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UCC 2013 Keynote Christof Weinhardt: Networked Services - The Meeting Point of Computer Science and Economics

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December 10, 2013

UCC 2013 Keynote Christof Weinhardt: Networked Services - The Meeting Point of Computer Science and Economics

Christof Weinhardt, Professor at the Karlsruhe Service Research Institute (KRSI) of the Karlsruhe Institute of Technology (KIT) talking at the 6th IEEE/ACM International Conference on Utility and Cloud Computing in Dresden, Germany

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December 10, 2013
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  1. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Karlsruhe

    Service Research Institute (KSRI) Prof. Dr. Christof Weinhardt www.kit.edu NETWORKED SERVICES The Meeting Point of Computer Science and Economics 6th IEEE/ACM International Conference on Utility and Cloud Computing UCC Dresden, December 10th, 2013
  2. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu

  3. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Overview

    1 Service Value Networks: Terms and Foundations 2 Market Engineering for Service Value Networks 3 Mechanism Design for Networked Services 4 Conclusions and Outlook
  4. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks
  5. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu From

    Service Mashups to Service Networks Service Oriented Architectures Service Mashups Service Value Networks From monolithic software applications to Service Oriented Architectures (SOA) Modularity is the key to face rising demands for sophisticated, customized services Customers purchase services on demand. Services can be dynamically composed from the offers of different modular providers.
  6. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Mashups Data/API-calls from different sources Service Mashup Goals Information §  Combination §  Visualization §  Aggregation
  7. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Mashups: Critique Data/API-calls from different sources Service Mashup Static Composition: Exchange of services not easily implemented Service Evolution: Mashup not easily adaptable to new technologies, more complex services, etc.
  8. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Fully customized solutions from one provider or intermediary IT as an enabler Providers focus on core competencies Dynamic, automated composition Service Value Networks
  9. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu I

    want to support a business process which comprises of Payment and Billing. Licence-based software? Software as a Service? Service Park, Service Value Network (SVN) Orchestration platform with common, specified standard Adaptability, Specificity? Service Value Networks Specificity through flexible (re-)combination of service modules Service Value Networks are smart business networks that provide business value by performing automated on- demand composition of complex services from a steady, but open pool of complementary as well as substitutive standardized service modules through a universally accessible network orchestration platform.
  10. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Park1 §  An established (web) service community with a common runtime §  Park operators set: §  Rules of Engagement, Participation etc. §  Business Objects §  Pricing and Allocation Mechanisms §  Entrance Fees and Requirements §  Parks are feasible due to their constrained and centralised approach Service Park, Service Value Network (SVN) Orchestration platform with common, specified standard 1: [Petrie and Bussler 2008]
  11. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Evolving

    a Service Value Network What are suitable incentives for attracting and retaining providers in a Service Value Network? What are the key economic institutions in engineering a sustainable platform for the dynamic composition of complex services?
  12. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Requirements

    for Network Formation Network Development Network Formation and Growth Network Administration and Profitability Phase Goals Business Model §  Increase network size and service diversity §  Attract new providers and customers Ensure market sustainability §  Profitable platform §  Participation incentives §  Reward participation in a fair way §  Accept temporary non- budget-balance §  Ensure budget-balance §  Limit effects of missing incentive compatibility
  13. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Dr.

    Simon Caton Market Engineering
  14. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    providers §  Control the services §  Private information §  Preferences §  QoS attributes §  opportunistic Service requester §  Requires complex service §  Willingness to pay §  Preferences on Quality-of-Service (QoS) attributes Platform operator §  Utility maximization over all participants §  Incomplete information §  Preferences §  QoS attributes Engineering Problem(s) à Definition of a social choice function [Hurwicz 1975, Green & Laffont 1979, Myerson & Satterthwaite 1983] Mechanism Design Challenges Information asymmetry and opportunistic behavior M
  15. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Mapping of specific customer requirements I want to support a business process which comprises of Payment and Billing. Billing Payment Price, Quality
  16. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Graph-based formalization Billing Payment
  17. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Graph-based formalization
  18. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Graph-based formalization Scoring function (“fit”) Complex service
  19. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu à

    Complex Service Auction
  20. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Payment

