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Injecting Realistic Burstiness to a Traditional Client-Server Benchmark

Injecting Realistic Burstiness to a Traditional Client-Server Benchmark

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Injecting Realistic Burstiness to a Traditional Client-Server Benchmark

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  1. Injecting Realistic Burstiness to a Traditional Client-Server Benchmark Ningfang Mi College of William and Mary Giuliano Casale SAP Research Ludmila Cherkasova Hewlett-Packard Labs Evgenia Smirni College of William and Mary Presenter: Lucy Cherkasova

  2. Front Server Web + Application Server Burstiness DB Server ? ? ? Highly Correlated Arrivals Origin of Burstiness • Enterprise and Internet applications: HTTP request SQL query HTTP reply SQL reply Clients International Conference on Autonomic Computing and Communications (ICAC) 2009

  3. Front Server Web + Application Server DB Server ? ?   Client-Server Benchmark • E.g., TPC-W (On-line bookstore Web site) • Exponentially distributed user think times Burstiness HTTP request SQL query HTTP reply SQL reply ? Clients Highly Correlated Arrivals International Conference on Autonomic Computing and Communications (ICAC) 2009

  4. Typical Client-Server Benchmark • Accounts for randomness and variability … • … but not for burstiness • Can we ignore burstiness in the arrival process? Variability Burstiness Service time Service time Request number Request number International Conference on Autonomic Computing and Communications (ICAC) 2009

  5. Why Need to Inject Burstiness? • Burstiness impacts the performance of resource allocation mechanisms. • Example: Session-based admission control (SBAC) • User session: sequence of transaction requests • Session is a unit of work • Typically, long sessions are “sales”. • Useful system throughput is the number of completed sessions • Admission controller admits/rejects sessions based on observed CPU utilization of the server (a combination of last measurement and some history). L. Cherkasova, P. Phaal. Session Based Admission Control: a Mechanism for Peak Load Management of Commercial Web Sites. IEEE J. TOC, June 2002. International Conference on Autonomic Computing and Communications (ICAC) 2009

  6. Front Server Web + Application Server DB Server New Client Arrival limited server queue highly undesirable Requests from already accepted clients SBAC • Reject a new session when utilization is above the threshold • Abort an accepted session when the server queue is full International Conference on Autonomic Computing and Communications (ICAC) 2009

  7. highly undesirable Impact of Burstiness • We performed experiments for the same workload with different arrival patterns: non-bursty vs bursty • Aborted ratio = aborted sessions/accepted sessions International Conference on Autonomic Computing and Communications (ICAC) 2009

  8. Why Need to Inject Burstiness? (2) • Service level agreement (SLA) • support given response time guarantees for accepted sessions • SLA of 1.2s can be supported for 98% of requests with queue size =250 for non-bursty traffic • Only 90% of requests meet SLA=1.2s bursty traffic. Non-Bursty Bursty Response Time (s) Response Time (s) Queue Size Queue Size International Conference on Autonomic Computing and Communications (ICAC) 2009

  9. Limitations of Standard TPC-W • Think times are drawn randomly from the exponential distribution identical for all clients • Exponential think times are incompatible with the notion of burstiness. Need to inject burstiness into user think times. International Conference on Autonomic Computing and Communications (ICAC) 2009

  10. Our Methodology • Basic Idea: modify the distribution of client think time to create bursty arrivals • Regulate the arrivals by using a 2-phase Markovian Arrival Process (MAP). • MAPs are variations of popular On/OFF traffic models that can be easily shaped to create correlated inter-arrival times • All clients share a MAP(2) to draw think times • A new module for client-server benchmarks • Regulate the intensity of traffic surges by using the index of dispersion. • A simple tunable knob of burstiness International Conference on Autonomic Computing and Communications (ICAC) 2009

  11. burstiness variability Index of Dispersion (I) • Popular burstiness index in networking • Definition • SCV – the squared coefficient of variation (variance/mean2) • ρk – autocorrelation coefficients • i.e., correlation of service times • Exponential: I = SCV = 1 Variability Burstiness Service time Service time Request number Request number International Conference on Autonomic Computing and Communications (ICAC) 2009

  12. Num. of arrivals λlong λshort pl,s Normal Traffic Traffic Surge time pl,l ps,s ps,l Markovian Arrival Process (MAP) • MAPs have ability to provide variability and temporal locality. • We use a class of MAPs with two states only 2 states: λshort > λlong pl,s, ps,l, ps,s, pl,sshape correlation International Conference on Autonomic Computing and Communications (ICAC) 2009

  13. MAP Fitting • Input • Estimated mean service demands at servers: E[Di] • Mean user think timeE[Z] • The pre-defined index of dispersionI • Output • A MAP(2) to draw user think times International Conference on Autonomic Computing and Communications (ICAC) 2009

  14. MAP Fitting (2) Key: determine (λshort,λlong, pl,s, ps,l) • Condition for traffic surge • Condition for normal traffic • Mean think time • We use non-linear optimizer to search for such f and ps,l and find a MAP(2) to best match the predefined I Arrival > Departure the arrival rate is f times higher than the throughput of the system Departure > Arrival the arrival rate is f times slower for balanced system throughput Balancing the height and the width of the burst International Conference on Autonomic Computing and Communications (ICAC) 2009

  15. Realistic values for Burstiness • What is the range of realistic values for defining burstiness via index of dispersion I ? • Exponential: I = SCV = 1 • Bursty: values of thousands, • e.g., FIFA World Cup 1998, one of the servers over 10 days, I = 6300 International Conference on Autonomic Computing and Communications (ICAC) 2009

  16. TPC-W Testbed • On-line bookstore Web site • Testbed: clients + front server + DB server • Constant number of emulated browsers (EBs) • User session • sequence of transaction requests • think time (mean=7 sec) between two transaction requests • 14 transactions types grouped in three mixes: • Browsing mix • Shopping mix • Ordering mix International Conference on Autonomic Computing and Communications (ICAC) 2009

  17. Bursty (I=4000) Non-bursty (I=1) Number of active clients Number of active clients Time (s) Time (s) Validation – Arrival Process • Arrival clients to the system (front server) Shopping Mix Think times drawn by a MAP(2) with I create the bursty conditions. International Conference on Autonomic Computing and Communications (ICAC) 2009

  18. Validation – Utilization Distribution Shopping Mix Bursty (I=4000) Non-bursty (I=1) pdf pdf Front Utilization (%) Utilization (%) pdf DB pdf Utilization (%) Utilization (%) International Conference on Autonomic Computing and Communications (ICAC) 2009

  19. Validation - Average Latency Browsing Mix Shopping Mix Response time (ms) Response time (ms) International Conference on Autonomic Computing and Communications (ICAC) 2009

  20. Validation – Latency Distributions Browsing Mix Shopping Mix 0.83 0.04 1.25 CDF CDF 2.98 Response time (ms) Response time (ms) International Conference on Autonomic Computing and Communications (ICAC) 2009

  21. Conclusion • Burstiness critical for autonomic system design • need representative benchmarks for system evaluation • need reproducible and controllablebursty workloads • Traditional client-server benchmarks ignore burstiness in arrival flows • e.g., TPC-W with exponential think times • Explicitly inject burstiness • a simple and tunable parameter: index of dispersion • can introduce different intensity of traffic surges • http://www.cs.wm.edu/~ningfang/tpcw_codes/ • Supported by NSF grants CNS-0720699 and CCF-08114171 and HPLabs gift. International Conference on Autonomic Computing and Communications (ICAC) 2009