A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment

The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of resources need to be addressed towards managing the cloud resources efficiently. The provisioning and scheduling of resources with respect...

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Bibliographic Details
Published in:Wireless personal communications 2020-08, Vol.113 (4), p.2027-2065
Main Authors: Abrol, Preeti, Gupta, Savita, Singh, Sukhwinder
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Communications Engineering
Computer Communication Networks
Engineering
Heterogeneity
Mapping
Networks
Optimization
Placement
Provisioning
Quality of service
Resource management
Resource scheduling
Signal,Image and Speech Processing
Task scheduling
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Summary:The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of resources need to be addressed towards managing the cloud resources efficiently. The provisioning and scheduling of resources with respect to the tasks depends primarily on the quality of service (QoS) requirements of cloud applications and is a challenging task. For the complete satisfaction of the client, execution of tasks should be as per the QoS parameters; hence a QoS aware cloud framework is required for the purpose mapping of resources efficiently. To handle the complex issue of the resource placement problem, a cloud architectural framework named cloud orchestrated framework for efficient resource placement presents efficient and effective management and placement of resources in the cloud. In this paper, a novel QoS aware resource placement algorithm is proposed based on the social spider mating strategy that manages and places tasks for the computation of resources automatically by optimizing the QoS metrics as a significant feature. The performance of proposed algorithm is evaluated in the cloud and results show that the proposed framework performs better in terms of execution cost, execution time, throughput, and availability, reliability, waiting time, turnaround time, utilization and convergence of cloud resources and utilizes these resources optimally.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-020-07306-1