Survey of Validation
Methods in Autonomic
Computing Systems
Ronald Stevens and Brittany Parsons
June 15, 2006
REU sponsored by NSF
Outline of Presentation
1. What is an autonomic computing system?
2. History of autonomic computing
3. Characteristics of ACSs
4. Architecture of ACSs
5. Overview and Evaluation of projects
6. Future Work
7. References
2
1. What is an autonomic
computing system (ACS)?
Autonomic computing is a computing
environment with the ability to manage itself and
dynamically adapt to change in accordance with
business policies and objectives [IBM05]
3
2. History of ACSs
• IBM first introduced the idea of Autonomic
Computing in March 2001.
• The word autonomic was used to describe
this computing because of its biological
origins.
• Autonomic systems of the body handle
involuntary functions that are needed for the
body to live.
4
3. Characteristics of ACSs
- Diagnose and
correct problems
- Automatically
adapts to
change
Self Configuring
Self Healing
Self Optimizing
Self Protecting
- Able to
- Be able to track
anticipate and
changes and act
handle security
accordingly
5
Figure 1 Diagram of Autonomic Computing risks
Characteristics [IBM05]
4. Architecture of an ACS Element
Analyze
Monitor
Plan
Knowledge
Sensors
monitoring - collects
detailed data.
Effectors
Execute
analysis - uses the
data gathered.
planning – comes up
with a plan of action
executing - perform
desired actions
Managed Element
Figure 2 Autonomic Manager [KC03]
6
5. Overview and Evaluation of
projects
•
•
•
•
Impala
OceanStore
Model-Driven Autonomic Manager
Bison
7
Evaluation of projects
• We evaluated each of the four projects
based on the criteria and a scale that
we came up with for Autonomic
Computing Systems.
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Evaluation criteria for ACSs
• QoS – How well systems perform required tasks
• Cost – cost effectiveness
• Adaptivity – ability to adapt
• Fault Tolerance – ability to deal with problems
• Fault Avoidance – ability to avoid problems
• Fault Detection – ability to find problems
• Degree of Autonomy – amount of human input needed
• Granularity – flexibility of system
• Availability of Source Code
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Scale for Criteria Evaluation
Rating
Description
1
This system does not fit the criteria.
2
This system may possibly fit some part of the criteria.
3
This system has characteristics of the criteria.
4
This system is close to fitting the criteria.
5
This system fits the criteria.
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Impala
• Project Name: Impala
• Organization: Princeton University
• Research Team: Margaret Martonosi (Lead), Steve
Lyon, Li-Shiuan Peh, Vince Poor, Dan Rubenstein,
Chris Sadler, Philo Juang, Ting Liu, Yong Wang, Pei
Zhang
• Purpose of Project: ZebraNet will create a wireless
ad-hoc network capable of tracking the movements of
wild zebras via tracking collars. [MLP+06] Impala is
the middleware that allows each zebra’s collar to act
as a node in a wireless network.
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Impala
Adaptivity
Cost
Quality of Service
Granularity
Fault Avoidance
Fault Tolerance
Fault Detection
Degree of Autonomy
Availability of Source Code
Self-Healing
1
1
1
1
1
1
1
1
Self-Protecting
1
1
1
1
1
1
1
1
Self-Configuring Self Optomizing
4
4
1
1
3
3
3
3
4
2
4
2
4
1
5
4
no
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OceanStore
• Project Name: OceanStore
• Organization: UC Berekley
• Research Team: John Kubiatowicz(Lead),
Byung-Gon Chun,Steven Czerwinski, Patrick
Eaton
• Purpose of Project: OceanStore is designed to
be a massively scalable storage system.
