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Decentralized vs. Centralized Economic Coordination of Resource Allocation in Grids T. Eymann, M. Reinicke Albert-Ludwigs-University, Freiburg (DE) O.

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Presentation on theme: "Decentralized vs. Centralized Economic Coordination of Resource Allocation in Grids T. Eymann, M. Reinicke Albert-Ludwigs-University, Freiburg (DE) O."— Presentation transcript:

1 Decentralized vs. Centralized Economic Coordination of Resource Allocation in Grids T. Eymann, M. Reinicke Albert-Ludwigs-University, Freiburg (DE) O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, L. Navarro, D. Royo Universitat Politècnica de Catalunya, Barcelona (ES) CATNET project – Open Research, Evaluation (3/2002-3/2003)

2 Problem and objective Problem: Provisioning services Requiring (huge amount of) resources From large number of computers CDN, Grid and P2P Objective: evaluation of decentralized mechanism for resource allocation, based on economic paradigm: Catallaxy. (compare against a centralized mechanism using an arbitrator object) Methodology: simulation Network simulator (javasim) + application network (catnet)

3 Resource infrastructures Content distribution networks, Grid Peer-to-Peer Application networks on top, run in multiple resource locations Example: word-processor requiring service for creation of PDF files Client: Look for nearest/cheapest svc. Instance Network: always provide svc, optimize provisioning costs and network communication Service control, resource allocation

4 Service control+resource allocation Decentralized economic coordination Price generation and negotiation Trading resources and services Regulation of supply and demand in large and complex systems Catallaxy

5 Catallaxy Basics (1) Catallaxy is an alternative word for “market economy” (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) “The Market” as a technically decentralized, distributed, dynamic coordination mechanism Adam Smith’s “invisible hand” Hayek’s “spontaneous order” Walras’ “non-tâtonnement process”

6 How does Catallaxy avoid chaos and achieve order? Spontaneous order of the participants  „Unplanned result of individuals' planful actions“ (Hayek) Constitutive Elements of the Catallaxy Access to a Market  Knowledge about scarceness of resources is transported through price information Constitutional Ignorance Self-interest and autonomy of participants Ability to choose between alternative actions Institutions and Evolution "Institutions are frictions which, like frictions in mechanical systems, by restricting movement may make controlled movement possible.” (Loasby 2000, p. 299). Implementation of Norms, Rules, Objects Learning Dynamic Co-Evolution Income expectations and price relations stabilize development

7 Catallactic Information Systems – Internal model Self-Interest Individual goals of the agent can be formalized (e.g. profit maximization) Agent attempts to prognose future world state Actions effect environmental state in order to achieve goals Choice Agent can choose between diverse alternatives Agent can rank alternatives according to prognosed goal approximation Environment is worth-oriented domain (cf. Rosenschein/Zlotkin) Constitutional Ignorance No agent can exactly prognose a future market state („future is blind“) No agent can exactly prognose a „best strategy“ (always historically bound)  You never step twice in the same river (Heraklit) Strategy is sophisticated trial and error procedure at best Requires adaptive and learning strategy Learning procedures are based on subjective past experiences

8 Consequences for Application Development Application must be a Worth-Oriented Domain Application Domain needs common value denominator (money) Requires “money vs. Goods“ exchange However: if the application domain already uses money, it can be directly modelled

9 Consequences for Application Development Agent-based solution is always inferior to analytical optimization Catallaxy is inverse scalable Works better, the larger the network is Information The more information is available, the more accurate are the choices The more agents, the more information exists Computation Computation is fully parallel (no central bottleneck) Solution always exists in the system (no non-allocated resource)

10 What we could expect? Catallaxis good for certain situations: Load balancing Large systems: inherent cost of global/up-to-date state information for resource allocation  where autonomous and decentralized algorithms work well Adaptive to changes: in demand, topology, location and number of resources  evolutionary learning  self-organisation (specially for non-uniform systems with “hot spots”)  Centralized/de-centralized systems may have oscillatory behaviour  “constitutional ignorance”  Centralized: tragedy of state info overload with scale;  Decentralized: tragedy of commons

11 Catallactic Information systems

12 The Catnet network simulator Client: computer program at host, requests service Service Copy: instance of service, hosted in a resource computer Resource: host computer with limited storage and bandwidth

13 We are measuring... Social Welfare: the sum of all utilities over all participants in a given timespan. Utility = Benefit - Cost, basic utility function per participant. Resource Allocation Efficiency (RAE): [Marketing] "fill rate", the ratio of matched transactions divided by the number of all proposals. (#"accepts“/#"proposals“) Comm.Cost= #messages * #hops Response time

14 Our goal: compare baseline/Catallactic Quasi-static Very dynamic Low node density High node density Dynamics: change: %node disconnection time (SC?) Node density: many small nodes / few large nodes SWFResource Allocation Efficiency BW utilization Communication cost Reaction time ~ C BBBB C C C C C C C BC ~ BBBB System /

15 Message Flows (Baseline)

16 Message Flows (Catallactic)

17 Scenarios Appropiate scale: 10th or 100th or 1000th nodes … Change (dynamics): Movement / failure, creation (R) Change of demand (C)  Location of demand (which clients)  Characteristics (many, including temporal distribution) Density: Fragmentation of resource capabilities  Same global amount of resources: P2P  many small PC, CDN  few large servers /

18 Demand From several clients At the same time, at different times Requests with different price/priority Rate: #requests/second  distribution in time, space. Deterministic, random


20 Ongoing work RAE (%) C/B Dynamics Density/#SC: 0/5 1/25 2/75 Network of 105 nodes, 75 Clients, 105 Resources Resource Density/#SC: 0/5, 1/25, 2/75 500 Client requests for service, during 10, 50, 75, 100, 125, 150 seconds

21 Conclusions Initial simulation results prove that a decentralized, economic model works better in certain situations. “Better” is a combination of factors (SWF) Promising: Large scale Dynamic Saturation Resource allocation

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