1 / 16

Hardware Counter Driven On-the-Fly Request Signatures

Hardware Counter Driven On-the-Fly Request Signatures. Kai Shen Ming Zhong Sandhya Dwarkadas Chuanpeng Li Christopher Stewart Xiao Zhang University of Rochester. Motivation. Hardware counters on modern processors:

Download Presentation

Hardware Counter Driven On-the-Fly Request Signatures

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Hardware Counter Driven On-the-Fly Request Signatures Kai Shen Ming Zhong Sandhya Dwarkadas Chuanpeng Li Christopher Stewart Xiao Zhang University of Rochester

  2. Motivation • Hardware counters on modern processors: • instruction mix, rate of execution, branch prediction accuracy, memory access behavior • Operating system utilization of hardware counter metrics • Advantages as fine-grain workload signatures: • application-transparency compared to application statistics • consistent availability compared to OS software statistics • free fine-grain counter maintenance compared to software statistics in general ASPLOS 2008

  3. On-the-Fly Request Signatures • Identifying requests for server workloads • On-the-fly: identify a request while it still executes • Utilizations: • Predicting request properties to guide OS adaptations • Classifying requests on-the-fly to detect anomalies ASPLOS 2008

  4. Challenges • Hardware metrics as workload signatures in server system environments • fluctuating concurrency and frequent context switches ⇒ unstable hardware execution characteristics • requests are fine-grain workload units • Tracking request contexts within the OS • on-the-fly • transparent to applications ASPLOS 2008

  5. Hardware Metrics As Request Signatures:ChoosingNormalization Base • Acquiring stable metrics as request executes: • time-normalized metrics: divide by elapsed CPU cycles • progress-normalized metrics: divide by retired instructions • Finding: • time-normalization for “time duration”-style metrics (e.g., trace cache deliver mode) ASPLOS 2008

  6. Hardware Metrics As Request Signatures:Choosing Effective Metrics • Environmental dynamics: • concurrent request execution in server environments • hardware resource-sharing – multi-threading and multi-core • Example metrics that are significantly affected: ASPLOS 2008

  7. Hardware Metrics As Request Signatures • Metric effectiveness across different applications • inconsistent (e.g., floating-point ops very useful for some but useless for others) ⇒Disappointing result: difficult to find a small set of universally effective metrics • Require application-specific calibration ASPLOS 2008

  8. OS Support of Request Context Tracking • On-the-fly transparent tracking of request contexts • Resource containers [Banga et al.’99] – not application-transparent • Magpie [Barham et al.’04] – not on-the-fly • High-level guidance: • component activities reachable through control or data flows are semantically related, and thus likely part of one request • One case: propagate request context through message passing • tag messages with senders’ request context IDs • handle asynchronous messages, clarify message boundaries in stream-based communications ASPLOS 2008

  9. Example of Request Context Propagation • Multi-tier RUBiS • web server • application components • database • Entirely at the OS • transparent to application ASPLOS 2008

  10. Signature-driven Request Identification • Request identification: • maintain a bank of recent past requests • signature is a vector of metric statistics • match each new request with banked requests on-the-fly • Property inference: • infer the property of new request using the property of matched past request ASPLOS 2008

  11. Prototype • Platform • Linux 2.6.10/Intel Xeon processors with hyper-threading • Overhead (not yet optimized): ASPLOS 2008

  12. Evaluation Results:Accuracy of Predicting Request CPU Time • Comparison base (running average): the average properties of recent past requests to predict future requests ASPLOS 2008

  13. Utilization:Shortest-Job-First Scheduling • 15-27% shorter response time than running average • perform similar to oracle ASPLOS 2008

  14. Utilization:Request Classification and Anomaly Detection • Dots are normal TPC-H requests • Circles are anomalies (SQL injection attacks) • 10-ms cumulative metrics ASPLOS 2008

  15. Related Work • Other uses of hardware counters • phase detection [Dhodapkar&Smith’02, Sherwood et al.’03] • behavior prediction [Duesterwald et al.’03, Bulpin&Pratt’05] • anomaly tracking [Sweeney et al.’04] ⇒we handle challenges due to dynamic server environments • Request characterization using system software metrics • tracking request/response [Aguilera et al.’03] • request modeling [Barham et al.’04] • failure diagnosis [Chen et al.’04] ⇒hardware metrics have unique advantages: consistent availability, free fine-grain counter maintenance First to realize on-the-fly request signatures for server workloads. ASPLOS 2008

  16. Conclusion • Our contributions: • investigate the effectiveness of hardware counter metrics as request signatures in dynamic server environments • propose OS mechanism to support on-the-fly request context tracking and adaptation • demonstrate the effectiveness of request signature-enabled on-the-fly OS exploitations • High-level takeaway: • OS exploitation of hardware metrics to improve performance and dependability [HotOS’07] ASPLOS 2008

More Related