Information and Control in Gray-Box Systems Arpaci-Dusseau and Arpaci-Dusseau SOSP 18, 2001 John Otto Wi06 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 goal  e.g. prefetching a file Move the system to a known state Implement feedback systems  Repeated use should optimize the ICL  Design 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

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 Implementation 1. Call stat() on each file 2. Refresh the directory 3. 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” functionality  e.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 Systems Arpaci-Dusseau and Arpaci-Dusseau SOSP 18, 2001 John Otto Wi06 CS 395/495 Autonomic Computing Systems