    Billing Complex Service Auction for dynamic allocation (joint work with Benjamin Blau) 20 I am offering my service 4 with a customer satisfaction of 1.0 for a price of… •  … 17 as successor of sevice 1, and … •  … 20 as successor of service 3. I need a complex service consisting of Payment and Billing, and I am willing to pay up to 100 for it.
  21. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Complex

    Service Auction for dynamic allocation UT = 100 (min{0.6,0.7}) – (13 + 16) = 31 UM = 100 (min{0.6,1.0}) – (13 + 17) = 30 UB = 100 (min{0.5,1.0}) – (10 + 20) = 20 Provider of Service 1 gets: Provider of Service 2 gets: Customer utility: Willingness to Pay Allocation Function Transfer Function Legend Aggregation Function Throughput „Critical value“ Truth telling with respect to prices is weeky dominant strategy
  22. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Complex

    Service Auction Compensation and Service Level Enforcement §  Service providers might have an incentive to lie about their services’ configuration §  Idea is to distinguish between a: •  Declaration phase Ex-ante announcements between requester and providers •  Execution phase Ex-post verification of announcements during run-time (e.g. WS monitoring) “The payments need only to be given after the execution. Intuitively we view the execution part as allowing the mechanism to verify in some sense the agents’ declarations, and “punish” them for lying.” [N. Nisan et al. 2001] Compensation term: Maximum utility based on actual realized attribute values of service provider s’s services: Maximum utility based on announced attribute values:
  23. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu The

     Complex  Service  Auction  (CSA)   Ensuring  SLA  Adherence Service  Level  Enforcement Extension  for  CSA  that  enhances  the  transfer  function  by  a  compensation  function,  which •  punishes  service  providers  for  untruthful  announcements  of  QoS  a>ributes  and •  compensates  service  requesters  for  the  utility  loss  they  incur  due  to  resulting  non-­‐‑  or   mal-­‐‑performance Compensation  Function * * * *, ervices  owned  by  n : ( ) ( ) if   ,    allocated : 0 otherwise k ver k ij k j ij S j t p v e t -­‐ Ï Ô = + -­‐ -­‐ -­‐ Ô Ô Ô =Ì Ô Ô Ô Ô Ó Â U U U U Truth-­‐ telling  with   respect  to   all   dimensions   of  the  bid  is   a  weakly   dominant   strategy   Overall  utility  gap  that  results  from  the  utility  difference  based  on   the  announced  a>ribute  values  and  the  verified  ones  measured   after  execution. {
  24. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Complex

    Service Auction (CSA) Extension Abstract model & Mechanism Design Blau et al (2009) Allocation Transfer Consumer surplus Provider surplus Critical value SLA verification Multidimensional incentive compatibility sacrifices budget balance. Hence, system requires constant subsidization! Sustainability? Budget Balance and Sustainability
  25. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Achieving

    budget balance – The Interoperability Transfer Function (ITF) 0,4 0,6 0,8 1 1,2 -­‐‑25% 0% 25% 50% 75% 100% 125% 150% Mean  provider  utility  relative  to   truth-­‐‑telling  utility Bid  deviation  relative  to  truth-­‐‑telling ITF_12_4 ITF_16_4 ITF_32_4 Relative provider utility that deviates from the true valuation dependent on the degree of competition. ITF_{#Provider}_{#Cluster} Approximate incentive compatibility while retaining budget balance! •  Computation of allocation and „critical values“ of service providers •  Computation of approximate values that minimize the weighted sum of all distances to the actual „critical values“ in a budget-balanced manner (Parkes et al. 2001) Approach to Retain Sustainability [Parks et al.2001]
  26. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Co-opetition

    Mechanism for Networked Services
  27. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Network

    Formation NETWORK EFFECTS •  Increasing returns with positive feedback loops •  Co-opetition •  Ecology thought: Participants ultimately share the fate of the network •  è Two-sided Market MODULARIZATION/ SPECIALIZATION •  Participants focus on core competencies •  Leveraging the assets of partners as key to success in today’s dynamic environment •  Providers should be flexibly interchangeable PERMEABILITY •  Low entry barriers through loose coupling •  The network is open to new entrants CUSTOMER-CENTRICITY •  Offered through intermediaries that bundle modular service/product offerings into a complex, holistic package Participants Value Customers Value
  28. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Service

