Harnessing Task Usage Prediction and Latency Sensitivity for Scheduling Workloads in Wind-Powered Data Centers

Idun Osnes, Anis Yazidi, Hans-Arno Jacobsen, Frank Eliassen and Sabrina Sartori
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Idun Osnes: Department of Technology Systems, University of Oslo, 2027 Kjeller, Norway
Anis Yazidi: Department of Computer Science, Oslo Metropolitan University, 0176 Oslo, Norway
Hans-Arno Jacobsen: Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
Frank Eliassen: Department of Informatics, University of Oslo, 0316 Oslo, Norway
Sabrina Sartori: Department of Technology Systems, University of Oslo, 2027 Kjeller, Norway

Energies, 2022, vol. 15, issue 12, 1-16

Abstract: The growing number of data centers consumes a vast amount of energy for processing. There is a desire to reduce the environmental footprint of the IT industry, and one way to achieve this is to use renewable energy sources. A challenge with using renewable resources is that the energy output is irregular as a consequence of the intermittent nature of this form of energy. In this paper, we propose a simple and yet efficient latency-aware workload scheduler that creates an energy-agile workload, by deferring tasks with low latency sensitivity to periods with excess renewable energy. The scheduler also increases the overall efficiency of the data center, by packing the workload into as few servers as possible, using neural-network-based predictions of resource usage on an individual task basis to avoid unnecessarily provisioning an excess number of servers. The scheduler was tested on a subset of real-world workload traces, and real-world wind-power generation data, simulating a small-scale data center co-located with a wind turbine. Extensive experimental results show that the devised scheduler reduced the number of servers doing work in periods of low wind-power production up to 93% of the time, by postponing tasks with a low latency sensitivity to a later interval.

Keywords: renewable energy sources; latency-aware workload scheduler; data center; wind power; resource prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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