Information and Control in Gray-Box SystemsArpaci-Dusseau and Arpaci-DusseauSOSP 18, 2001

John OttoWi06 CS 395/495 Autonomic Computing Systems

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective

What is Gray-Box?

Premise Operating systems cannot be easily modified without

performance risks

Goal Incorporate new, “special application” OS ideas into

systems without modifying the OS itself

Method Using knowledge of OS algorithms, observe the OS

“state” and present an optimized interface for the user (the Information and Control Layer, ICL)

General Capabilities

Applications do not necessarily need to be designed to interface with the ICL

Easy to port—ICLs usually assume an algorithm and perform general tests to determine the OS state.

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective

Gaining Information

Obtain Algorithmic Knowledge Trade-off between generality and optimization

Monitor Outputs Information in “covert channels” implies state

Use Statistical Methods Generate a “system profile” to distinguish normal and

abnormal system performance Use Microbenchmarks

Judiciously conduct performance tests on the system Insert Probes

Probes help obtain, but also modify, the system state

Asserting Control

Exploit algorithmic knowledge to simply achieve a goale.g. prefetching a file

Move the system to a known state Implement feedback systems

Repeated use should optimize the ICLDesign should keep OS in known state

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective

Existing Microbenchmarks

Typically run in a controlled environment Collect static data Time restrictions are not imposed

Hence, they do not offer insight into the unknown state of a system—only static parameters

Existing Gray-Box Systems

Capabilities TCP: diagnose network congestion Implicit Coscheduling: run communicating processes

concurrently MS Manners: optimize resource (CPU) availability for important

processes

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective

Detailed Case Studies

File-Cache Content Detector

Goal Order data accesses to maximize cache hits,

minimize disk accesses Methods

Internal Simulation vs. Inference by Observation Simulation expensive, requires all processes to cooperate

Exploit spatial locality (page loading algorithms) Probing one region of a file can indicate whether that region

of the file is in cache

Limitations Probing small files significantly alters the cache state

of that file

FCCD: Exploiting Spatial Locality

FCCD: Implementation and Interface

Resilient Interface Library: built-in application ICL functionality Command line: orders a list of files passed to

command line tool Implementation

Differentiation between cache hit and miss Sort files/regions of a file by shortest probe access time

Choice of Access Unit size—minimize disk seek time Choice of Prediction Unit size—minimize probe use

Perform a few probes per access unit (prediction unit smaller than access unit)

Select random byte in prediction unit

FCCD: In Action

FCCD: In Action

File Layout Detector and Controller

Goal To ascertain the layout on disk of a set of files

“Gray-Box” Knowledge Most file systems localize contents of a directory on the same set of

disk cylinders Methods

Refresh directory structure Use knowledge of i-node assignment to order file accesses

Implementation1. Call stat() on each file2. Refresh the directory3. Return list of files sorted by i-node

Limitations UNIX-oriented optimization (i-nodes!) Dependence of other running applications on i-node numbers

FLDC: In Action

Memory-based Admission Control

Goal Prevent overuse of memory resources

Methods Measure amount of memory that can be referenced

without causing a page replacement Applications are notified when there is not enough

free memory for an allocation request Limitations

Accuracy limited by page-replacement algorithm Just because the MAC application is “nice” doesn’t

mean that other applications can’t cause thrashing.

MAC: In Action

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective

Gray Toolbox

Microbenchmark results stored in common repository for use by ICLs at system level

Overhead-sensitive operations use system-optimized “plug-in” functionalitye.g. timers

Provide tools for simple statistical calculations

Overview

OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective?

Autonomic Perspective—Observations

Knowledge: In order for an autonomic tool to function well, the state of the system must be well-known. Hence, keeping the system in a known state is an

important objective for autonomic tools. Trust: If a system can provide evidence and

reasons for its actions, a user is more likely to trust the system. A user interface detailing decisions and the

benchmarks leading to an action would be beneficial. Simplicity: Autonomic systems should operate

based on known algorithms; actions would be predictable and explainable.

Information and Control in Gray-Box SystemsArpaci-Dusseau and Arpaci-DusseauSOSP 18, 2001

John OttoWi06 CS 395/495 Autonomic Computing Systems