    Value Networks Challenges in networked economies Challenges •  Two-sided markets: Critical mass •  Co-opetition: Cooperative and competitive element in SVNs Co-opetition Mechanism Platform operator Design of a suitable coordination mechanism for SVNs in their launch phase Billing Payment
  29. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu The

    Co-Opetition Mechanism Allocation function Allocation function argmax argmax , :   ( ) : ( ( ) ) l l l l ij l F F ij F F F F i j e E W o p Œ Œ Œ = = ◊ -­‐  U Q A a Willingness to pay w.r.t. complexe service Fl Aggregated price bids of services in the complex service Fl Transfer function Compensation of allocation and the generation of a potential added value / contribution for the SVN
  30. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu The

    Co-Opetition Mechanism Contribution to SVN 2 15 F = U 1 20 F = U 3 25 F = U Contribution •  If v4 fails, two other complex services are ready, which create positive utility for the system •  Compensation of readiness to provide a service •  Power Ratio quantifies the individual contribution of services to the SVN Internal cooperations and value function Network view Internal cooperations of a SVN Value function
  31. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu The

    Co-Opetition Mechanism Allocation and transfer function Approach: Incentivize via monetizing the created added value (Power Ratio). Transfer function (PRTF) tj for all services vj in V Willingness to pay w.r.t. complexe service Fl Allocation function Aggregated price bids of services in the complex service Fl Allocation-based and contribution- based component of transfer function else
  32. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu The

    Co-Opetition Mechanism Allocation and transfer function Transfer function (PRTF) tj for all services vj in V For all cooperations Sm , vj is a part of… … weighted by the probability of Sm to form,… … the power ratio calculates the added value (=marginal contribution) vj is responsible for . [Shapley 1953, Myerson 1977, Jackson and Wolinsky 1996, Jackson 2005] Approach: Incentivize via monetizing the created added value (Power Ratio). Willingness to pay w.r.t. complexe service Fl Allocation function Aggregated price bids of services in the complex service Fl Allocation-based and contribution- based component of transfer function else
  33. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu (R1)

     Budget  Balance (R2)  Individual  Rationality (R3)  Incentive  Compatibility (R4)  Allocative  Efficiency (R5)  Network  Growth (R6)  Readiness (R7)  Fairness (R8)  Interconnectedness (R9)  Service  Sequenz (R10)  QoS  AXributes (R11)  SLA  Enforcement (R12)  Tractability 16.12.13 Classic   mechanism   design Network   design Applicability   in  SVNs =  fulfilled =  parlty        fulfilled =  not  fulfilled/          not  applicable         The  Co-­‐‑Opetition  Mechanism   Properties
  34. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu SUMMARY

    AND OPEN CHALLENGES
  35. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Summary:

    Engineering Service Markets Automated service composition via Web APIs Social Business Network to augment SVN formation Auction mechanisms determine service collaborations Stimulating growth through co-opetition Service Value Networks
  36. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Evolving

    a Service Value Network Attract and retain providers in a Service Value Network? Economic institutions to create a sustainable platform? Networked Mechanism Design Complex Service Auctions
  37. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Evolving

    a Service Value Network Monitorable, fault tolerant and adaptive SVNs? Integrating social business relationships and trusted collaboration? ?
  38. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Dr.

    Simon Caton 238 Million Users from 200 Countries 3 Million Business Pages showcasing 1.2 million products and services 1 billion endorsements
  39. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Can

    someone complement my service? Me Me Social Cloud: Social Network-Based Collaboration and Exchange (Young Investigator Group – Simon Caton) Objective: leverage social networks for the collaborative exchange of resources, services and capabilities Approach: cross section of Computer Science (platform architectures and analysis technologies), Economics (market engineering and incentives) and Sociology (understanding online communities and their intricacies)
  40. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Provisioning

    and consumption of services Matching in Social Network-based Environments Economic and social matching mechanisms to allocate services
  41. Karlsruher Institut für Technologie Karlsruhe Service Research Institute www.ksri.kit.edu Christof

    Weinhardt weinhardt@kit.edu Thank you for your attention!