PDL Abstract Minimizing Data Center SLA Violations and Power Consumption via Hybrid Resource Provisioning 2nd IGCC 2011 (IEEE International Green Computing Conference 2011) July 25-28, 2011 Orlando, Florida, USA. -- BEST PAPER AWARD Anshul Gandhi, Yuan Chen*, Daniel Gmach*, Martin Arlitt*, Manish Marwah* Parallel Data Laboratory Carnegie Mellon University Pittsburgh, PA 15213 * HP Labs http://www.pdl.cmu.edu/ This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Our approach first analyzes historical workload traces to identify long-term patterns that establish a "base" workload. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the estimated base workload at coarse time scales (e.g., hours or days) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy, and reducing provisioning costs. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35% savings in power consumption and reduce SLA violations by as much as 21% compared to existing techniques, while avoiding frequent power cycling of servers. Keywords-data center; power management; performance management; resource allocation FULL PAPER: pdf Parallel Data Laboratory

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PDL Abstract

Minimizing Data Center SLA Violations and Power Consumption via Hybrid Resource Provisioning

2nd IGCC 2011 (IEEE International Green Computing Conference 2011) July 25-28, 2011 Orlando, Florida, USA. -- BEST PAPER AWARD

Anshul Gandhi, Yuan Chen*, Daniel Gmach*, Martin Arlitt*, Manish Marwah*

Parallel Data Laboratory
Carnegie Mellon University
Pittsburgh, PA 15213

* HP Labs

http://www.pdl.cmu.edu/

This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Our approach first analyzes historical workload traces to identify long-term patterns that establish a "base" workload. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the estimated base workload at coarse time scales (e.g., hours or days) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy, and reducing provisioning costs. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35% savings in power consumption and reduce SLA violations by as much as 21% compared to existing techniques, while avoiding frequent power cycling of servers. Keywords-data center; power management; performance management; resource allocation

FULL PAPER: pdf