Energy Prediction for I/O Intensive Workflow Applications

Energy Prediction for I/O Intensive Workflow Applications

Energy Prediction for I/O Intensive Workflow Applications MASc Exam Hao Yang NetSysLab The Electrical and Computer Engineering Department The University of British Columbia 1 Background - Workflow Applications Computation Characteristics: File based communication Large number of tasks

Large amount of I/O Common data access patterns File Dependency Montage Workflow 2 Background - Application Execution File based communication Workflow Runtime Engine Large I/O volume App. task

App. task App. task App. task App. task Local storage Local storage Local storage

Local storage Local storage I/O Bottleneck Central Storage System (e.g., GPFS, NFS) 3 Background - Intermediate Storage System Workflow Runtime Engine Compute Nodes

App. task Local storage App. task App. task Local storage Local storage Stage Out

Intermediate Storage Stage In Central Storage System (e.g., GPFS, NFS) 4 Background - Context of this thesis This work focuses on workflow application execution on intermediate storage systems. 5 Research Problem Energy Consumption Computing Equipment Energy Bill

The pursuit of performance use to dominate the conventional computing area. Energy efficiency is the new concern. 6 Research Problem - Configuration Decisions Configuring the runtime system is complex (Example: resource allocation decision) Montage Workload Energy Delay Product (EDP) 7 Research Problem - Questions Q1: What performance optimizations in storage systems lead

to energy savings? Q2: What is the performance and energy impact of powercentric tuning techniques? Q3: How can users balance time-to-solution and energy consumption when given a target application? 8 Outline Background Research Problem

Methodology Evaluation Conclusion 9 Methodology Building Energy Consumption Predictor The goal of this work is to build an energy consumption predictor to aid system configuration and provisioning decisions. Answer what-if questions (E.g, is A configuration better than B from the energy perspective?) Customize optimization metric (E.g., energy consumption, performance-energy product) 10

Methodology Energy Model Workflow Runtime Engine A Compute Nodes B App. task App. task Local storage

Local storage C D App. task App. task Local storage

Local storage Intermediate Storage Execution States: Idle Network Transfer Storage I/O Task Processing Power Profiles: 11 Methodology Energy Model

Execution States: Energy Power Profile * Predicted Times Idle Network Transfer I/O ops (read, write) Task Processing 12 Methodology Energy Model How to seed the energy model? Power states: using synthetic benchmarks to get

the power consumption in each state. Time estimates: augments a performance predictor to track the time spent in each state. 13 Methodology Building Energy Consumption Predictor Sources of inaccuracies Model Simplification (metadata, scheduling, ) Time Prediction homogeneity, Power meter L. B. Costa, S. Al-Kiswany, H. Yang, and M. Ripeanu, Supporting Storage Configuration for I/O Intensive Workflows, In Proceedings of the 28th ACM International Conference on

Supercomputing, ICS'14, (Acceptance Rate: 20%) June 2014. L. B. Costa, S. Al-Kiswany, A. Barros, H. Yang, and M. Ripeanu, Predicting Intermediate Storage Performance for Workflow Applications, In Proceedings PDSW'13, 2013. 14 Evaluation Outline Synthetic benchmarks: Workflow Patterns Real workflow applications Predicting Energy Impact of Power-tuning Techniques Predicting Energy-Performance Tradeoffs

15 Evaluation - Platform Grid5000 Lyon site Idle Taurus Cluster (11 nodes) App Storage two 2.3GHz Intel Xeon E5-2630 CPUs (each I/O with 6 cores), Net transfer 32GB memory, 10 Gbps NIC

Sagittaire Cluster (16 nodes) two 2.4GHz AMD Opteron CPUs (each with one core), 2GB RAM and 1 Gbps NIC SME Omegawatt power-meter per Node 0.01W power resolution at 1Hz sampling rate 16 Evaluation Synthetic benchmarks: Workflow Patterns Montage Workflow Reduce Pipeline 17

Evaluation Synthetic benchmarks: Workflow Patterns 18 Evaluation Synthetic benchmarks: Workflow Patterns Average 88% accuracy 20-30x times faster than running the actual benchmark 200x-300x less resources (machines * runtime) Using Default Storage System Configuration (DSS) 19

Evaluation Synthetic benchmarks: Workflow Patterns Q1: What are the energy savings that performance optimizations in storage can bring? DSS Default Storage System Accurate in both configurations. Configuration WOSS Suggests the configuration from Workflow Optimized Storage energy perspective. System Configuration

Pipeline Energy Consumption S. Al-Kiswany, L. B. Costa, H. Yang, E. Vairavanathan, M. Ripeanu, The Case for Cross-Layer Optimizations in Storage: A Workflow-Optimized Storage System, IEEE Transactions on Parallel and Distributed Systems (TPDS), Under Review, Submitted in June 2014 L.B. Costa, H. Yang, E. Vairavanathan, A. Barros, K. Maheshwari, G. Fedak, D.S. Katz, M. Wilde, M. Ripeanu and S. Al-Kiswany, The Case for Workflow-Aware Storage: An Opportunity Study using MosaStore, Journal of Grid Computing 2014. 20 Evaluation Real Workflow Applications BLAST workflow Montage workflow 21

