Presentation by Yvonne Bernard at the 2nd Awareness Workshop on Challenges for Achieving Self-awareness in Autonomic Systems @ SASO 2012, Lyon, France

1. Using trust-aware strategic agents for a
self-organising computing grid
Y. Bernard 10.09.2012 Awareness PhD Forum
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2. Outline
q Motivation: Organic Compting system class
q Trust in OC systems
q Application scenario: Trusted Desktop Grid
q Contribution
q Agent types and hierarchy [10]
§ Static agents [3]
§ Trust-adaptive Agents
- iTC Agents [5],[7]
- Evolutionary Agents [9]
§ Strategic Agents [8]
q Adaptive Model of Observation
q Summary and Outlook
Y. Bernard Bernard Evolutionary Approach to Grid Computing Agents
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3. Motivation: Organic Computing system class Example:
q New way of dealing with complexity
Open Desktop Grid
§ Self-X properties for decentralised solutions
§ Incomplete system information Agent D
§ Manage opennes
- Autonomous unknown agents Agent A
X
- Selfish agents Agent C
- Malicious agents
Agent B
q Implications to Trust facettes:
§ Reliability/Functionality: Dynamic structure requires new approaches
§ Security: Privacy and cooperation at the same time
§ Safety: corrections during runtime possible
§ Credibility: analyse environment at runtime
§ Usability: Transparency and predictability not guaranteed
à New class of algorithms necessary
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4. Trust
q Trust := expectation value
§ Probability that a certain event will happen in the future
§ Reputation := Trust from indirect experience
q Trust is a social mechanism, which allows more efficient and effective
cooperation between individuals.
q This mechanism can be transferred into technical systems.
§ Include trust aspect in cooperation decision
q Trust as a constitutional part of technical systems
§ Reduces information uncertainty in open systems (e.g. OC)
§ Enables cooperation between subsystems (agents)
- increase efficiency of cooperating agents
- increase robustness regarding misbehaving agents
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5. Application Open Desktop Grid Computing
Agent A Agent G
Agent B
E F
Agent F
Agent C
Agent E
Agent D
§ Computation on computers from different domains:Open system
§ Free-riders refuse to accept work units.
§ Egoists return wrong/incomplete results.
§ Requires job replication and result checking -> inefficient
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6. Trusted Desktop Grid
q Decentralized system: All agents can
§ offer computing resources (worker) and/or
§ submit work units (submitter).
q Autonomous agents act on behalf of the users.
q Agents have a motivation to cheat.
q Basic Idea: enhance matchmaking with trust information
§ Submitter: Who will be asked to process work units?
§ Worker: Whose work units to accept?
q Goal: Enhance efficiency and robustness using trust and adaptation.
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7. Contribution
q Architecture for trust-adaptive strategic agents
§ Generic model for OC and adaptive systems
- Adaptive to current situation
- Strategic decision making
- Model of Observation
- Institutional contol using constraints
q Implementation of local trust-based adaptive strategy algorithms
§ Submitter strategies
§ Worker strategies
q Evaluate architecture and matchmaking strategies and compare to Related
Work (based on Grid metrics)
§ H-Trust[12]: Trust- and Credibility-Tables
§ Organic Grid[11]: Adaptive Tree Overlay
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8. Agent types
High throughput/time
Short makespan
Decreased waste
Decreased replication overhead
Performance
à Increasing observation overhead!
Workload +Trust/Rep. +SD.S +SD.L
Awareness
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9. Agent types
Pro-active trust-strategic agents
Tactical agent eTC agent Adaptive MoO agent
Reactive trust-adaptive agents
Performance
iTC agent Evolutionary agent
Trust-aware agents
Fixed stereotype agents
Trust-
neglecting
agent
Workload +Trust/Rep. +SD.S +SD.L
Awareness
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10. Agent hierarchy
q Static trust-considering agents:
§ Behaviour prototypes:
Free Rider, Egoist
q Trust-adaptive agents: reactive
§ Adapt parameters to situation
§ iTC Agent
§ Evolutionary Agent
q Trust-strategic agents: proactive
§ Tactical Agent: includes other agents expected behaviour
§ eTC Agent: includes institutional control
§ MoO Agent: Long-term strategic behaviour (access to predictions)
- Aim: find suited information/solution quality relation regarding
overhead à adaptive Model of Observation
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11. Adaptive Model of Observation
q Only evaluate information necessary for the current situation
q Overall goal: Reduce Overhead without sacrificing solution quality
q Types of Overhead
§ Communication:
- Update frequency (e.g. of reputation values)
- How many agents are asked to determine certain values (e.g.
workload)?
§ Calculation/storage:
- Aggregation of values
- Storing values for further evaluation (e.g. Time series analysis for
prediction, relevant for strategic level)
q Adaptive cognition: select observed parameters based on
§ role (submitter, worker) and
§ situation (normal, increased attentiveness, alert)
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12. Summary and Outlook
q Trust can enhance communication, collaboration and negotiation in complex
systems (e.g. OC systems)
q Application scenario Trusted Desktop Grid
q Approaches to trust-adaptive strategic agents [10]
§ Static agents[3]: stereotypes of agent behaviour
§ Trust-adaptive agents
- iTC Agents [5],[7]
§ Efficient and robust
§ Planned: Optimisation using learning techniques (thresholds)
- Evolutionary Agents [9]: first distributed learning approach
§ Strategic Agents
- First approach: Tactical agent[8]
- Planned: eTC and MoO agent:
§ Strategic Level on top of iTC Agents, institutional constraints
§ Strategy based on long-term data and prediction
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13. Thank you for your attention!
