Enhanced Task Scheduling Algorithm Using Harris Hawks Optimization Algorithm for Cloud Computing

Amongst the most transformational technologies nowadays, cloud computing can provide resources such as CPU, memory, and storage over secure internet connections. Due to its flexibility and resource availability with guaranteed QoS, cloud computing allows comprehensive business and research adoptions...

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Vydané v:International journal of advanced computer science & applications Ročník 16; číslo 1
Hlavný autor: WANG, Fang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: West Yorkshire Science and Information (SAI) Organization Limited 2025
Predmet:
Algorithms
Availability
Business metrics
Cloud computing
Communication
Computer memory
Computer science
Efficiency
Energy consumption
Internet of Things
Optimization
Optimization algorithms
Quality of service
Random walk
Resource allocation
Resource management
Resource scheduling
Response time
Response time (computers)
Scheduling
Simulation
Software services
Task scheduling
ISSN:2158-107X, 2156-5570
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Shrnutí:Amongst the most transformational technologies nowadays, cloud computing can provide resources such as CPU, memory, and storage over secure internet connections. Due to its flexibility and resource availability with guaranteed QoS, cloud computing allows comprehensive business and research adoptions. Despite the rapid development, resource management remains one of the significant challenges, especially handling task scheduling efficiently in this environment. Task scheduling strategically assigns tasks to available resources so that Quality of Service (QoS) metrics are effectively related to response time and throughput. This paper proposes an Enhanced Harris Hawks Optimization (EHHO) algorithm for scheduling cloud tasks to mitigate the common limitations found in existing algorithms. EHHO integrates a dynamic random walk strategy, enhancing exploration capabilities to avoid premature convergence and significantly improving scalability and resource allocation efficiency. Simulation outcomes reveal that EHHO minimizes makespan by up to 75%, memory usage by up to 60%, execution time by up to 39%, and cost by up to 66% compared to state-of-the-art algorithms. These benefits demonstrate that EHHO can optimize resource allocation while being highly scalable and reliable. Consistent performance over various stacks such as Kafka, Spark, Flink, and Storm further evidences the superiority of EHHO in handling complex scheduling challenges in dynamic cloud computing environments.
Bibliografia:ObjectType-Article-1
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160189