International Journal of Networked and Distributed Computing

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Volume 8, Issue 3, June 2020, Pages 131 - 138

Towards a Task and Resource Aware Task Scheduling in Cloud Computing: An Experimental Comparative Evaluation

Authors
Muhammad Ibrahim1, *, Said Nabi1, Abdullah Baz2, Nasir Naveed1, Hosam Alhakami3
1Department of Computer Science, Virtual University of Pakistan, Rawalpindi, Pakistan, Rawalpindi, 44000, Pakistan
2Department of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
3Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
*Corresponding author. Email: ibrahimmayar@vu.edu.pk
Corresponding Author
Muhammad Ibrahim
Received 3 January 2020, Accepted 26 February 2020, Available Online 22 May 2020.
DOI
10.2991/ijndc.k.200515.003How to use a DOI?
Keywords
Cloud computing; resource allocation; task scheduling; scheduling algorithms; load balancing; performance evaluation; load imbalance
Abstract

Cloud computing has been considered as one of the large-scale platforms that support various type of services including compute, storage, compute, and analytic to the users and organizations with high agility, scalability, and resiliency intact. The users of the Cloud are increasing at an enormous rate which also resulted in issues related to handling and scheduling the users’ requested workload effectively and efficiently on the available Cloud resources. The aim of the Cloud service providers is to maximize resource utilization and in turn increased revenue generation. In the last few years, Cloud Task scheduling has been considered as an important area of research for the researchers. As different scheduling heuristics are associated with different underlying assumptions; thus, performing a precise comparison cannot be guaranteed. This work empirically compares and provides an insight into the performance of some renown state-of-the-art task scheduling heuristics concerning the Makespan, average resource utilization ratio, Throughput. Those approaches include task-aware, resource-aware, and some hybrid approaches. The experiments were then extended by evaluating the performance using average response time for all the compared approaches. The simulation experiments are conducted by utilizing Heterogeneous Computing Scheduling Problems (HCSP) and Google Cloud Jobs (GOCJ) benchmark datasets using CloudSim a renowned simulation tool for Cloud. Based on the findings of the comparative analysis and results discussion, we have highlighted some important aspects of the underlying approaches and for future work we will propose a task-cum-resource aware task scheduling approach.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
8 - 3
Pages
131 - 138
Publication Date
2020/05/22
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
10.2991/ijndc.k.200515.003How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Muhammad Ibrahim
AU  - Said Nabi
AU  - Abdullah Baz
AU  - Nasir Naveed
AU  - Hosam Alhakami
PY  - 2020
DA  - 2020/05/22
TI  - Towards a Task and Resource Aware Task Scheduling in Cloud Computing: An Experimental Comparative Evaluation
JO  - International Journal of Networked and Distributed Computing
SP  - 131
EP  - 138
VL  - 8
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.200515.003
DO  - 10.2991/ijndc.k.200515.003
ID  - Ibrahim2020
ER  -