OceanStore employs promiscuous caching and
cryptography to ensure that data will not be lost
or compromised. [KGC06]
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OceanStore
Adaptivity
Cost
Quality of Service
Granularity
Fault Avoidance
Fault Tolerance
Fault Detection
Degree of Autonomy
Availability of Source Code
Self-Healing
1
1
1
1
1
1
1
1
Self-Protecting
4
1
4
3
5
5
3
4
Self-Configuring Self Optomizing
3
1
1
1
5
1
3
1
5
1
5
1
2
1
4
1
yes
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Model-Driven Autonomic Manager
• Project Name: Model Driven Autonomic
Manager
• Organization: Indiana University
• Research Team: Dr. Yuashun Dai (Lead)
• Purpose of Project: The purpose of this project
is to develop a model driven autonomic
manager. This project focuses on autonomic
traits in networks.[DAI06]
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Model Driven Autonomic
Manager
Adaptivity
Cost
Quality of Service
Granularity
Fault Avoidance
Fault Tolerance
Fault Detection
Degree of Autonomy
Availability of Source Code
Self-Healing
5
1
1
5
2
4
4
5
Self-Protecting
5
1
1
5
2
4
4
4
Self-Configuring Self Optomizing
5
5
1
1
1
1
4
5
2
2
4
4
4
4
5
5
no
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Bison
• Project Name: The Bison Project
• Organization: Future & Emerging Technologies
initiative of the Information Society Technologies
Programme of the European Commission.
• Research Team: The leads for their resepective
universities are Ozalp Babaoglu, Geoff Canwright,
Luca Maria Gambardella, and Andreas Deutsch.
• Purpose of Project: The Bison Project stands for
Biology-Inspired techniques for Self-Organization in
dynamic Networks. This project studying biological
systems to improve Peer to Peer networks. [DDG05]
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The Bison Project
Adaptivity
Cost
Quality of Service
Granularity
Fault Avoidance
Fault Tolerance
Fault Detection
Degree of Autonomy
Availability of Source Code
Self-Healing
5
1
3
5
5
5
5
5
Self-Protecting
5
1
3
5
5
5
5
5
Self-Configuring Self Optomizing
5
5
1
1
3
3
5
5
4
5
4
5
5
4
4
5
yes
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6. Future work
•
Validating autonomic characteristics in a system.
1.
2.
3.
4.
5.
6.
7.
Project in conjunction with graduate students in the Software
Testing Research Group.
Identify a system (X) with a test history (test cases and actual
output)
Use the IBM Autonomic Toolkit to log the state of X during
execution (Overview of the IBM Autonomic Toolkit)
Introduce testing concept.
Investigate if the logging facility provided by IBM Autonomic
Toolkit (IAT) allows for the validation of the X using the test
history.
Simulate a change for an autonomic characteristic e.g., self
configuration, then use the logs generated by the IAT and the
test history of X to check if the behavior of X can be validated.
To accomplish the above we will be learning how to use two
automated testing tools – JUnit and Rationla Functional Tester.
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7. References
[IBM05]
IBM Corporation. IBM, 2005. An architectural blueprint for
autonomic computing. June 2005.
[KC03]
J. Kephart and D. Chess. The vision of autonomic computing.
Computer, vol. 36, no.1, pp. 41-50, January 2003.
[MH04]
J. McCann and M. Huebscher. Evaluation issues in autonomic
computing. International Workshop
on Agents and Autonomic Computing and Grid Enabled
Virtual Organizations (AACGEVO04),
Wuhan, China 2004
[MLP+06]
ZebraNet. http://www.princeton.edu/~mrm/zebranet.html
[KGC06]
Ocean Store.
http://oceanstore.cs.berkeley.edu/info/overview.html
[DAI06]
Y. Dai. Model-Driven Autonomic Management. 2006
http://www.cs.iupui.edu/~ydai/AMSCS/MAM.htm
[DDG05]
G. Di Caro, F. Ducatelle, and L.M. Gambardella. Project
description: BISON: Biology-Inspired techniques for SelfOrganization in dynamic Networks.
Zeitschrift Knstliche Intelligenz, Special Issue on Swarm
Intelligence, November 2005.
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