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Request Behavior Variations

Kai Shen University of Rochester. Request Behavior Variations. Background Research Overview. Complex request behaviors in server systems Potentially high concurrency in servers Fine-grained request activities over multiple stages Non-determinism due to multicore resource sharing

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Request Behavior Variations

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  1. Kai Shen University of Rochester Request Behavior Variations ASPLOS 2010

  2. Background Research Overview • Complex request behaviors in server systems • Potentially high concurrency in servers • Fine-grained request activities over multiple stages • Non-determinism due to multicore resource sharing • Goal:Characterize/manage fine-grained request behaviors • Model request performance (Cycles/ins.) and resource usage (to shared cache and memory bandwidth) • Online signature identification and anomaly analysis • Adaptive request scheduling and throttling • Operating system techniques on existing hardware ASPLOS 2010

  3. A Request’s Life • RUBiS: multi-stage online auction • Web server • J2EE application components • Database • A few milliseconds or less at each stage ASPLOS 2010

  4. Request Behavior Variations • Performance obfuscation of multicore resource sharing • Intel 3.0GHz “Woodcrest” processors One core Four cores ASPLOS 2010

  5. Intra-Request Behavior Variations • Fluctuating behaviors over the course of one request execution • Opportunties for signature identification and contention management ASPLOS 2010

  6. Related Work and Challenges • Past work on phase identification/exploitation • Program analysis: [Batson&Madison 76], [Shen et al. 04] • Architecture: [Dhodapkar&Smith 02], [Sherwood et al. 03], [Isci&Martonosi 06] • Challenges for characterizing request behavior variations • High concurrency, frequent net/storage I/Os, and multiple-stage exec. in server applications fine-grained patterns, no long stable phases • OS-level techniques requiring no application changes or special hardware assistance • Online characterization with low overhead, like in some past work ASPLOS 2010

  7. Our Approach • Online request context maintenance [ASPLOS’08] • Tracking request context propagation by tagging messages passed between server stages • Online sampling of hardware event counters • Sample event counters at request switches • Sample at periodic interrupts to capture variation patterns • As frequent as 10 microsecs per sample • Observer’s effect • An interrupt costs ~2300 cycles, triggers 12 references to L2 ASPLOS 2010

  8. System Call-Triggered Sampling • In-kernel call much cheaper than interrupt • Frequent system calls in servers present in-kernel opportunities ASPLOS 2010

  9. Behavior Transition Signals • System calls are part of application semantics • Some may signal impending behavior transitions • Sample at these occasions more cost-effective • Example: Apache web server • Read file from disk  output HTTP headers to net  output file • Output headers involve fragmented memory accesses  poor CPI signaled by a writev system call • How to learn this? • Collect sample system calls and metric changes before/after call • On average, writev system call leads to CPI increase of 3.66 ASPLOS 2010

  10. Modeling: Request Differencing • Quantify the similarity/difference of two requests • Time shifting (due to non-deterministic multicore executions?) • Dynamic time warping (originated in speech recognition) Time shifting ASPLOS 2010

  11. Modeling: Anomaly Detection • Anomalies due to effects of dynamic resource contention • Requests with similar L2 references/ins. but different Cycles/ins. Suspected contention ASPLOS 2010

  12. Utilization: Multicore Scheduling • Contention-easing scheduling • Avoid co-execution of high-resource-usage request periods • Challenges • Require prediction of fine-grained request behavior patterns • Lack of long stable phases • Much shorter than typical CPU scheduling quantum, in millisecs • Results • Able to reduce worst-case contention • Reduce high-resource-usage co-execution at all four cores by 25% • Little effect on average performance ASPLOS 2010

  13. Conclusions • Request characterization helps request-level management • Challenges: high concurrency, fine granularity, multicore obfuscation • Our contributions • Efficient online request behavior sampling • Demonstrate utilizations in request modeling and management • OS-level techniques requiring no application changes or special hardware assistance • Future work: End-to-end request behavior analysis • Correlate with program/application info to answer high-level questions • Request characterization over distributed server systems ASPLOS 2010

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