using trust-aware strategic agents for a self-organising computing grid

Y. Bernard 10.09.2012 Awareness PhD Forum

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Using trust-aware strategic agents for a self-organising computing grid

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Y. Bernard An Evolutionary Approach to Grid Computing Agents 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

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q  New way of dealing with complexity

§  Self-X properties for decentralised solutions

§  Incomplete system information

§  Manage opennes -  Autonomous unknown agents -  Selfish agents -  Malicious agents

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

Agent B

Motivation: Organic Computing system class

Agent C

XAgent A

Agent D

Example: Open Desktop Grid

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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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Application Open Desktop Grid Computing

§  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

Agent B

Agent A

Agent C

Agent D Agent E

Agent F

Agent G

F E

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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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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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Agent types P

erfo

rman

ce

Awareness

Workload +Trust/Rep. +SD.S

High throughput/time Short makespan Decreased waste Decreased replication overhead

+SD.L

à Increasing observation overhead!

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Agent types P

erfo

rman

ce

Awareness

Workload +Trust/Rep. +SD.S +SD.L

Trust-neglecting

agent

Trust-aware agents

Reactive trust-adaptive agents

iTC agent Evolutionary agent

Fixed stereotype agents

Pro-active trust-strategic agents

Tactical agent Adaptive MoO agent eTC agent

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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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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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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

Summary and Outlook

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Thank you for your attention!

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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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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 Workshops. ICDCS ’08. Juni 2008, S. 235 –240.

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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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Trusted Manager O C

Agent

Operational level Observer Current Situation

Controller

Operational Decision S

W

Productive level Observer Internal Situation

Controller Productive Interaction

Submitter

Worker

Strategic level

Observer Long-term Situation

Controller

Strategic Decision S W

SD.S WLTC,

TrustAgents, RepAgents, RepOwn, Fitness

SD.L Predict(WL), Predict(Trust) Predict(Rep)

Pre-selected Behaviour

Behaviour

Constraints

using trust-aware strategic agents for a self-organising computing grid

Education

evolutionary agents

q agents

grid computing agents

egoistq trustadaptive

static agents

subsystems agents

trustaware strategic

q autonomous agents