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Catnet Project Review

Catnet Project Review. Project participants: T. Eymann, M. Reinicke Albert-Ludwigs-University, Freiburg (D) O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, L. Navarro, D. Royo Technical University of Catalonia, Barcelona (ES) IST-FET-Open Assessment project: 26 mm, 100K€

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Catnet Project Review

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  1. Catnet Project Review • Project participants: • T. Eymann, M. ReinickeAlbert-Ludwigs-University, Freiburg (D) • O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, L. Navarro, D. RoyoTechnical University of Catalonia, Barcelona (ES) • IST-FET-Open Assessment project: • 26 mm, 100K€ • Contract no.: ITS-2001-34030 • 1 year: March 2002 - 2003

  2. Review Agenda • 1. Motivation • 2. ALN Background • 3. Economic background • 4. Overview of the Work done • 5. Simulation overview • 6. Simulator • 7. Methodology • 8. Experiments • 9. Conclusions

  3. Review objectives • Present work we have done during this assessment project • Get scientific recommendations for future work • Get support for submitting a full proposal

  4. Motivation CatNet Review Meeting Barcelona, April 25th, 2003

  5. The future heads to Utility computing: • On-demand computing and services • require dynamically/real-time/on-the-fly provisioned resources • Demand cannot be planned in advance • A complex and large market of services and resources. • Convergence of Grid, P2P and Web Services

  6. A Future Application Scenario How to match clients and services in a utility network? Acrobat PDF converter service providers (MS Word) service clients ?

  7. Centralized vs. Decentralized Approaches

  8. Centralized Approaches • Employ a centralized coordinator, who matches clients and service providers according to some optimization rule • Examples • Globus and the Nimrod-G extension • (simulation possible using GridSim) • Global coordination explicitly achieved by coordinator

  9. (Buyya 2003, p.4)

  10. Cast of Characters With centralized message flow MSC Client Application SC SC SC SC Request service Resource Resource Resource Resource Middleware Node Node Node Node Node Node Network Layer

  11. Decentralized Approaches • Clients select service provider from received responses according to some local optimization rule • Examples • Peer-to-Peer Networks, e.g. Gnutella • Optimization rule applied by human user • Catallaxy approach: Local economic optimization leads to global coordination

  12. Cast of Characters With decentralized message flow Client Application SC SC SC SC Request service Resource Resource Resource Resource Middleware Node Node Node Node Node Node Network Layer

  13. Goal of the Assessment Project • Compare performance of centralized vs. decentralized approach by simulation • Using an easy simulator implementation • Using a hypotheses-based framework • Check feasibility of „Full Project“ proposal • If decentralized approach outperforms centralized approaches • for creating generic „Catallactic“ middleware for Application Layer Networks

  14. ALN background CatNet Review Meeting Barcelona, April 25th, 2003

  15. ALN Background • Aplication Layer Networks • Programmable Infrastructures for ALN • Resource Allocation Problem • Related work, Resource Allocation in Grids and P2P

  16. S S S S S S S S S S D D S S S S S D S D S S D S S S S S S S Application Layer Networks

  17. ALN deployment on a Programmable Infrastructure • Motivation: • ALN have dynamic demands -> Need to be deployed to adapt to changes. • Deployment Requirements: • Programable Infrastructure: • Nodes with BW, Storage & Processing Resources. • Deployment Mechanisms: • Resource Allocation Algorithm, ….

  18. S S S S S S S S S S D D S S S S S D S D S S D S S S S S S S Application Deployment Example • Web Proxy Caching Hierarchy: • 6 servers each requires 1 Mbits net capacity, 200 Mbytes Storage, Demand Regions: A,B,C,D,E Resource Allocation Algorithm • Programmable Infrastructure: • 30 nodes each 10 Mbit net capacity, 2 GByte Storage

  19. Resource Allocation Problems • Centralized RA is computationally intensive (and a single point of failure). • And it will get worse: • Very Dynamic Infrastructures (Nodes come and go frequently): dial up nodes, mobile nodes, ... • High Node Density Infrastructures (Many nodes with little resources): pervasive computing,.. • Solution Requirement: • Decentralized autonomous RA.

  20. Related Work: Resource Allocation in Grids Grids are programmable infrastructures for Computationally intensive apps. (demand CPU resources). Grids RA: • Condor-G, DMR-broker: • centralized heuristic based dispachers. • Nimrod-G Broker: • centralized budget constraint sheduler. • DataGrid OptorSim: • centralized economic based optimizer.

