Autonomic Power and Performance Management of Large-scale Data Centers

1. Center for Autonomic Computing Planning Workshop, September 2007 Autonomic Power and Performance Management of Large-scale Data Centers Salim Hariri, Ph.D. Professor of ECE, University of Arizona Bithika Khargharia, PhD Student, University of Arizona

2. Outline • Project Goals, Motivations, Challenges • Background and Related Research • Project Team Members (faculty & students) • Tasks • Overview of First Year Tasks • Task 1: Hierarchical Modeling of Autonomic Power and Performance Management • Task 2: Develop online power/performance management strategies • Task 3: Validation and Evaluation of performance on a test bed • Milestones, Deliverables, Budget • Conclusions, Member Benefits

3. Project Goals, Motivation, Challenges • Motivation • Reduce the power consumption in data centers while maintaining performance • Detect resource over-provisioning when they occur • Identify appropriate power/performance management strategies that reconfigure the data center components to adapt to the incoming workload • Develop a scalable and general solution for power management. • Goals • Online modeling, monitoring and analysis of power and performance for geographically distributed data centers • Autonomic Runtime Manager • Automatic detection of application execution phases and properties using AppFlow • Select the appropriate management algorithm at runtime to optimize both performance and power consumption • Integrate with Anomaly Based Management Framework (Autonomia) • Challenges • Accurate modeling of energy consumption from a system-level perspective • Real-time prediction of system resources behavior as workloads change dynamically by several order of magnitude within a day or a week – AppFlow Data Structure • Design “instantaneous” and adaptive optimization mechanisms to develop efficient power and performance management strategies – Game Theory, Optimization Theory

4. Background & Related Research • Earlier Work: Power/Performance Management in isolation • Proof of concept: • Developed autonomic power/performance management for server clusters and memory sub-system within a server. Solutions at the cluster-level are refined at the lower-levels. • Objective: Autonomic Power and Performance Management of Data Centers • Optimization for power and performance at all levels of the data center • Improve scalability and efficiency • Solution: Hierarchical approach • Solution refining going from one hierarchy to another • Autonomic data structure that enables runtime decision making • Evaluate strategies derived from different research areas – Game Theory, Optimization, Mathematical Programming etc. • Advantages • Adaptive to workload, scalable and efficient • Good runtime performance

5. Project Team Members • Faculty • Dr. Salim Hariri • Professor of ECE, University of Arizona • Dr Hesham El Rewini • Professor of CSE, Southern Methodist University • Dr Ishfaq Ahmad • Professor of CSE, University of Texas at Arlington • Dr Mazin S. Yousif • Intel Corporate Technology Group • Students • Bithika Khargharia, - student project leader • PhD Candidate, University of Arizona • Manal Houri, Abdul Aziz, Wael Kudoh • PhD Students, Southern Methodist University • Samee Khan • PhD Candidate, University of Texas at Arlington

6. Overview of Tasks • Three primary tasks planned for Y1 • Task 1: Hierarchical autonomic power and performance management • End-to-end modeling of large scale distributed data centers for power and performance management • Task 2: Develop online power/performance management strategies based on Optimization, Game Theory • Evaluate effectiveness of different power minimization strategies implemented at different data center hierarchies • Develop the AppFlow data structure for online decision support • Task 3: Demonstrate a prototype on implemented testbed(s) • Development of a test-bed to enable the experimentation and evaluation of different power consumption models and optimization algorithms. • This prototype will be implemented within the Autonomia autonomic management framework

7. Task 1: Hierarchical Modeling of Autonomic Power and Performance Management Workload Profiling SMU Game Theory UTA Runtime Optimization Techniques UOA

8. Task 2 (Y2): Develop online power and performance management strategies • Application flows characterize the dynamic behaviors and resource requirements of data center workloads. • Autonomic Manager use AppFlows to allocate current workloads to managed resources such that both performance and power are optimized. • AppFlow is coherent and consistent as we move from one hierarchy to another. • AppFlow captures both resource requirements and temporal behaviors of applications

9. Memory Power and Performance Management Maximize performance/watt such that • Maximum performance/watt • improvement of 88.48% • Energy saving of about 48.8 % (26.7 kJ)

10. Task 3: Build a Prototype • Incorporate power/performance as an autonomic management objective within Autonomia • Use existing Autonomia software tools to implement the autonomic management strategies on UA test-bed. • Generate a wide range of workloads that are typical in large-scale data centers • Demonstrate the capabilities of the autonomic runtime manager for power/performance management • Evaluate system performance and overhead, and the scalability of the proposed runtime management strategies

11. Y1 Milestones, Deliverables, Budget • Milestones • Developed and validated power/performance models for server clusters and memory (Feb ’07). • Developed strategies based on Game Theory and Optimization Theory (Jun ’07). • Developing AppFlow for online decision support (Oct ’07). • Deliverables • Midterm and final reports documenting research methods, progress, results, and analysis • Power/performance management algorithms prototyped in hardware and simulation. • Two scholarly conference and/or journal publications • Budget • 95K/Year

12. Conclusions & Member Benefits • Conclusions • Hierarchical Autonomic Power/Performance Management. • Power/Performance management strategies based on Data mining, optimization and Game Theory. • Develop run-time data structure for autonomic decision making. • Member Benefits • Use developed autonomic management strategies to optimize power and performance in their data centers • Access to research results, algorithms developed and publications • Assist in adapting results to the member computing environment