information and control in gray-box systems
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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?. - PowerPoint PPT PresentationTRANSCRIPT
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Information and Control in Gray-Box SystemsArpaci-Dusseau and Arpaci-DusseauSOSP 18, 2001
John OttoWi06 CS 395/495 Autonomic Computing Systems
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective
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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)
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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.
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective
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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
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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
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective
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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
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Existing Gray-Box Systems
Capabilities TCP: diagnose network congestion Implicit Coscheduling: run communicating processes
concurrently MS Manners: optimize resource (CPU) availability for important
processes
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective
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Detailed Case Studies
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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
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FCCD: Exploiting Spatial Locality
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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
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FCCD: In Action
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FCCD: In Action
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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
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FLDC: In Action
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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.
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MAC: In Action
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective
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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
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Overview
OS and Gray-Box Advantages Techniques Previous Approaches Case-Studies Gray Toolbox Autonomic Perspective?
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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.
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Information and Control in Gray-Box SystemsArpaci-Dusseau and Arpaci-DusseauSOSP 18, 2001
John OttoWi06 CS 395/495 Autonomic Computing Systems