Operational Cost Optimization for Cloud Computing Data Centers Using Renewable Energy

The electricity cost of cloud computing data centers, dominated by server power and cooling power, is growing rapidly. To tackle this problem, inlet air with moderate temperature and server consolidation are widely adopted. However, the benefit of these two methods is limited due to conventional air cooling system ineffectiveness caused by recirculation and low heat capacity. To address this problem, hybrid air and liquid cooling, as a practical and inexpensive approach, has been introduced. In this paper, we quantitatively analyze the impact of server consolidation and temperature of cooling water on the total electricity and server maintenance costs in hybrid cooling data centers. To minimize the total costs, we proposed to maintain sweet temperature and available sleeping time threshold (ASTT) by which a joint cost optimization can be satisfied. By using real-world traces, the potential savings of sweet temperature and ASTT are estimated to be average 23% of the total cost, while 96% requests are satisfied compared to a strategy which only reduces electricity cost. The co-optimization is extended to increase the benefit of the renewable energy, and its profit grows as more wind power is supplied.

[1]  Kenneth G. Brill.  The Economic Meltdown of Moore's Law and the Green Data Center , 2007, LISA.

[2]  Dimos Poulikakos,et al.  Aquasar: A hot water cooled data center with direct energy reuse , 2012 .

[3]  J. Hardisty.  The Analysis of Tidal Stream Power , 2009 .

[4]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[5]  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.

[6]  M.K. Patterson,et al.  The effect of data center temperature on energy efficiency , 2008, 2008 11th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[7]  Ke Chen,et al.  System implications of memory reliability in exascale computing , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[8]  Zhao Zhang,et al.  Thermal modeling and management of DRAM memory systems , 2007, ISCA '07.

[9]  Vincent P. Manno,et al.  Energy savings achievable through liquid cooling: A rack level case study , 2010, 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[10]  Nilesh Pancholi,et al.  Guidelines to Understanding Design of Experiment and Reliability Prediction , 2013 .

[11]  Thomas F. Wenisch,et al.  PowerNap: eliminating server idle power , 2009, ASPLOS.

[12]  J. Sikora.  Disk failures in the real world : What does an MTTF of 1 , 000 , 000 hours mean to you ? , 2007 .

[13]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[14]  C. Patel,et al.  Model-Based Approach for Optimizing a Data Center Centralized Cooling System , 2006 .

[15]  Thomas F. Wenisch,et al.  DreamWeaver: architectural support for deep sleep , 2012, ASPLOS XVII.

[16]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[17]  Jian Li,et al.  TAPO: Thermal-aware power optimization techniques for servers and data centers , 2011, 2011 International Green Computing Conference and Workshops.

[18]  E. N. Elnozahy,et al.  Energy-Efficient Server Clusters , 2002, PACS.

[19]  Amip J. Shah,et al.  Green server design: beyond operational energy to sustainability , 2010 .

[20]  J. G. Elerath.  Specifying reliability in the disk drive industry: No more MTBF's , 2000, Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055).

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

[22]  Myron Hlynka,et al.  Queueing Networks and Markov Chains (Modeling and Performance Evaluation With Computer Science Applications) , 2007, Technometrics.

[23]  Eric Peterson,et al.  Hybrid cooled data center using above ambient liquid cooling , 2010, 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[24]  Eduardo Pinheiro,et al.  Failure Trends in a Large Disk Drive Population , 2007, FAST.

[25]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[26]  T. N. Vijaykumar,et al.  Joint optimization of idle and cooling power in data centers while maintaining response time , 2010, ASPLOS XV.

[27]  David E. Irwin,et al.  Ensemble-level Power Management for Dense Blade Servers , 2006, 33rd International Symposium on Computer Architecture (ISCA'06).

[28]  Mukund Patel,et al.  Wind and Solar Power Systems , 1999 .

[29]  Kai Strunz,et al.  Modeling of tidal energy conversion systems for smart grid operation , 2011, 2011 IEEE Power and Energy Society General Meeting.

[30]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[31]  Sarita V. Adve,et al.  AS SCALING THREATENS TO ERODE RELIABILITY STANDARDS, LIFETIME RELIABILITY MUST BECOME A FIRST-CLASS DESIGN CONSTRAINT. MICROARCHITECTURAL INTERVENTION OFFERS A NOVEL WAY TO MANAGE LIFETIME RELIABILITY WITHOUT SIGNIFICANTLY SACRIFICING COST AND PERFORMANCE , 2005 .

[32]  Chao Li,et al.  A quantitative analysis of cooling power in container-based data centers , 2011, 2011 IEEE International Symposium on Workload Characterization (IISWC).