CompSci 296.2 Self-Managing Systems

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CompSci 296.2 Self-Managing Systems

• Shivnath Babu

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Today

• Some current work in self-managing systems
• Ideas resources for projects
• IBM
• ROC (Discussion deferred to next class)
• Our projects at Duke
• HP

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Project

• Group size lt 2
• Identify general topic by end of January, meet
  Shivnath
• Feb 7 Scope problem and give 15-minute talk
• Feb 21 3-minute talk
• March 7 15-minute talk
• March 28 3-minute talk
• April 4/6 15-minute talk
• April 20/24 15-minute final in-class
  presentation ( demo)

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Work on Self-Managing Systems

• IBM
• IBM Journal, Volume 42, Number 1, 2003
• Autonomic computing home page
• IBM autonomic home library, demos
• Autonomic computing toolkit
• IBM Tivoli

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Work on Self-Managing Systems

• Berkeley-Stanford ROC project
• Reading for this class
• Interesting source of project ideas and source
  code
• Sample project reports/presentations (follow the
  CS444A/294-4 link)

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The past research goals andassumptions of last
15 years

• Goal 1 Improve performance
• Goal 2 Improve performance
• Goal 3 Improve cost-performance

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New research goals for a New Century ACME

• Availability
• Changeability
• support rapid deployment of new software, apps,
  UI
• Maintainability
• reduce burden on system administrators
• provide helpful, forgiving SysAdmin environments
• Evolutionary Growth
• allow easy system expansion over time
• Also Security/Privacy

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Recovery-Oriented Computing (ROC) Philosophy

• If a problem has no solution, it may not be a
  problem, but a fact, not to be solved, but to be
  coped with over time
• Shimon Peres (Peress Law)

• People/HW/SW failures are facts, not problems

• Recovery/repair is how we cope with above facts
• Since major Sys Admin job is recovery after
  failure, ROC also helps with maintenance/TCO

ROC focus is on fast repair Vs.old focus on
longer time between failures
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An Example Project in ROC

• Undo functionality for system administrators
  (useful for self-managing components as well)
• To recover from human errors
• To recover from failed operations like software
  upgrades, installs, and configuration updates
• An interesting mechanism project for self-healing

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Mechanism Projects

• Required/useful mechanisms for self-managing
  systems
• Take a goal related to self-managing (e.g.,
  self-optimization, predicting problems), take a
  system (e.g., a database) ? What mechanisms are
  needed? Will current mechanisms suffice?
• Ex Data collection
• nonintrusive, distributed, active probing

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Our Projects at Duke

• Ques Querying Systems (as data)
• Better tools for system administrators and
  self-managing system components
• CoD Cluster on Demand
• Allocate virtual clusters to applications on
  demand

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Querying Systems as Data
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Querying Systems as Data
WAN
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Querying Systems as Data

• What are probable causes of the
  Service-Level-Agreement (SLA) violations rising
  to 12?

Root-cause query
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Queries What if

• Given todays workload, how will average response
  time change if my database fails?
• If I double the memory on my application servers,
  how will SLA violation rate change?

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Queries Let me know

• Let me know if, with 75 probability, average
  response time will exceed 5 seconds in next 30
  minutes
• Prediction
• Continuous query

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Queries What should I do?

• What should I do to reduce SLA violations of
  requests A to lt1, without increasing violations
  of other requests?
• Root-cause What-if

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Querying Systems as Data

• Instrumented traces, logs
• System activity data
• Data from active probing
• Workload
• System configuration data (e.g., buffer size,
  indexes)
• Source code
• Models
• Analytic performance models
• Machine learning models
• Rules from system experts
• Simulators

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Querying Systems with QueS (30,000 ft)
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Challenges Query Complexity

• Support for complex queries
• Rank probable causes of SLA violation rising to
  12?
• What should I do queries
• Queries are ad-hoc
• Queries may be acquisitional

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Challenges Query Specification

• Declarative query language
• Expressibility of language
• Composition
• Snapshot queries and continuous queries

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Challenges Query Processing

• Model-based query processing
• Many types of data sources
• Structured, semi-structured, and unstructured
• Uncertainty in input data
• E.g., legacy systems may have partial/no
  instrumentation
• Imprecise answers
• Answers may include quantification of accuracy
• Ranking

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Challenges Run-time Overhead

• Real-time service for 24x7 systems
• Tunable data acquisition
• Active probing

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Work in Progress

• With Piyush Shivam
• Models for answering queries about expected
  performance given a resource assignment, feasible
  resource assignments to meet SLA, what-if queries
  for scientific applications
• With Songyun Duan
• Use of Bayesian Networks for performance
  prediction and root-cause queries
• With Wanhong Xu
• What-if queries on configuration-parameter
  settings

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Projects at HP Research

• Project 1 Predicting performance problems,
  finding root cases of problems
• Project 2 Debugging complex systems
• Project 3 Designing adaptive systems