Billing the Grid – Kick Off Meeting

1. Billing the Grid – Kick Off Meeting

2. Agenda

3. Agenda

4. A Unifying Framework for Behavior-based Trust Models Christian von der Weth, Klemens Böhm Universität Karlsruhe (TH), Germany {weth|boehm}@ipd.uni-karlsruhe.de

5. Motivation • Many fields of research require resource-intensive applications (analysis, simulation, visualization, etc.) • Real driving force: Particle Physics • Solution: Grid Computing • Participants (institutes, firms, persons, etc.) provide their own resources and share them with others • A participant can interact with partners to use their resources to run his own applications • Characteristic of Grid communities • Participants have full control over their entities  A partner can impair the outcome of an interaction by behaving uncooperatively, maliciously or defectively (close access to his resources, limit bandwidth/CPU/…) Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

6. Motivation • Goal: Mechanism that allows entities autonomously to distinguish good from bad partners • Promising approach: Behavior-based trust • Trust: "One's subjective degree of belief that a partner can and will perform a specific task in a certain situation." • Behavior-based: The trust in a partner is derived from the knowledge about his behavior in previous interactions • Basic Idea: • Enabling users to define their own policies whether a partner is trustworthy or not ( trust policies) and • Making these policies explicit to their controlled entities Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

7. Behavior-based Trust Policies • Example policies: • Alice: "I deem a partner trustworthy to use my resources if the average feedback value about him is positive." • Bob: "A partner can have 100% of my idle CPU time if there is no negative feedback about him within the last 24h." • Carol: "I only perform the task of others if their performance of complex tasks was satisfactorily." • Dave: "A partner can have limitless bandwidth if the k most reputable entities recommend him." • Eve: "I share my resources only with the k entities that have the highest PageRank." Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

8. What can we learn from the examples? • Requirement 1: Representation of knowledge that describes the behavior of a partner: behavior-specific knowledge • Different types of behavior-specific knowledge  Feedback, Reputation, Recommendation, Trust • Consideration of various aspects of the behavior-specific knowledge (e.g., context, age of knowledge, etc.) • Requirement 2: Mechanism makes trust policies explicit to controlled entities • Different user have different trust policies • Trust policies may require complex operations (e.g., aggregation or centrality computation) Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

9. What can we learn from the examples? (2) • Representation of knowledge as directed graph G(V,E) • V…set of participants • E…set of edges based on behavior-specific knowledge • Example:  Application of graph algorithms to find trustworthy partners • e.g., EigenTrust (Schlosser et al., 2003), PageRank (Brin and Page, 1996) A Feedback Recommendation Trust B C E D Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

10. Status Quo • Existing behavior-based trust models • Definition of the representation of behavior-based knowledge • Definition of a fixed evaluation scheme to derive the trust in a partner  A fixed evaluation scheme contradicts the subjective nature of trust • Common approach for making trust policies explicit: Logic-based trust policy languages • Definition of rules and clauses to derive the trustworthiness of a partner  Existing languages cannot satisfactorily cope with complex operations required by various behavior-based policies Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

11. A Framework for behavior-based trust models • Aspects of our framework • Relational representation of behavior-specific knowledge • Algebra-based language for the formulation of behavior-based trust policies • Advantages • Supports the definition of arbitrary user-defined trust policies for behavior-based trust models • Including all existing evaluation schemes from literature we are currently aware of • Relational representation allows for a straightforward implementation Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

12. Agenda • Introduction • Representation of behavior-based knowledge • Definition of a query algebra for trust • Preliminary Performance Experiments • Summary & Outlook Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

13. Types of behavior-specific knowledge (1) • Feedback • An entity's (rater) rating of an interaction performed by a partner (ratee) • Alice: "The last download from Bob was very reliable." • Recommendation • An entity's (recommender) opinion about the previous behavior of a partner (recommendee) • Alice: "For downloads I can recommend Bob." Introduction Knowledge Representation • Overview • Aspects • Relational Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

14. Types of behavior-specific knowledge (2) • Reputation • General opinion of the whole network towards a single entity  Global characteristic of an entity • Example: "With regards to downloads, Bob has an excellent reputation." • Trust • An entity's (truster) degree of belief that a partner (trustee) will behave as expected • Alice: "I trust Bob regarding the provision of reliable downloads." Introduction Knowledge Representation • Overview • Aspects • Relational Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

