"Firefly algorithm and learning-based geographical task scheduling for " by Ahmed Chiheb Ammari, Wael Labidi et al.
 

Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers

Document Type

Article

Publication Date

6-14-2022

Abstract

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on delay-bounded applications in distributed GDCs (DGDCs) and performs cost and energy-effective scheduling of multiple heterogeneous applications verifying delay bound constraints of different tasks. DGDCs’ operational cost minimization problem is formulated and successfully optimized using an innovative modified Firefly Algorithm (mFA). Real-life data trace-driven experiments are conducted to evaluate the effectiveness of the proposed mFA in solving this problem. High performance task scheduling results are obtained. The operational cost of each GDC is minimized, the utilization of solar and wind renewable energy from the different geographical locations is maximized while delay bound constraints of all tasks are strictly met. Compared to Bat Algorithm, Simulated-annealing Bat Algorithm and basic firefly algorithm, mFA can produce a schedule that outperforms its peers’ drastically in terms of operational cost of DGDCs. Moreover, mFA finds more rapidly both global or local optima than its peers. It succeeds to meet all equality and inequality constraints at all time slots while its peers may sometimes fail to find satisfactory solutions at some particular time slots.

Identifier

85127202239 (Scopus)

Publication Title

Neurocomputing

External Full Text Location

https://doi.org/10.1016/j.neucom.2022.01.052

e-ISSN

18728286

ISSN

09252312

First Page

146

Last Page

162

Volume

490

Grant

61802015

Fund Ref

National Natural Science Foundation of China

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