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Cited by 47Cloud Computing and Resource ManagementDistributed and Parallel Computing SystemsEnergy Efficiency in Computing

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Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

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Associate Professor, Chandigarh Engineering College, Landran (Mohali), Kaur, Amanpreet, Kaur, Bikrampal, Singh, Parminder, Devgan, Mandeep Singh, Toor, Harpreet Kaur·2020·International Journal of Information Technology and Computer Science
DOI 10.5815/ijitcs.2020.03.02
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Abstract

Load balancing is a significant aspect of cloud computing which is essential for identical load sharing among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs) hosted on physical servers.In cloud computing, Deep Learning (DL) techniques can be used to achieve QoS such as improve resource utilization and throughput; while reduce latency, response time and cost, balancing load across machines, thus, increasing the system reliability.DL results in effective and accurate decision making of intelligent resource allocation to the incoming requests, thereby, choosing the most suitable resource to complete them.However, in previous researches on load balancing, there is limited application of DL approaches.In this paper, the significance of DL approaches have been analysed in the area of cloud computing.A Framework for Workflow execution in cloud environment has been proposed and implemented, namely, Deep Learningbased Deadline-constrained, Dynamic VM Provisioning and Load Balancing (DLD-PLB).Optimal schedule for VMs has been generated using Deep Learning based technique.The Genome workflow tasks have been taken as input to the suggested framework.The results for makespan and cost has been computed for the proposed framework and has been compared with our earlier proposed framework for load balancing optimization -Hybrid approach based Deadline-constrained, Dynamic VM Provisioning and Load Balancing (HDD-PLB)" framework for Workflow execution.The earlier proposed approaches for load balancing were based on hybrid Predict-Earliest-Finish Time (PEFT) with ACO for underutilized VM optimization and hybrid PEFT-Bat approach for optimize the utilization of overflow VMs.

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  • Machine learning algorithms used for load balancing in cloud environments include K-Nearest Neighbors [86], deep neural networks [87], multi-layer perceptron [88], and Simulated Annealing [89].

    “There are various machine learning algorithms like K-Nearest Neighbors [86], deep neural networks [87], multi-layer perceptron [88], Simulated Annealing [89] that were used for LB in cloud environments.”

    From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [87]

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Research works printed but not linked: 27

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  16. Kaur A., Kaur B., Singh D. (2018), "Meta-heuristics based Load Balancing Optimization in Cloud Environment on Underflow and Overflow Conditions", Journal of Information Technology Research (JITR) (IGI Global), Vol.11(4) pp. 155-172.
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Metadata

Authors
Associate Professor, C. E. C., Kaur, A., Kaur, B., Singh, P., Devgan, M. S., & Toor, H. K.
ORCID
Venue
International Journal of Information Technology and Computer Science
Publisher
MECS Publisher
Published
2020-06-08
Volume
12
Issue
3
Pages
8-18
ISSN
  • 2074-9007 (ISSN-L)
  • 2074-9015
DOI
10.5815/ijitcs.2020.03.02
OpenAlex
W3039054640
Cited by
47
References
0
Citations by year
2026: 2 · 2025: 11 · 2024: 16 · 2023: 9 · 2022: 3 · 2021: 6
FWCI
4.24
Type
journal article
Language
en
Open access
Locations
International Journal of Information Technology and Computer Science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
load balancing, cloud computing, deep learning, workflow execution, virtual machine provisioning, deadline-constrained scheduling, makespan minimization, resource allocation, resource utilization, quality of service

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