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An Analytical Model for Multi-tier Internet Services and its Applications

This paper presents an analytical model for multi-tier internet services, focusing on capacity provisioning, performance prediction, application configuration, and request policing. The model captures the behavior of clients, request processing, and interactions between tiers, and is validated using 3-tier applications. Dynamic capacity provisioning and request policing techniques are also discussed.

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An Analytical Model for Multi-tier Internet Services and its Applications

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  1. An Analytical Model for Multi-tier Internet Services and its Applications Bhuvan Urgaonkar, Giovanni Pacifici, Prashant Shenoy, Mike Spreitzer, Asser Tantawi University of Massachusetts and IBM TJ Watson

  2. Internet Applications • Proliferation of Internet applications auction site online game online store • Growing significance in personal, business affairs • Focus: Modeling Internet applications

  3. Why Model Internet Applications? • Capacity provisioning • How many servers does the application need? • Performance prediction • E.g., predict response time • Application configuration • Tune various application parameters • Request policing • Turn away excess requests during overloads

  4. Internet Application Architecture queries search “moby” response Melville’s ‘Moby Dick’ Music CDs by Moby HTTP J2EE Database request processing in an online bookstore • Multi-tier architecture • Each tier uses services provided by its successor • Session-based workloads • Caching, replication

  5. Existing Application Models • Models for Web servers [Chandra03, Doyle03] • Do not model Java server, database etc. • Black-box models [Kamra04, Ranjan02] • Unaware of bottleneck tier • Extensions of single-tier models[Welsh03] • Fail to capture interactions between tiers • Existing models inadequate for multi-tier Internet applications

  6. Talk Outline • Motivation • Application Model • Evaluation of the Model • Dynamic Capacity Provisioning • Summary and Future Research

  7. Baseline Application Model • Model consists of two components • Sub-system to capture behavior of clients • Sub-system to capture request processing inside the application clients application

  8. Modeling Clients • Clients think between successive requests • Infinite server system to capture think time Z • Captures independence of Z from processing in application Z Client 1 Z Client 2 clients application Z Client N Q0

  9. Modeling Request Processing p1 p3 pM=1 p2 S1 S2 SM Q1 Q2 QM N tier 1 tier 2 tier M • Transitions defined to capture circulation of requests • Request may move to next queue or previous queue • Multiple requests are processed concurrently at tiers • Processor sharing scheduling discipline • Caching effects get captured implicitly!

  10. Putting It All Together p1 p3 pM=1 • A closed-queuing model that captures a given number of simultaneous sessions being served Z p2 S1 S2 SM client Z client Q1 Q2 QM Q0 N tier 1 tier 2 tier M

  11. Model Solution and Parameter Estimation • Mean Value Analysis (MVA) Algorithm • Computes mean response time • Visit ratios • Equivalent to trans. probs. for MVA • Vi ≈ λi / λreq ; λreq at policer, λifrom logs • Service times • Use residence time Xi logged at tier i • For last tier, SM ≈ XM • Si = Xi – ( Vi+1 / Vi ) ·Xi+1 • Think time • Measured at the entry point of application

  12. Talk Outline • Motivation • Application Model • Evaluation of the Model • Dynamic Capacity Provisioning • Summary and Future Research

  13. 30000 Observed 25000 Basic Model 20000 Avg resp time (msec) 15000 10000 5000 0 0 100 200 300 400 500 Num sessions Evaluation of Baseline Model • Auction site RUBiS • One server per tier Apache JBOSS Mysql 75 150 • Concurrency limits not captured

  14. Handling Concurrency Limits Z • Requests may be dropped due to concurrency limits • Need to model the finiteness of queues! S1 S2 SM Z Q1 Q2 QM Q0 N dropped requests

  15. Handling Concurrency Limits Z • Approach: Subsystems to capture dropped requests • Distinguish the processing of dropped requests S1 S2 SM Z Q1 Q2 QM Q0 N drop Q1 QM drop pM drop p1 drop drop drop S1 SM

  16. 30000 Observed 25000 Basic Model Enh Model 20000 Avg resp time (msec) 15000 10000 5000 0 0 100 200 300 400 500 Num sessions Response Time Prediction • Enhanced model can capture concurrency limits

  17. Query Caching at the Database • Caching effects • Captured by tuning Vi and/or Si • Bulletin-board site RUBBoS • 50 sessions • SELECT SQL_NO_CACHE causes Mysql to not cache the response to a query • More model enhancements • Replication at tiers • Multiple session classes

  18. Prototype Data Center • 40+ Linux servers • Gigabit switches • Multi-tier applications • Auction (RUBiS) • Bulletin-board (RUBBoS) • Apache, JBOSS (replicable) • Mysql database

  19. Dynamic Capacity Provisioning • Auction application RUBiS • Factor of 4 increase in 30 min Server allocations Workload Response time • Server allocations increased to match increased workload • Response time kept below 2 seconds

  20. Talk Outline • Motivation • Baseline Application Model • Evaluation of the Model • Dynamic Capacity Provisioning • Summary and Future Research

  21. Summary and Future Work • Analytical model for Multi-tier Internet Applications • Mean-value analysis • Concurrency limits, replication, caching, multiple classes • Model validation using 3-tier applications • Dynamic provisioning, request policing • Future work • Handling load imbalances at replicated tiers • Handling more diverse workloads • Handling other kinds of scheduling disciplines at servers

  22. Thank you! More information at: http://www.cs.umass.edu/~bhuvan

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