Thermal time shifting: Leveraging phase change materials to reduce cooling costs in warehouse-scale computers

Datacenters, or warehouse scale computers, are rapidly increasing in size and power consumption. However, this growth comes at the cost of an increasing thermal load that must be removed to prevent overheating and server failure. In this paper, we propose to use phase changing materials (PCM) to shape the thermal load of a datacenter, absorbing and releasing heat when it is advantageous to do so. We present and validate a methodology to study the impact of PCM on a datacenter, and evaluate two important opportunities for cost savings. We find that in a datacenter with full cooling system subscription, PCM can reduce the necessary cooling system size by up to 12% without impacting peak throughput, or increase the number of servers by up to 14.6% without increasing the cooling load. In a thermally constrained setting, PCM can increase peak throughput up to 69% while delaying the onset of thermal limits by over 3 hours.

[1]  D. Hale,et al.  Phase-change materials handbook , 1971 .

[2]  Cullen E. Bash,et al.  Thermal considerations in cooling large scale high compute density data centers , 2002, ITherm 2002. Eighth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Cat. No.02CH37258).

[3]  Vincent W. Freeh,et al.  Boosting Data Center Performance Through Non-Uniform Power Allocation , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[4]  Kurt Roth,et al.  Cool Thermal Energy Storage , 2006 .

[5]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[6]  H. Ibrahima,et al.  Energy storage systems — Characteristics and comparisons , 2008 .

[7]  Adrian Ilinca,et al.  Energy storage systems—Characteristics and comparisons , 2008 .

[8]  A. Sharma,et al.  Review on thermal energy storage with phase change materials and applications , 2009 .

[9]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[10]  Thomas F. Wenisch,et al.  Peak power modeling for data center servers with switched-mode power supplies , 2010, 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED).

[11]  Fabien Volle,et al.  Thermal Management of a Soft Starter: Transient Thermal Impedance Model and Performance Enhancements Using Phase Change Materials , 2010, IEEE Transactions on Power Electronics.

[12]  Lachlan L. H. Andrew,et al.  Geographical load balancing with renewables , 2011, PERV.

[13]  Anand Sivasubramaniam,et al.  Benefits and limitations of tapping into stored energy for datacenters , 2011, 2011 38th Annual International Symposium on Computer Architecture (ISCA).

[14]  Lachlan L. H. Andrew,et al.  Greening geographical load balancing , 2011, PERV.

[15]  Arlan Burdick,et al.  Strategy Guideline: Accurate Heating and Cooling Load Calculations , 2011 .

[16]  Kevin Skadron,et al.  Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations , 2011, 2011 44th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[17]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[18]  Thomas F. Wenisch,et al.  Power management of online data-intensive services , 2011, 2011 38th Annual International Symposium on Computer Architecture (ISCA).

[19]  Amir Michael,et al.  Facebook: The open compute project , 2011, 2011 IEEE Hot Chips 23 Symposium (HCS).

[20]  Marios C. Papaefthymiou,et al.  Computational sprinting , 2012, IEEE International Symposium on High-Performance Comp Architecture.

[21]  Houman Homayoun,et al.  Managing distributed UPS energy for effective power capping in data centers , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).

[22]  Yefu Wang,et al.  TEStore: Exploiting thermal and energy storage to cut the electricity bill for datacenter cooling , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[23]  Anand Sivasubramaniam,et al.  Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters , 2012, ASPLOS XVII.

[24]  Adam Wierman,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012, SIGMETRICS '12.

[25]  Thu D. Nguyen,et al.  Parasol and GreenSwitch: managing datacenters powered by renewable energy , 2013, ASPLOS '13.

[26]  Marios C. Papaefthymiou,et al.  Computational sprinting on a hardware/software testbed , 2013, ASPLOS '13.

[27]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition , 2013, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition.

[28]  Anand Sivasubramaniam,et al.  Aggressive Datacenter Power Provisioning with Batteries , 2013, TOCS.

[29]  Lingjia Tang,et al.  Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers , 2013, ISCA.

[30]  Marios C. Papaefthymiou,et al.  Utilizing Dark Silicon to Save Energy with Computational Sprinting , 2013, IEEE Micro.

[31]  Adam Wierman,et al.  Data center demand response: avoiding the coincident peak via workload shifting and local generation , 2013, SIGMETRICS '13.

[32]  Lingjia Tang,et al.  SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers , 2014, 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture.

[33]  Gu-Yeon Wei,et al.  Tradeoffs between power management and tail latency in warehouse-scale applications , 2014, 2014 IEEE International Symposium on Workload Characterization (IISWC).

[34]  Kai Ma,et al.  Exploiting thermal energy storage to reduce data center capital and operating expenses , 2014, 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA).

[35]  Myoungsoo Jung,et al.  Power, Energy, and Thermal Considerations in SSD-Based I/O Acceleration , 2014, HotStorage.

[36]  Lingjia Tang,et al.  Protean Code: Achieving Near-Free Online Code Transformations for Warehouse Scale Computers , 2014, 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture.

[37]  Lachlan L. H. Andrew,et al.  Greening Geographical Load Balancing , 2015, IEEE/ACM Transactions on Networking.

[38]  David Oliver Phase-change materials for thermal energy storage , 2015 .

[39]  Eric S. Chung,et al.  A reconfigurable fabric for accelerating large-scale datacenter services , 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).