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14. Publications
q [1] Martin Hoffmann, Michael Wittke, Yvonne Bernard, Ramin Soleymani, Jörg Hähner, DMCtrac:
Distributed Multi Camera Tracking,
ICDSC 08. Second ACM/IEEE International Conference on
Distributed Smart Cameras, Sept. 2008.
q [2] Sven Tomforde, Martin Hoffmann, Yvonne Bernard, Lukas Klejnowski and Jörg Hähner, "POWEA:
A System for Automated Network Protocol Parameter Optimisation Using Evolutionary
Algorithms",
Beiträge der 39. Jahrestagung der Gesellschaft für Informatik e.V. (GI), 2009,
pp. 3177--3192, Gesellschaft für Informatik e.V. (GI)
q [3] Yvonne Bernard, Lukas Klejnowski, Jörg Hähner, Christian Müller-Schloer, "Towards Trust in
Desktop Grid Systems", ccgrid, pp.637-642, 2010 10th IEEE/ACM International Conference on
Cluster, Cloud and Grid Computing, 2010
q [4] Jan-Philipp Steghöfer, Rolf Kiefhaber, Karin Leichtenstern, Yvonne Bernard, Lukas Klejnowski,
Wolfgang Reif, Theo Ungerer, Elisabeth André, Jörg Hähner, and Christian Müller-Schloer,
"Trustworthy Organic Computing Systems: Challenges and Perspectives", Proceedings of the 7th
International Conference on Autonomic and Trusted Computing (ATC 2010), Springer
q [5] Lukas Klejnowski, Yvonne Bernard, Jörg Hähner and Christian Müller-Schloer, "An architecture for
trust-adaptive agents", Proceedings of the 2010 Fourth IEEE International Conference on Self-
Adaptive and Self-Organizing Systems Workshop (SASOW 2010)
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15. Publications
q [6] Jan-Philipp Steghöfer, Florian Nafz, Wolfgang Reif, Yvonne Bernard, Lukas Klejnowski, Jörg
Hähner and Christian Müller-Schloer, "Formal Analysis of Trusted Communities", Proceedings of
the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Workshop (SASOW 2010)
q [7] Yvonne Bernard, Lukas Klejnowski, Emre Cakar, Jörg Hähner and Christian Müller-Schloer,
"Efficiency and robustness using Trusted Communities in a Trusted Desktop Grid", Proceedings
of the 2011 Fifth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Workshop (SASOW 2011)
q [8] Yvonne Bernard, Lukas Klejnowski, Ronald Becher, Markus Thimm, Jörg Hähner, Christian Müller-
Schloer, "Grid agent cooperation strategies inspired by Game Theory", 4. Workshop Grid-
Technologie für den Entwurf technischer Systeme, Dresden, 21.-22. September 2011, ISSN
1862-622X
q [9] Yvonne Bernard, Lukas Klejnowski, David Bluhm, Jörg Hähner and Christian Müller-Schloer, "An
Evolutionary Approach to Grid Computing Agents", Proc. of the Italian Workshop on Artificial Life
and Evolutionary Computation, 2012 , pp. 1-12, ISBN 978-88-903581-2-8
q [10] Yvonne Bernard, Lukas Klejnowski, Jörg Hähner, and Christian Müller-Schloer, "Self-organising
Trusted Communities of Trust-adaptive Agents", Awareness Magazine 2012,
www.awareness-mag.eu, doi: 10.2417/3201011.004065
q [11] A.J. Chakravarti, G. Baumgartner and M. Lauria. „The organic grid: self-organizing computation on
a peer-to-peer network“. In: IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems
and Humans, 35.3 (Mai 2005), S. 373 –384. issn: 1083-4427. doi: 10.1109/TSMCA.2005.846396.
q [12] Huanyu Zhao and Xiaolin Li. „H-Trust: A Robust and Lightweight Group Reputation System for
Peer-to-Peer Desktop Grid“. In: 28th International Conference on Distributed Computing Systems
Y. Bernard Bernard Evolutionary Approach –240. Computing Agents
Workshops. ICDCS10.09.2012 2008, S. 235 to Grid
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16. Outlook
q Controller
§ Parameter optimisation (learning) on operational level
§ Add long-term strategies (strategy)
- influence operational level
§ Institutional control: Constraints
- Pre-filtering
- Post-filtering
q Observer
§ Adaptive Model of Observation regarding:
- Which parameters are observed?
- Update frequency
- Agents sample size
- Memory size
- Aggregation method (time series analysis, Neural Networks,…)
q Compare trust-strategic agent with related work (H-Trust, Organic Grid)
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17. Trusted Manager O C
Agent Constraints
SD.L
Strategic level
Predict(WL), Observer Controller W
Predict(Trust) Long-term Situation S
Predict(Rep) Strategic Decision
Operational level Pre-selected Behaviour
SD.S Observer Controller W
WLTC, S
TrustAgents, Current Situation Operational Decision
RepAgents,
RepOwn, Productive level Behaviour Worker
Fitness
Observer Controller Submitter
Internal Situation Productive Interaction
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