CompSci 296.2
Self-Managing Systems
Shivnath Babu
Today
• Some current work in self-managing systems
 Ideas & resources for projects
• IBM
• ROC (Discussion deferred to next class)
• Our projects at Duke
• HP
2
Project
• Group size <= 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”)
3
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)
5
The past: research goals and
assumptions 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 (“Peres’s 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
8
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
9
Mechanism Projects
• Required/useful mechanisms for self-managing
systems
• Take a goal related to self-managing (e.g., selfoptimization, 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
Clients
WAN
Web
server
Application
servers
Database
servers
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Querying Systems as Data
Clients
WAN
WAN
WAN
Web
server
Application
servers
Database
WAN
servers
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 today’s 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
<1%, 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
D
A
T
A
• Models
–
–
–
–
Analytic performance models
Machine learning models
Rules from system experts
Simulators
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Querying Systems with QueS (30,000 ft)
Data
Maintenance
System
mgmt. Queries
services
Answers
Query
Processor
Modeldriven DB
Engine
D
A
T
A
Data
Acquisition
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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
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