Smart Heuristics for Power Constraints in Data Centers Powered by Renewable Sources

Résumé In recent years, academics and industry have increased their efforts to find solutions to reduce greenhouse gas (GHG) due to its impact on climate change. Two approaches to reducing these emissions are decreasing energy consumption and/or increasing the use of clean energy. Data centers are one of the most expensive energy actors in Information and Communications Technology (ICT). One way to provide clean energy to Data Centers is by using power from renewable sources, such as solar and wind. However, renewable energy introduces several uncertainties due to its intermittence. Dealing with these uncertainties demands different approaches at different levels of management. This work is part of the Datazero2 Project which introduces a clean-by-design data center architecture using only renewable energy. Due to no connection to the grid, the data center manager must handle power envelope constraints. This article investigates some scheduling and power capping online heuristics in an attempt to identify the best algorithms to handle fluctuating power profiles without hindering job execution. Then, it details experiments comparing the results of the heuristics. The results show that our heuristic provides a well-balanced solution considering power and Quality of Service (QoS).

[1]  Siamak Mohammadi,et al.  Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization , 2022, IEEE Transactions on Cloud Computing.

[2]  G. Blair,et al.  The climate impact of ICT: A review of estimates, trends and regulations , 2021, 2102.02622.

[3]  Michael Inouye,et al.  Green Algorithms: Quantifying the Carbon Footprint of Computation , 2020, Advanced science.

[4]  Soongeol Kwon,et al.  Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations , 2020 .

[5]  Shin'ichiro Takizawa,et al.  Effect of an Incentive Implementation for Specifying Accurate Walltime in Job Scheduling , 2020, HPC Asia.

[6]  Huazhe Zhang,et al.  PoDD: power-capping dependent distributed applications , 2019, SC.

[7]  Jean-Marc Nicod,et al.  DATAZERO: Datacenter With Zero Emission and Robust Management Using Renewable Energy , 2019, IEEE Access.

[8]  Danilo Carastan-Santos,et al.  One Can Only Gain by Replacing EASY Backfilling: A Simple Scheduling Policies Case Study , 2019, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[9]  Eric Gaussier,et al.  Online Tuning of EASY-Backfilling using Queue Reordering Policies , 2018, IEEE Transactions on Parallel and Distributed Systems.

[10]  Georges Da Costa,et al.  Modeling, classifying and generating large-scale Google-like workload , 2018, Sustain. Comput. Informatics Syst..

[11]  Paul Renaud-Goud,et al.  IT Optimization for Datacenters Under Renewable Power Constraint , 2018, Euro-Par.

[12]  Woongki Baek,et al.  RPPC: A Holistic Runtime System for Maximizing Performance Under Power Capping , 2018, 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[13]  Bo Wang,et al.  Dynamic Application-aware Power Capping , 2017, E2SC@SC.

[14]  Masaki Samejima,et al.  Power and Performance Estimation for Fine-Grained Server Power Capping via Controlling Heterogeneous Applications , 2017, ACM Trans. Manag. Inf. Syst..

[15]  Laurent Lefèvre,et al.  Energy Aware Dynamic Provisioning for Heterogeneous Data Centers , 2016, 2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).

[16]  Martin Schulz,et al.  Dynamic power sharing for higher job throughput , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[17]  Pierre-François Dutot,et al.  Batsim: A Realistic Language-Independent Resources and Jobs Management Systems Simulator , 2015, JSSPP.

[18]  Sherief Reda,et al.  vCap: Adaptive power capping for virtualized servers , 2013, International Symposium on Low Power Electronics and Design (ISLPED).

[19]  David A. Lifka,et al.  The ANL/IBM SP Scheduling System , 1995, JSSPP.

[20]  David Thomas,et al.  The Art in Computer Programming , 2001 .