1. Hardware Counter Driven On-the-Fly Request Signatures
Kai Shen
2/22/2019
Hardware Counter Driven On-the-Fly Request Signatures
Kai Shen Ming Zhong Sandhya Dwarkadas
Chuanpeng Li Christopher Stewart Xiao Zhang
University of Rochester
ASPLOS 2008

2. Motivation Hardware counters on modern processors:
Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

3. On-the-Fly Request Signatures
Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

4. Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

5. Hardware Metrics As Request Signatures: Choosing Normalization Base
Kai Shen
2/22/2019
Hardware Metrics As Request Signatures: Choosing 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)
2/22/2019
ASPLOS 2008
ASPLOS 2008

6. Hardware Metrics As Request Signatures: Choosing Effective Metrics
Kai Shen
2/22/2019
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:
2/22/2019
ASPLOS 2008
ASPLOS 2008

7. Hardware Metrics As Request Signatures
Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

8. OS Support of Request Context Tracking
Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

9. Example of Request Context Propagation
Multi-tier RUBiS
web server
application components
database
Entirely at the OS
transparent to application
2/22/2019
ASPLOS 2008

10. Signature-driven Request Identification
Kai Shen
2/22/2019
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
2/22/2019
ASPLOS 2008
ASPLOS 2008

11. Prototype Platform Overhead (not yet optimized):
Kai Shen
2/22/2019
Prototype
Platform
Linux /Intel Xeon processors with hyper-threading
Overhead (not yet optimized):
2/22/2019
ASPLOS 2008
ASPLOS 2008

12. Evaluation Results: Accuracy of Predicting Request CPU Time
Kai Shen
2/22/2019
Evaluation Results: Accuracy of Predicting Request CPU Time
Comparison base (running average): the average properties of recent past requests to predict future requests
2/22/2019
ASPLOS 2008
ASPLOS 2008

13. Utilization: Shortest-Job-First Scheduling
Kai Shen
2/22/2019
Utilization: Shortest-Job-First Scheduling
15-27% shorter response time than running average
perform similar to oracle
2/22/2019
ASPLOS 2008
ASPLOS 2008

14. Utilization: Request Classification and Anomaly Detection
Kai Shen
2/22/2019
Utilization: Request Classification and Anomaly Detection
Dots are normal TPC-H requests
Circles are anomalies (SQL injection attacks)
10-ms cumulative metrics
2/22/2019
ASPLOS 2008
ASPLOS 2008

15. Related Work Other uses of hardware counters
Kai Shen
2/22/2019
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.
2/22/2019
ASPLOS 2008
ASPLOS 2008

16. Conclusion Our contributions: High-level takeaway:
Kai Shen
2/22/2019
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]
2/22/2019
ASPLOS 2008
ASPLOS 2008