15. Aspects of Behavior-specific Knowledge (1) • Value ∈ [-1,1] • Continuous valuation allows for a finer granularity • Alice: "The performance of Bobs last computation was quite good (~0.6)." • Context • Allows to distinguish between different situations in which two entities can interact • Alice: "Bob provided fast downloads but his CPU performance was very poor." • Facets of a context • Allows to distinguish between different perspectives of a context • Alice: "The connection for the last download was very stable but unfortunately very slow." Introduction Knowledge Representation • Overview • Aspects • Relational Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

16. Aspects of Behavior-specific Knowledge (2) • Timestamp • Allows to emphasize the impact of current knowledge • Alice: "Bobs early downloads were quite fast but recent ones were very slow." • Certainty ∈ [0,1] • Allows to quantify the certainty of an assessment • Alice: "I am absolutely sure (e.g., ~1.0) that Bobs performance according to his last computation was good." • Estimated Effort ∈ [0,1] • Allows to quantify the perceived complexity of an interaction • Alice: "Bob performed simple (e.g., ~0.2) computations quite good but complex ones (e.g., ~0.9) very poor." Introduction Knowledge Representation • Overview • Aspects • Relational Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

17. Relational Representation of Knowledge • Relations that represent behavior-specific knowledge: Feedback, Recommendation, Reputation, Trust (Additional relation: Entity(ID) • Alice: "I am quite sure that the download from Bob was very fast. It was a big file."  New Feedback tuple • In our scenario: • Only Feedback tuples reflect direct experiences • Other knowledge must be derived from feedback (including Trust tuples)  Goal: Trust policy language as mechanism to derive Trust, Recommendation and Reputation tuples Introduction Knowledge Representation • Overview • Aspects • Relational Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

18. Approach to an Algebra-based Policy Language • Source: Relational representation of knowledge • Evaluation of a trust policy = Query on the knowledge base • Common way to deal with relations: Relational Algebra (RA) • Set of operators for the application on relations • Closure property of the operators allows for nesting of the operators to more complex algebra expressions  Basic Idea: Relational Algebra (RA) as basis for our trust policy language Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

19. Example Trust Policy • Informal formulation: • "I trust you (idpartner) in context c and facet fc if your average feedback value from the 10 most reputable entities tops a specific threshold." • Only feedback tuples with a certainty>0.8 should be considered  Algebra expression of that policy: Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook PROJECTION[trusted]( MAP[trusted, (avg_value>threshold)]( GROUP[avg_value, AVG(Feedback.value), {ratee}]( JOIN[Feedback.rater=Reputation.entity]( TOP[10, Reputation.value]( SELECTION[context=c, facet=fc](Reputation) SELECTION[ratee=idpartner, context=c, facet=fc, certainty>0.8](Feedback) ) ) ); Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

20. Algebra-based Policy Language • Observation: • Basic operators of the RA are not sufficient for the formulation of behavior-based trust policies • Extension by means of additional operators are necessary  Clarification which further operators are essential to provide the desired expressiveness • First step: Existing additional operators from literature • Top operator (e.g., Bertino et al., 2004) • Map operator (e.g., Aberer and Fischer, 1995) Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

21. Conventional Extensions to the RA (1) • Top Operator: TOP[k,attr](relation) • returns the k tuples with the highest value of a attribute attr • Example: Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook TOP[3, Value](Reputation) Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

22. Conventional Extensions to the RA (2) • Map Operator: MAP[attr,expression(A1,...,An)](relation) • Allows the execution of user-defined functions over the attributes of a relation • The functions are separately applied to each single tuple of the relation; the results are stored as a new attribute • Example: Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook MAP[Weighted, (Value*Effort)](Feedback) Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

23. Centrality Indices • Centrality index • Graph-based measure to quantify the importance of a vertex according to the graph structure • Different existing measures: Indegree, PageRank, ProximityPrestige, HITS, Integration&Radiality, etc. • Different measures yield different rankings • Example: Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook A 1.0 0.9 B 0.6 1.0 0.2 C 0.2 0.5 0.1 0.9 E D Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

