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Bridging the Tenant-Provider Gap in Cloud Services

Bridging the Tenant-Provider Gap in Cloud Services. Virajith Jalaparti , Hitesh Ballani , Paolo Costa Thomas Karagiannis , Ant Rowstron. Today’s Interface to the Cloud. Resource-centric Interface “I want 100 small VMs” Per-VM Per-Hour pricing E.g.: $0.08 per hour in Amazon EC2.

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Bridging the Tenant-Provider Gap in Cloud Services

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  1. Bridging the Tenant-Provider Gap in Cloud Services VirajithJalaparti, Hitesh Ballani, Paolo Costa Thomas Karagiannis, Ant Rowstron

  2. Today’s Interface to the Cloud • Resource-centric Interface • “I want 100 small VMs” • Per-VM Per-Hour pricing • E.g.: $0.08 per hour in Amazon EC2 Simple but problematic! Bridging the Tenant-Provider Gap in Cloud Services

  3. Using the Resource-centric Interface • Cloud Provider Thrs, $40T Job 2T hrs,$80T!! 40 VMs Done in Thrs Private Cluster [40 machines] User Unpredictable/Variable Performance and Costs Bridging the Tenant-Provider Gap in Cloud Services

  4. Proposal: Job-centric Interface • Provider determines resources to use to meet tenants’ goal • VMs, Network Bandwidth etc. • Tenant specifies high-level goals they care about • Completion Time, Cost to run a job etc. • Cloud Provider Finish in Thrs Done in Thrs!! Dedicated Resources User Bridging the Tenant-Provider Gap in Cloud Services

  5. Proposal: Job-centric Interface Guaranteed performance for tenants Bridging the Tenant-Provider Gap in Cloud Services

  6. Proposal: Job-centric Interface Incentive for provider? Exploit multi-resource tradeoff Resource Trade-off Curve LinkGraph in 300sec Increases Goodput/Revenue <N,B> = <10, 150> <N,B> = <20, 100> Bridging the Tenant-Provider Gap in Cloud Services

  7. Outline • Motivation • Job-centric Interface • Multi-resource tradeoff • Bazaar: A Job-centric Cloud Framework • Performance Prediction • Resource Selection • Evaluation • Bazaar: Extensions and Opportunities • Conclusion Bridging the Tenant-Provider Gap in Cloud Services

  8. Bazaar: A Job-centric Cloud Framework <N1,B1> <N2,B2> … <Nk,Bk> Job Specification Resource tuple Performance Prediction Resource Selection Bazaar <N,B> Completion Time User Datacenter State • Focus on MapReduceApplications • Focus on two resources: Compute (N) and Bandwidth (B) • Notation: <N,B> denotes resources allocated Bridging the Tenant-Provider Gap in Cloud Services

  9. Performance Prediction • Well studied area • Run-time profiling, Static analysis, Simulations • Bazaar requirements • Fast prediction (trades-off with accuracy) • Account for Network along with Compute • Not addressed by Jockey, MRPerf, Aria • Dedicated N and B makes the problem tractable Bridging the Tenant-Provider Gap in Cloud Services

  10. MRCuTE: Performance Prediction in Bazaar Analytical Modeling + Profiling based approach Resource Parameters <N, B> MRCuTE Analytical Model Completion Time Profiler Job Specifications Program (P), Input data (I), Sample Data (Is) Job Specific Map • Completion Time determined by • Input data size • Rate of progress Reduce Map Program (P) profiled using sample data (Is) on one machine Reduce Map Map Phase Shuffle Phase Reduce Phase Bridging the Tenant-Provider Gap in Cloud Services

  11. Resource Prediction • MRCuTE( P, I, Is,N, B )  Completion Time • N = MRCuTE-1( P, I, Is, Completion Time , B) User specified Provider can determine multiple <N, B> resource tuples B1 N1 B2 N2 B3 N3 … Which <N,B> to use? Bridging the Tenant-Provider Gap in Cloud Services

  12. Resource Selection Which <N,B> tuple maximizes the provider’s ability to accept future requests? Increases Goodput/Revenue Bridging the Tenant-Provider Gap in Cloud Services

  13. Resource Selection: Example R1 = <3 VMs, 500Mbps> or R2 = <6 VMs, 200Mbps> <4 VMs, 400Mbps> TOR TOR Replica 1 Replica 2 Select the resource tuple leading to better goodput 4 Physical machines 2 VM slots each Greedy packing allocation algorithm : Oktopus [Sigcomm’11] Replica 1 will accept more requests than Replica 2 Bridging the Tenant-Provider Gap in Cloud Services

  14. Resource Selection • Similar to Multi-dimensional Bin Packing • Heuristic: Minimize Resource Imbalance Metric • Select <N,B> which balances the remaining capacity across resources Bridging the Tenant-Provider Gap in Cloud Services

