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A Novel Framework for VM Selection and Placement in Cloud Environment

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Cybersecurity and Evolutionary Data Engineering (ICCEDE 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1073))

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Abstract

In spite of various research that has been conducted in the past but there are some challenges that are still into existence related to balancing of workload in cloud applications. There has been a great need for efficient allocation of resources that is handling all the data center, servers & various virtual machines connected with cloud applications. It is the responsibility of cloud facility providers to confirm high facility delivery in an unavoidable situation. All such type of hosts is overloaded or underloaded based on their execution time and throughput. Task scheduling helps in balancing the load of resources and on the other hand task scheduling adheres to the requirement of service level agreement. SLA parameters such as deadlines are concentrated on the Load Balancing algorithm. This paper proposes algorithm which optimizes cloud resources and improves the balancing of load based on migration, SLA and energy efficiency. Proposed load-balancing algorithm discourses all states and focuses on existing research gaps by focusing on the literature gaps. Task scheduling is mainly concentrating on balancing the load and task scheduling mainly adheres to SLA. SLA is one of the documents offered by the service provider to the user. There are various parameters of load balancing such as deadlines which are discussed in the load balancing algorithm. The key focus of proposed process identifies optimize method of resources and improved Load Balancing based on QoS, priority migration of VMs and resource allocations. This proposed algorithm addressed these issues based on the literature review findings.

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Authors and Affiliations

  1. University Institute of Computing, Chandigarh University, Mohali, India

    Krishan Tuli & Manisha Malhotra

Authors
  1. Krishan Tuli
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  2. Manisha Malhotra
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Corresponding author

Correspondence to Krishan Tuli .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Washington University in St. Louis, Pullman, WA, USA

    Raj Jain

  2. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Carlos M. Travieso

  3. GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

    Sanjeev Kumar

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Tuli, K., Malhotra, M. (2023). A Novel Framework for VM Selection and Placement in Cloud Environment. In: Jain, R., Travieso, C.M., Kumar, S. (eds) Cybersecurity and Evolutionary Data Engineering. ICCEDE 2022. Lecture Notes in Electrical Engineering, vol 1073. Springer, Singapore. https://doi.org/10.1007/978-981-99-5080-5_16

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  • DOI: https://doi.org/10.1007/978-981-99-5080-5_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5079-9

  • Online ISBN: 978-981-99-5080-5

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