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Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments

Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments

Sathiyamoorthi V., Keerthika P., Suresh P., Zuopeng (Justin) Zhang, Adiraju Prasanth Rao, Logeswaran K.
Copyright: © 2021 |Volume: 33 |Issue: 5 |Pages: 18
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799867487|DOI: 10.4018/JOEUC.20210901.oa7
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MLA

Sathiyamoorthi V., et al. "Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments." JOEUC vol.33, no.5 2021: pp.135-152. http://doi.org/10.4018/JOEUC.20210901.oa7

APA

Sathiyamoorthi V., Keerthika P., Suresh P., Zuopeng (Justin) Zhang, Rao, A. P., & Logeswaran K. (2021). Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments. Journal of Organizational and End User Computing (JOEUC), 33(5), 135-152. http://doi.org/10.4018/JOEUC.20210901.oa7

Chicago

Sathiyamoorthi V., et al. "Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments," Journal of Organizational and End User Computing (JOEUC) 33, no.5: 135-152. http://doi.org/10.4018/JOEUC.20210901.oa7

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Abstract

Cloud computing is an optimistic technology that leverages the computing resources to offer globally better and more efficient services than the collection of individual use of internet resources. Due to the heterogeneous and high dynamic nature of resources, failure during resource allocation is a key risk in cloud. Such resource failures lead to delay in tasks execution and have adverse impacts in achieving quality of service (QoS). This paper proposes an effective and adaptive fault tolerant scheduling approach in an effort to facilitate error free task scheduling. The proposed method considers the most impactful parameters such as failure rate and current workload of the resources for optimal QoS. The suggested approach is validated using the CloudSim toolkit based on the commonly used metrics including the resource utilization, average execution time, makespan, throughput, and success rate. Empirical results prove that the suggested approach is more efficient than the benchmark techniques in terms of load balancing and fault tolerance.