Scheduling of Cloud Computing Tasks via Intelligent Optimization Methods
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
Distributed green cloud datacenters (DGCDs) are increasingly deployed around the world. DGCDs integrate many renewable sources to provide clean power and decrease their operating cost. They are spread over multiple locations, where renewable energy availability, bandwidth prices and grid electricity costs have high geographical diversity. This paper focuses on delay-bounded applications in DGCDs and performs cost and energy-effective scheduling of multiple heterogeneous applications subject to delay-bound constraints. The minimization problem of operational cost of DGCDs is formulated and successfully solved by using Firefly, bat, and simulated annealing-bat algorithms. Data-driven experiments are conducted to assess and compare their effectiveness to solve it. The Firefly algorithm is shown to well outperform its peers.
Identifier
85136953794 (Scopus)
ISBN
[9789811923968]
Publication Title
Lecture Notes in Networks and Systems
External Full Text Location
https://doi.org/10.1007/978-981-19-2397-5_21
e-ISSN
23673389
ISSN
23673370
First Page
209
Last Page
221
Volume
465
Grant
EG/SQU-OT/19/04
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Ammari, Ahmed Chiheb; Labidi, Wael; Aldaoud, Manar; Mnif, Faisal; Yuan, Haitao; Zhou, Meng Chu; and Sarrab, Mohammed, "Scheduling of Cloud Computing Tasks via Intelligent Optimization Methods" (2023). Faculty Publications. 2314.
https://digitalcommons.njit.edu/fac_pubs/2314