An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

Kumar, Ashok; Lal, Madan; Kaur, Sumandeep

doi:10.1002/cpe.7699

Cited by 1 publication

(1 citation statement)

References 47 publications

(69 reference statements)

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“…Apart from the limitations identified in the recent work, 10 there are number of flaws also observed in the related existing literature such as, 14,16,17,20,21 which ignores the user's aspect. A resource provisioning technique improves the QoS by allocating the services based on the user's requirement.…”

Section: The Motivationmentioning

confidence: 99%

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

Singh,

Vidyarthi

2024

Concurrency and Computation

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SummaryFog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named “Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA),” which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment.

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