  21. Related Work: Resource Allocation in P2P systems P2P - Grid: • [Foster et al, IPTP03]: P2P are Grids with thousands of resource nodes, but only 1 service: file transfer, datamining, ... P2P RA: • [Gu et al, HPDC 2002]: simulated n-hop service aggregation in P2P

  22. Economic Background: the Catallaxy CatNet Review Meeting Barcelona, April 25th, 2003

  23. Observations on Decentralized Networks Application Technology Grid Nodes Mobile Devices Smart Chips ResourceNetworks Ubiquitous Computing Networked Household Appliances

  24. Observations on Decentralized Networks Common Properties (1) Application Cooperation Communication Application Services Individually owned (mobile) autonomous devices with access to open, decentralized networks Network Services Physical Services

  25. Observations on Decentralized Networks Common Properties (2) Application Cooperation Messaging using SOAP instead of RMI Communication Individually owned (mobile) autonomous devices with access to open, decentralized networks Application Services Network Services Physical Services

  26. Observations on Decentralized Networks Common Properties (3) Application Cooperation Exchanging Property Rights for Utility Maximization Communication Negotiation/Messaging vs. Commands/Method Invocation Application Services Individually owned (mobile) autonomous devices with access to open, decentralized networks Network Services Physical Services

  27. Observations on Decentralized Networks Selected Application Domains P2P Storage & Collaboration MobileCommerce Service Webs Application Cooperation Exchanging Property Rights for Utility Maximization Communication Negotiation/Messaging vs. Commands/Method Invocation Application Services Individually owned (mobile) autonomous devices with access to open, decentralized networks Network Services Physical Services

  28. Observations on Decentralized Networks Implementing Coordination (1) Application Economic Processes, e.g. Distributed Resource Allocation, Multi-Commodity Flow Problems ? Cooperation Exchanging Property Rights for Utility Maximization Communication Negotiation/Messaging vs. Commands/Method Invocation Application Services Individually owned (mobile) autonomous devices with access to open, decentralized networks Network Services Physical Services

  29. Observations on Decentralized Networks Implementing Coordination (2) Application Economic Processes, e.g. Distributed Resource Allocation, Multi-Commodity Flow Problems A mechanism to resolve interdependencies between participants sharing resources Coordination Cooperation Exchanging Property Rights for Utility Maximization Communication Negotiation/Messaging vs. Commands/Method Invocation Application Services Individually owned (mobile) autonomous devices with access to open, decentralized networks Network Services Physical Services

  30. Implementing Markets • A market is a mechanism to resolve interdependencies between participants and to allocate resources in economic theory • Market as the abstract point of matching supply and demand, as opposed to “marketplace” or “market platform” • Result is market clearance, supply and demand are satisfied • Evaluation Criteria • Utility of all participants is maximized: Social Welfare Utility • No participant can get a better result without another losing utility: Pareto Optimum • But the market mechanism is not fully understood, so how to implement it in a technical environment? • Computable General Equilibrium (“top-down”) • Rooted in Neo-Classical Theory, Walras “tâtonnement process” • Agent-Based Computational Economics (“bottom-up”) • Neo-Austrian Economics, Evolutionary Economics, Adam Smith’s “invisible hand”, Hayek’s “spontaneous order”, Walras’ “non-tâtonnement process”

  31. How to implement a “market”? (1) Economic Concept The „market“ as a decentralized, dynamic coordination mechanism just distribution of utility by a central arbitrator direct agreement between negotiating agents decentralized action of utility-maximing agents using a central auctioneer WALRAS‘ian Auctioneer Technical Implementation Multiagent systems

  32. How to implement a “market”? (2) Economic Concept The „market“ as a decentralized, dynamic coordination mechanism direct agreement between negotiating agents decentralized action of utility-maximing agents using a central auctioneer WALRAS‘ian Auctioneer MISES/HAYEK‘s Catallaxy Market-Oriented Programming Technical Implementation Nimrod-G Multiagent systems

  33. How to implement a “market”? (3) Economic Concept The „market“ as a decentralized, dynamic coordination mechanism WALRAS‘ian Auctioneer MISES/HAYEK‘s Catallaxy Market-Oriented Programming Catallactic Information Systems Technical Implementation Multiagent systems

