Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocat...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1375 - 1380
Main Authors Rajagopal, R, Arunarani, AR, Arivarasi, A, Ingle, Anup, T, Ravichandran, Prakash, R. Vijaya
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Cloud computing
Costs
Honey Badger Algorithm
Metaheuristics
Multi-objective optimization
Processor scheduling
System performance
Task Scheduling
Throughput
Whale optimization algorithms
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Summary:Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources.
DOI:10.1109/ICOSEC58147.2023.10275908