Mobide2010
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This document presents three algorithms for power-aware operator placement and broadcasting of continuous query results in a distributed stream processing system. The first two algorithms, MinDataCut and MinPowerCut, aim to minimize data transfer or processing power consumption but are not optimal. The third algorithm, BOSe, considers both tuning and processing energy costs to select operator placements. Experiments show BOSe improves energy efficiency by 53% over centralized processing. Future work includes supporting operator and query sharing to further optimize the energy-response time tradeoff.
Mobide2010
- 1. POWER-AWARE OPERATOR PLACEMENT AND BROADCASTING OF CONTINUOUS QUERY RESULTS Panickos Neophytou, Mohamed Sharaf, PanosChrysanthis, AlexandrosLabrinidis MobiDE 2010 – June 6, 2010
- 2. Motivation Energy Constraints
- 3. Streams: Collection, Processing, Delivery Social Media Events Environment Readings DSMS Broadcast Stock Market Q1 Q2 News Continuous Events Q3 Queries (CQs) Q4
- 4. Problem Definition Goal: Designoperator placement algorithms that balance the tradeoff between the overall Tuning and Processing energy at Tuning Energy the clients. Processing Energy Q1 Q1 Tuning Energy Q1 Processing Energy Q2 Q2 Q2 Q3 Q3 Q3 Tuning Energy Processing Energy
- 5. Roadmap Motivation/Introduction System Model Stream Processing Model Broadcast Access Model Operator Placement Algorithms Experiments Conclusion
- 6. Stream Processing Model Selectivity Tuning Power Projectivity Processing Power Cost in cycles Processor Speed Client Tuning Energy: Client Processing Energy:
- 7. Streams Broadcast Model A broadcast is broken into cycles Q1 Q1 Broadcast Organization Q2 Q3 Q3 Q4 Q4 Cycle:
- 8. Streams Broadcast Model A broadcast is broken into cycles Q1 Q1 Broadcast Organization Q2 Q2 Q3 Q4 Q4 Cycle: Q1 Q4 Q3
- 9. Streams Broadcast Model Q5 Q1 Q2 Q3 Q4 Sorted (1) (2) (3) (4) (5) By size Q3 Tuning Energy
- 10. Roadmap Motivation/Introduction System Model Stream Processing Model Broadcast Access Model Operator Placement Algorithms Experiments Conclusion
- 11. Algorithm - MinDataCut Query Plan: Minimal Edge Clients’ Overall Energy Consumption: Tuning Energy Processing Energy MinDataCutgives us the minimal Broadcast Size
- 12. Algorithm - MinPowerCut Query Plan: Minimal Edge Clients’ Overall Energy Consumption: Tuning Energy Processing Energy
- 13. Drawbacks of MinDataCut and MinPowerCut MinDataCut MinPowerCut Tuning Energy Tuning Energy Processing Energy Processing Energy • Oblivious to Processing costs • Processing-energy aware • High processing energy • High impact on tuning energy Q1 Q4 Q3 Q1 Q4 Q3 (1) (5) (6) (3) (5) (6) 4 1 MinPowerCut is oblivious to Broadcast Organization
- 14. BOSe: Broadcast Aware Operator Selection Query Plan (MinDataCut): Query Plan (1 step further): Tuning Energy Tuning Energy Calculate the impact on: Processing Energy Processing Energy 1. processing energy 2. globaltuning 1. Start from the MinDataCut point. 2. For each query, calculate the amount of energy reduction provided by each segment of operators if it were brought back to the server. 3. Bring back the one segment with the maximum reduction. 4. Repeat until no more energy reduction is attainable.
- 15. BOSe: Cost-Benefit Segment from Q1 (at Client N1) Tuning Energy Processing Energy Q1A Q1B Benefit Cost (2) (4.5) tr0 tr1 tr2 N1 N1 N4 N1 N2 N3 N4 Tuning Energy Processing Energy Broadcast Organization (Sorted by size): Q5 Q1A Q2 Q3 Q4 (1) (2) (3) (4) (5)
- 16. Roadmap Motivation/Introduction System Model Stream Processing Model Broadcast Access Model Operator Placement Algorithms Experiments Conclusion
- 17. Experimental Setup Query Workload: Parameter Values Number of queries 20-300 (default 50) Levels per query 2-20 (default 10) Sources tuple rate 500-1000 tuples/sec Sources tuple size 2000-4000 bytes Selectivity 0.2-1.8, uniform Projectivity 0.5-1.5, uniform Operator costs 100*106-200*106 cycles, Zipf Broadcast: Bandwidth 125000 bytes/sec Mobile Clients: CPU Speed 1*109 cycles/sec Processing to Tuning power ratio 0.16
- 18. Processing to Tuning Power Ratio 22% improvement BOSe always performs best
- 19. Scalability: Number of Queries
- 20. Scalability: Number of Operators per Query
- 21. Indexed Broadcast Model Ix Q5 Q1 Q2 Q3 Q4 Indexed (0.5) (1) (2) (3) (4) (5) Q3 Tuning Energy
- 22. Processing vs. Tuning Power 53% improvement
- 23. Conclusions 3 power-aware operator placement algorithms for broadcasting CQ results BOSe algorithm improves by 53% over centralized processing Future: Support sharing of operators Support sharing of queries Study the tradeoff between Energy and Response Time
- 24. Thank you – Questions? Advanced Data Management Technologies Laboratory http://db.cs.pitt.edu Part of AQSIOS project: NSF GRANT IIS-0534531 NSF career award IIS-0746696
Editor's Notes
- #4: “in general it could support more users and more kinds of applications blablabla”We want to disseminate the results with the least amount of energyEmphasize the
- #5: Use the query plan diagram.Show that it can shrink the broadcast etc…
- #10: FOCUS on one client.
- #12: Color the circles.Show Large,Small,Large for Q1. instead of tr*ts…Show the bars of processing and tuning energies
- #13: Change the multiplication to blueEmphasize the plus of the processing powers.Add the sliders to show larger broadcast but smaller processing.
- #14: MPC is a local optimization
- #15: Make the steps of the algorithm clear.Looking at each query using a cost-benefit evaluation which like PMC considers processing but also the impact on the broadcast overall the client population.
- #16: A concrete exampleProcessing to the leftTuning to the right.Energy bars to show how they compare.
- #18: DSMSBroadcastClients
- #19: Remove the second small graph.
- #20: Make larger graphs…. Animate to make it bigger.
- #22: FOCUS on one client.
- #23: Add animation to make it small