Research Article
Enhancing Load Balancing in Cloud Computing Through Deadlock Prediction
@INPROCEEDINGS{10.1007/978-3-031-47359-3_19, author={Hieu Le Ngoc and Hung Tran Cong}, title={Enhancing Load Balancing in Cloud Computing Through Deadlock Prediction}, proceedings={Industrial Networks and Intelligent Systems. 9th EAI International Conference, INISCOM 2023, Ho Chi Minh City, Vietnam, August 2-3, 2023, Proceedings}, proceedings_a={INISCOM}, year={2023}, month={10}, keywords={Cloud Computing Load Balancing Deadlock Prediction}, doi={10.1007/978-3-031-47359-3_19} }- Hieu Le Ngoc
Hung Tran Cong
Year: 2023
Enhancing Load Balancing in Cloud Computing Through Deadlock Prediction
INISCOM
Springer
DOI: 10.1007/978-3-031-47359-3_19
Abstract
Cloud computing has become a crucial aspect of Information Technology, offering solutions to many of the challenges faced by internet users. However, the increasing number of users worldwide has led to congestion at certain nodes, resulting in unbalanced loads or hanging systems, commonly known as deadlock. This paper proposes an algorithm using deadlock prediction to enhance the load balancer in Cloud environment. This algorithm leverages Machine Learning and prediction techniques, specifically the Linear Regression Model, to forecast the possibility of deadlock in VMs. By predicting deadlock, the available resources can be allocated to satisfy all requests. The algorithm was deployed in the CloudSim simulation environment, which was integrated with Weka library for the Machine Learning techniques. The results were compared to well-known algorithms such as FCFS, RoundRobin, MaxMin, and MinMin. The evaluation revealed that the proposed algorithm outperformed these popular algorithms, demonstrating its effectiveness in enhancing Cloud computing’s load balancing capabilities.
