Hardware Counter Driven On-the-Fly Request Signatures

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

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

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

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Hardware Metrics As Request SignaturesChoosing
Normalization 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)

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Hardware Metrics As Request SignaturesChoosing
Effective Metrics

• Environmental dynamics
• concurrent request execution in server
  environments
• hardware resource-sharing multi-threading and
  multi-core
• Example metrics that are significantly affected

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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

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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

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Example of Request Context Propagation

• Multi-tier RUBiS
• web server
• application components
• database
• Entirely at the OS
• transparent to application

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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

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Prototype

• Platform
• Linux 2.6.10/Intel Xeon processors with
  hyper-threading
• Overhead (not yet optimized)

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Evaluation ResultsAccuracy of Predicting
Request CPU Time

• Comparison base (running average) the average
  properties of recent past requests to predict
  future requests

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UtilizationShortest-Job-First Scheduling

• 15-27 shorter response time than running average
• perform similar to oracle

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UtilizationRequest Classification and Anomaly
Detection

• Dots are normal TPC-H requests
• Circles are anomalies (SQL injection attacks)
• 10-ms cumulative metrics

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Related Work

• Other uses of hardware counters
• phase detection DhodapkarSmith02, Sherwood et
  al.03
• behavior prediction Duesterwald et al.03,
  BulpinPratt05
• 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.
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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 HotOS07