Firefly Algorithm and Learning-based Geographical Task Scheduling for Operational Cost Minimization in Distributed Green Data Centers,

Ahmed Chiheb Ammari, Wael Labidi, Faical Mnif, Meng Chu Zhou, Haitao Yuan, Mohamed K. Sarrab

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)146
Number of pages162
JournalNeurocomputing
Volume490
Publication statusPublished - 2022

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