TY - JOUR
T1 - Performance and energy-aware bi-objective tasks scheduling for cloud data centers
AU - Materwala, Huned
AU - Ismail, Leila
N1 - Funding Information:
This research was funded by the National Water and Energy Center of the United Arab Emirates University (Grant 31R215).
Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.
AB - Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.
KW - Autonomous agents
KW - Cloud computing
KW - Energy-efficiency
KW - Evolutionary algorithm
KW - Genetic algorithm
KW - Multi-objective optimization
KW - Performance
KW - Quality of service
UR - http://www.scopus.com/inward/record.url?scp=85123761130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123761130&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.12.137
DO - 10.1016/j.procs.2021.12.137
M3 - Conference article
AN - SCOPUS:85123761130
SN - 1877-0509
VL - 197
SP - 238
EP - 246
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 6th Information Systems International Conference, ISICO 2021
Y2 - 7 August 2021 through 8 August 2021
ER -