1. Request Behavior Variations
Kai Shen
University of Rochester
ASPLOS 2010
4/10/2019

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
4/10/2019

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
4/10/2019

4. Request Behavior Variations
Performance obfuscation of multicore resource sharing
Intel 3.0GHz “Woodcrest” processors
One core
Four cores
ASPLOS 2010
4/10/2019

5. Intra-Request Behavior Variations
Fluctuating behaviors over the course of one request execution
Opportunties for signature identification and contention management
ASPLOS 2010
4/10/2019

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
4/10/2019

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
4/10/2019

8. System Call-Triggered Sampling
In-kernel call much cheaper than interrupt
Frequent system calls in servers present in-kernel opportunities
ASPLOS 2010
4/10/2019

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
4/10/2019

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
4/10/2019

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
4/10/2019

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
4/10/2019

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
4/10/2019