24. An Operator for Centrality Computation • Requirements for a centrality operator: • Flexible specification of the underlying graph  e.g., choice of the weight of an edge: "Value" vs. "Weighted" • Support of various centrality measures within one operator  Definition of centrality operator: • CENTRALITY[attr, Av, As, At, Aw, Measure](Rvertices, Redges) Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

25. Centrality Operator - Example Recommendation Entity A 1.0 0.9 Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook B 0.6 1.0 0.2 C 0.2 0.5 0.1 0.9 E D CENTRALITY[PageRank, ID, Recommender, Recommendee, Value, PageRank] (Entity, Recommendation) Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

26. Centrality Operator • Nature of centrality computation • Very time-consuming and resource-intensive  Centrality computation is the most costly part of the evaluation of a trust policy • Implemented centrality measures in PL/SQL (Oracle 10g) • PageRank, Positional Power Function (eigenvector centrality measures based on power iteration implementation) • Authorities, Proximity Prestige, Integration • Experiments • Efficiency: Performance of our implementations • Quality of Centrality Measures: Comparison of ranking results Introduction Knowledge Representation A Query Algebra for Trust • Basic Idea • Conventional Extensions • Centrality Operator Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

27. Efficiency (1) • Setup: • All centrality measures • Network sizes: 500, 1000, 2000 entities • Measured value: time in sec • Result • Performance varies significantly from measure to measure • Eigenvector centrality measures (based on power iteration implementation) show best performances Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

28. Efficiency (2) • Setup: • Eigenvector centrality measures • Network sizes: 2000, 10000, 50000, 100000 entities • Measured value: time in sec • Result: • Again, huge difference between both measures • Main factor: error threshold of power iteration implementation (causes the number of iteration steps) Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

29. Quality of Centrality Measures • Setup: • All centrality measures • Network size: 1000 entities • Measured value: Difference between two rankings in % • Mean distance between the position of an entity in both rankings • 0%...equal rankings, 100%...maximum difference • Result: • Most measurements yield different rankings (except for Integration and Proximity Prestige) • Choice of centrality measure might influence the result of trust policies significantly Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

30. Summary • What have we done so far? • Collection of various meaningful behavior-based trust policies from literature and our own attempts • Motivation of an algebraic approach for the formulation of behavior-based trust policies • Definition of a relational representation of behavior-specific knowledge • Definition of a query algebra for trust • Listing of necessary operators from literature (basic operators from the RA incl. existing extensions) • Definition of a centrality operator for the computation of various centrality measures • Presentation of some first experimental results Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

31. Open Questions • How efficient is the evaluation of various trust policies? • Further efficiency test including various optimization techniques for centrality computation • Evaluation of trust policies in distributed architectures (i.e., structured Peer-to-Peer systems) • How about effectiveness when entities with different trust policies interact repeatedly? Introduction Knowledge Representation A Query Algebra for Trust Experiments Summary&Outlook Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

32. Thanks for your interest! Questions? Christian von der Weth, Klemens Böhm: "A Unifying Framework for Behavior-based Trust Models"

33. Agenda

34. Virtuelle Währungen als Anreizmechanismus für Grids

35. Reputationsmechanismen • Beispiel für Reputationsmechanismus: eBay • Mechanismen für P2P: EigenTrust, PeerTrust, DMRep • Ziel: bösartiges und egoistisches Verhalten minimieren • Mehr Vertrauen des Käufers in Händler mit guter Reputation • Anreiz für Teilnehmer: Verbesserung der eigenen Reputation und folglich mehr Umsatz • Nachteile: • Erfüllung der Mindestanforderung ausreichend • Kollusion • White washing

36. Monetäre Mechanismen • Leistung ↔ Gegenleistung in Geld • Beschränkung und Kontrolle des Gesamtbudgets im System notwendig • Anreiz für Teilnehmer: Leistung anbieten → Geld verdienen → Leistung erhalten • Preis spiegelt Knappheit wider • Nachteile: • Befürchtung im universitären Bereich: Bessere Ausgangssituation für finanziell gut ausgestattete Institute.