  15. Outline • Motivation • Job-centric Interface • Resource Malleability • Bazaar: A Job-centric Cloud Framework • Performance Prediction • Resource Selection • Evaluation • Bazaar: Extensions and Opportunities • Conclusion Bridging the Tenant-Provider Gap in Cloud Services

  16. Evaluation • MRCuTE: Prediction accuracy • Benefits of Bazaar • Testbed Deployment • Simulations Bridging the Tenant-Provider Gap in Cloud Services

  17. MRCuTE: Prediction Accuracy • Setup: Hadoop on 35-node Emulabcluster Sort with 200GB of random data Average prediction error = 8.9% Bridging the Tenant-Provider Gap in Cloud Services

  18. MRCuTE: Prediction Accuracy 5 MapReduce Jobs Average Error < 12% Overcome prediction inaccuracies using slack Bridging the Tenant-Provider Gap in Cloud Services

  19. Evaluation: Benefits of Bazaar • Metrics • Fraction of rejected/accepted requests • Datacenter Goodput • Strategies • Bazaar: Select <N,B> using resource imbalance metric • Baseline: Select <N,B> randomly • Workload • Poisson job arrival process with a target arrival rate Bridging the Tenant-Provider Gap in Cloud Services

  20. Bazaar: Testbed Deployment • Working prototype on 26 node Emulab cluster • Workload: 100 Sort Jobs 15.5% more 11.4% more Bridging the Tenant-Provider Gap in Cloud Services

  21. Bazaar: Simulations • Datacenter scale: 16,000 machines • Cross-validated using testbed Bazaar is ~50% better than baseline Operational occupancy range for services like Amazon EC2 is 70-80% Requests arrive faster Bridging the Tenant-Provider Gap in Cloud Services

  22. Outline • Motivation • Job-centric Interface • Resource Malleability • Bazaar: A Job-centric Cloud Framework • Performance Prediction • Resource Selection • Evaluation • Bazaar: Extensions and Opportunities • Conclusion Bridging the Tenant-Provider Gap in Cloud Services

  23. Bazaar-T: An extension of Bazaar • Bazaar trades-off N and B • Finish jobs “on time” • Bazaar-T: Exploits flexibility with time • Finish jobs “before time” • More resources available in future • Extend resource imbalance metric to time domain SOCC 2012 - Bazaar

  24. Bazaar-T: More Flexibility, More Gains Bazaar vs. Bazaar-T Bazaar-T has 10-20% more goodput than Bazaar Bridging the Tenant-Provider Gap in Cloud Services

  25. Bazaar: Pricing implications • Today: Resource-based pricing • E.g: Using 20 VMs for 4hrs costs $80 • Extendable to multiple resources • No incentive for provider to finish in time • Bazaar enables job-based pricing • E.g.: Finish Sort over 200GB in 4hrs costs $100 • Tenants pay based on job characteristics • Aligns tenant and provider interests Bridging the Tenant-Provider Gap in Cloud Services

  26. Conclusion • Bazaar: Job-centric Framework for MapReduce • Win-win situation for provider and tenant • Tenants get predictable performance • Providers get increased revenue • Provides new avenues for pricing Bridging the Tenant-Provider Gap in Cloud Services

  27. Thank You! Bridging the Tenant-Provider Gap in Cloud Services

  28. Back-up Slides Bridging the Tenant-Provider Gap in Cloud Services

  29. Related Work • Performance Prediction • MRPerf [Mascots 2009], Mumak • Detailed Simulations • Elastisizer[SOCC 2011] • Detailed Modeling of MapReduce • SLOs • Jockey [Eurosys 2012]: • Simulations; Runtime monitoring to meet deadline • Conductor [NSDI 2012] • Solves optimization problem to meet goals • Proteus [Sigcomm 2012]: • Time varying network reservations • Aria [ICAC 2011] • Profiling and modeling Bridging the Tenant-Provider Gap in Cloud Services

  30. Hadoop Jobs Details Bridging the Tenant-Provider Gap in Cloud Services

  31. MRCuTE: Profiling Time Bridging the Tenant-Provider Gap in Cloud Services

  32. MRCuTE: Accounting for heterogeneity Bridging the Tenant-Provider Gap in Cloud Services

  33. Addressing Skew- Slack % of Late Jobs vs. Slack % of Rejected Requests vs. Slack Bridging the Tenant-Provider Gap in Cloud Services

  34. Goodput vs. Oversubscription Bridging the Tenant-Provider Gap in Cloud Services

  35. Rejected Requests vs. Mean BW Bridging the Tenant-Provider Gap in Cloud Services

  36. Rejected requests vs. Occupancy Bridging the Tenant-Provider Gap in Cloud Services

  37. Bazaar vs. Fair Sharing Bridging the Tenant-Provider Gap in Cloud Services

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