information Systems and Intelligent Applications. ICISIA 2022. Lecture Notes in Networks and Systems, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-031-16865-9_50

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