Evaluation Real Workflow Applications BLAST Result (Energy 89%, Time 95% ) Montage Result (Energy 84%, Time 86% ) 22 Evaluation CPU Throttling CPU throttling is an important technique where processors run at less-than-maximum frequency to Q2: What is the energy and performance

conserveofpower. impact CPU throttling? Is it application this technique can prolong the execution time while specific? conserving instantaneous power. CPU bound application: BLAST I/O bound application: pipeline benchmark 23 Evaluation CPU Throttling Frequency Level: 1200MHz, 1800MHz, 2300MHz Conclusion: The computational and I/O characteristics

Energy BLAST Result Time 96% cost when using maximum CPU throttling Energy savings/ energy costs The predictor can be used in make the decisions. Energy

Pipeline Result Time 17% savings when using maximum throttling 24 Evaluation Predicting Energy Delay Product Users optimization metric Performance (use more machines)

Energy Energy-Delay Product (EDP, energy * time) Q3: How can users balance time-to-solution and energy consumption when given a target application? Consider allocation decision. Use Montage workload on two clusters to demonstrate prediction. 25 Evaluation Predicting Energy Delay Product Montage EDP at Sagittaire Montage EDP at Taurus

26 Conclusion This thesis presents an energy consumption predictor in the workflow application domain. The proposed energy model and prediction framework achieve adequate accuracy to be useful for the energyoriented configurations this work targets. 27 Resulting Publications Energy Prediction H. Yang, L. B. Costa and M. Ripeanu, Energy Prediction for I/O Intensive Workflows Applications, submitted to 7th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS) 2014 (Co-located with Supercomputing/SC 2014), under-review. Performance Prediction and Provisioning

L. B. Costa, S. Al-Kiswany, H. Yang, and M. Ripeanu, Supporting Storage Configuration and Provisioning for I/O Intensive Workflows, In Preparation. L. B. Costa, S. Al-Kiswany, H. Yang, and M. Ripeanu, Supporting Storage Configuration for I/O Intensive Workflows, In Proceedings of ICS'14, Acceptance rate: 20%. June 2014. L. B. Costa, S. Al-Kiswany, A. Barros, H. Yang, and M. Ripeanu, Predicting Intermediate Storage Performance for Workflow Applications, In Proceedings PDSW'13, 2013. A Workflow-Optimized Storage System S. Al-Kiswany, L. B. Costa, H. Yang, E. Vairavanathan , M. Ripeanu, A Software Defined Storage for Scientific Workflow Applications, In Preparation. S. Al-Kiswany, L. B. Costa, H. Yang, E. Vairavanathan, M. Ripeanu, The Case for Cross-Layer Optimizations in Storage: A Workflow-Optimized Storage System, IEEE Transactions on Parallel and Distributed Systems (TPDS), Under Review, Submitted in June 2014 L.B. Costa, H. Yang, E. Vairavanathan, A. Barros, K. Maheshwari, G. Fedak, D.S. Katz, M. Wilde, M. Ripeanu and S. Al-Kiswany, The Case for Workflow-Aware Storage: An Opportunity Study using MosaStore,

accepted by Journal of Grid Computing, 2014. Evaluating Storage Systems for Scientific Data in the Cloud K. Maheshwari, J. Wozniak, H. Yang, D. S. Katz, M. Ripeanu, V. Zavala, M. Wilde, Evaluating Storage Systems for Scientific Data in the Cloud, In Proceedings of the 5th Workshop on Scientific Cloud Computing (ScienceCloud), Co-located with ACM HPDC 2014 (Best Paper Award) 28 Backup Slides System Deployment Configuration I/O traces Number Number of of Storage

Storage Nodes Nodes Task Dependency Graph The system model Model seeding Workload description Number Number of of Client Client Nodes Nodes Chunk Chunk Size Size

Replication Level Replication Level Platform Performance Parameters Manger Service Time Storage Service Time Client Service Time Remote network service Time Local network service time lo

L. B. Costa, S. Al-Kiswany, H. Yang, and M. Ripeanu, Supporting Storage Configuration for I/ O Intensive Workflows, In Proceedings of the 28th ACM International Conference on Supercomputing, ICS'14, June 2014. 29 Backup Slides Limitations: Simplification of the model Short tasks/ small workload Not validated using new devices (e.g, SSD) 30 Backup Slides Alternative Approaches: Utilization

Detailed simulation Machine learning 31 Backup Slides Combined states Apply benchmarks in parallel to get combined power state: E.g., perform storage and network benchmarks in parallel 91.6W, :129.0W, : 127.7W 32 Backup Slides

Energy Composition (pipeline benchmark): Idle energy: 64% App processing: 9.2% Storage operations: 15.8% Network transfer: 10.6% 33 Backup Slides Sagittaire power profiles 175W 25W 8W 7W 34

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