37. Stamp Trading [Nach Moreton und Twigg 2003] Stamp Trading (nach Moreton & Twigg) • Jeder Nutzer in Besitz seiner eigenen, persönlichen Marken • Gleicher Wert für alle Marken (z.B. nur 10€ Scheine) • Zahlung: Handel zwischen Person X und Person Y nur möglich mit Marken • Reputation: Abhängigkeit des Markenwertes von der Anzahl der Einlösung und der Erfüllung der nachgefragten Leistung • Regelung des Markenwertes durch eine zentrale Instanz für Wechselkurse • Bestimmung des Markenwerts durch eine geeignete anreizkompatible Funktion, Bsp: w = m * rs / i Reputationsmechanismen Monetäre Mechanismen

38. Verteilung der Marken

39. Ausblick • Vorteile: • Rückverfolgbarkeit möglich (Dokumentation der Zahlungsflüsse durch zentrale Instanz) • Reputation und Zahlung in einem System (Marken) erfasst • Nachteile: • Zentrale Verwaltung der Wechselkurse notwendig  Nachteil der Skalierbarkeit • Profilerstellung über die Nutzer durch zentrale Verwaltung. • Systemabsturz durch technische (und juristische) Attacken auf die zentrale Einheit • Eingelöste Marken nicht automatisch durch die andere Partei gelöscht  mehrmaliges Benutzen einer Marke (Double spending) • Kollusionen und White washing möglich Entwicklung eines dezentralen Ansatzes für Stamp Trading

40. Agenda

41. Mitarbeiterstruktur A. Ankolekar ??? EKP AIFB Integration in AIFB durch Besuch der Oberseminare Christian v. d. Weth Integration durch … Arun Anandasivam IPD IISM ??? D. Neumann

42. Einordnung der Billing Dienste Billing Dienst 2 (Virtuelle Währungen) Billing Dienst 1 (Reputationsmechanismus) Grid Applikation Common Virtualization Middleware (Globus GT4)

43. Zielsetzung Projektziel: Entwurf und Realisierung einer anreizkompatiblen Billing-Infrastruktur Theorie Praxis

44. Billing the Grid und KIT Adaption und Veränderung Vorhandene Schnittstellen? Reputations- mechanismen RZ FZK (Mickel) RZ Karlsruhe (Juling) Cluster Teilchenphysik CERN? Institut X D-Grid Integrationsprojekt Zeit Ansprechpartner? Pilotprojekt?

45. Meilensteine Meilenstein 1 Meilenstein 2 Meilenstein 3 Meilenstein 4 Berichte Folgeantrag Erste Ergebnisse Alternative Ansätze Feldexperiment Verbesserter Prototyp Anforderungserhebung Literaturrecherche Erster Prototyp 04.08.2006 01.08.2008 01.02.2007 01.08.2007 01.02.2008 Phase „Vorbereitung“ Phase „Forschung und Entwicklung“ Phase „Evaluation“

46. First steps (1/2) Anforderungsanalyse für Anreizmechanismen (AP10) : • Domänenstrukturierung • Erhebung Anreizprobleme • Bösartiges vs. egoistisches Verhalten • Identifikation Wissensressourcen • Ableitung Anforderungen an Anreizmechanismus • Ziele • Lösung der Anreizprobleme • Performanz  • Usability/Sicherheit • … • Funktionale Anforderung • Prozessablauf • Interaktion mit dem Benutzer • … • Grenzen vorhandener Anreizmechanismen • D-Grid Integrationsprojekt • SORMA • Definition geeigneter Metriken

47. First steps (2/2) P2P Netzwerk (AP1) • Konzeption eines strukturierten P2P Netzwerkes • Content Adressable Network • Speicherung von Feedback und anderen Metadaten • Implementierung eines strukturierten P2P Netzwerkes • Roll-Out

48. Organisation • Regelmäßigkeit der internen Reports • Externer Report (Abschlussbericht) Reports Buchung • Institutsintern oder institutsübergreifend? • Intervalle / Zeitpunkte • Treffen aller Beteiligten (2x im Jahr?) • Kleine Treffen (1x pro Woche bzw. Monat) Meetings • Inhalt der Homepage (www.billing-the-grid.org) • Logo PR

49. Anschubfinanzierung • „Landesschwerpunktprogramm erwartet Antragstellung“ • BMBF • EU-Projekt FP7 IST • DFG SPP • DFG Forschergruppe • Welches Ziel wird nach dem Projekt verfolgt? • Ist ein Folgeprojekt erforderlich? • Sorma EU-Projekt FP6 Call5 • Biz2Grid • …

50. Agenda