  34. Catallaxy • Catallaxy is an alternative word for „market economy“ (coined by Mises and Von Hayek of the Neo-austrian economic school) • “Fundamentally, in a system in which the knowledge of the relevant facts is dispersed among many people, prices can act to co-ordinate the separate actions of different people in the same way as subjective values help the individual to co-ordinate the parts of his plan.” (Friedrich A. von Hayek, The Use of Knowledge in Society, 1945) • An economic metaphor for complex adaptive systems (CAS) • Coordination and a stable environment are emergent features of the market • Pursuing local goals alone already stabilizes and coordinates the system • Economist Research: Agent-Based Computational Economics

  35. Catallaxy Characteristics • Software agents act selfish, because their human owners do: Competition is the norm. • Software agents keep their utility function private: If made public, the agent can be exploited. • Software agents communicate directly: Centralized control institutions can always be bypassed. • Cooperation is always pareto-eliciting (increases utility of all participants) • No free lunch: everyone has a utility function (business model), even centralized institutions • Information is not free or public (every participant operates on private knowledge and subjective private values) • Utility is the difference between transaction price and private value

  36. Example: CatNet • Clients negotiate with Service Copies (SC) • Goal of Client is to buy service access for the lowest price • Goal of SC is to sell service access for the highest price Client Application SC SC SC SC Request service Resource Resource Resource Resource Middleware Node Node Node Node Node Node Network Layer

  37. The Negotiation Protocol and the Goal Function Application Goal Function: maximize the spread between input and output prices Coordination Negotiation Protocol: Monotonic Concession Protocol, based on Alternating Offers between Buyer and Seller Cooperation Communication Application Services Network Services Physical Services

  38. Principles of Software Agents Reasoning, e.g. calculation of a counter-offer using heuristics (may become arbitrarily complex, e.g. AI) Agent Effector, e.g. sent offers (Intention: increase own utility) Sensor, e.g. received offers Environment, e.g. Market

  39. Negotiation Protocol - Example Client SC Buyer Seller cfp (service access) propose (service access, pS=$24) propose (service access, pB=$18) propose (service access, pS=$21) accept-offer(service access, pB=$21) commit (service access, pS=$21) time time

  40. Heuristic-Adaptive Reasoning:Parameters Concession Probability Application Concession Amount Mark-up Continuation Probability Market Price Learning Weight Coordination Negotiation Strategy: Achieving utility maximization setting e.g. concession rate, concession amount, time pressure in relation to market (and the transaction partner). Cooperation Communication Application Services Network Services Physical Services

  41. Heuristic-Adaptive Reasoning:Example for a Seller (1) propose (service access, pB=$18) propose (service access, pS=$24) Update Market Price Valuation

  42. Heuristic-Adaptive Reasoning:Example for a Seller (2) propose (service access, pB=$18) propose (service access, pS=$24) Should I leave the negotiation?

  43. Heuristic-Adaptive Reasoning:Example for a Seller (3) propose (service access, pB=$18) propose (service access, pS=$24) Should I leave the negotiation? Yes reject No Should I make a concession?

  44. Heuristic-Adaptive Reasoning:Example for a Seller (4) propose (service access, pB=$18) propose (service access, pS=$24) Should I leave the negotiation? Yes reject No Should I make a concession? No propose (service access, pS=$24) Yes What amount should I concede?

  45. Heuristic-Adaptive Reasoning:Example for a Seller (5) propose (service access, pB=$18) propose (service access, pS=$24) Should I leave the negotiation? Yes reject No Should I make a concession? No propose (service access, pS=$24) Yes propose (service access, pS=$21) „costs of life“ (tax) will be deducted in discrete time slots

  46. Heuristic-Adaptive Reasoning: adaptation by evolutionary learning  Send „plumage“ (profitx, Genotypex)  select Genotype (profitx)  Create agent (Genotype  Genotype1)

  47. Preliminary Results • Catallaxy works in a small scale for a multiagent system simulation (B2B-OS) • (Demonstration possible if time permits) • Coordination can be achieved emergently without a central coordinator (auctioneer) if the software agents • reason in an economical sense about alternative actions • implement feedback learning and adapt reasoning and price-setting strategies • Further research from here • Evaluate approach in different application domains and network technologies ( CatNet) • Construct Institutions to control and secure open multiagent system environments ( reputation tracking, emergent norms) • Formalize approach ( Agent-Based Computational Economics) • Optimize heuristics and learning mechanisms

  48. Hypotheses on using Catallaxy • Catallaxy should • Be more scalable than a centralized coordinator • Because all computation is decentralized • Be more flexible • Because all actions are based on local knowledge only • Because agents are able to adapt strategies • Use more bandwidth • Because negotiations need more communication • Grow better results over time • Because agents begin with inferior results and then adapt • Optimal results may only be reachable in static scenarios

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