1 / 24

Allocation in Application Layer Networks

Exploring Decentralized Resource. Allocation in Application Layer Networks. 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).

salene
Télécharger la présentation

Allocation in Application Layer Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Exploring Decentralized Resource Allocation in Application Layer Networks T. Eymann, M. ReinickeAlbert-Ludwigs-University, Freiburg (DE) O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, L. Navarro, D. RoyoUniversitat 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) • A concrete case for an application is, for instance, the distributed provisioning of web services for Adobe’s Acrobat (for creating PDF files) in an Akamai-like application layer network.

  3. Application Layer Networks (ALN) • Application layer networks are software architectures that allow the provisioning of services requiring a huge amount of resources by connecting large numbers of individual computers. They are built over a base network that is used to support this second network, “layered” upon the underlying infrastructure. • Motivation: • ALN have dynamic demands • Deployment/Allocation Requirements: • Programable Infrastructure: • Nodes with BW, Storage & Processing Resources. • Deployment/Allocation Mechanisms: • Resource Allocation Algorithm, ….

  4. ALN Lifecycle • Phases: • Deployment: initial positioning of resources. Deployment can also be economically modeled, although we treat as if done. • Allocation: main focus here. • Allocates resources for the demands. • Changes resource locations: • Migrate • Clone

  5. Catallaxy Basics • 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. Catallaxy • Coordination mechanism for systems consisting of autonomous decentralized devices. • Based on constant negotiation and price signaling • Based on efforts from both agent technology and economics • Agents are able to adapt their strategies using machine learning mechanisms • Evolution of software agent strategies, a stabilization of prices throughout the system and self-regulating coordination patterns • Earlier work has used economic principles for resource allocation in distributed computer systems, but most of these approaches rely on using a centralized auctioneer

  7. Spontaneous order of the participants „Unplanned result of individuals' planful actions“ (Hayek) Constitutive Elements of the Catallaxy Access to a Market Knowledge about availability of resources is transported through price information Constitutional Ignorance Self-interest and autonomy of participants Ability to choose between alternative actions Learning Dynamic Co-Evolution Income expectations and price relations stabilize development Problems Tragedy of commons Free riding Catallaxy properties

  8. Catnet Properties • Agent-based solution is always inferior to analytical optimization • 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)

  9. Agents State • Agents genotype: • Acquisitiveness • Satisfaction • Price Step • Price Next • Weight Memory • Reputation • For each service: • Price Distribution • For each negotiation: • Negotiation History

  10. Parameters to measure • Social Welfare (SWF): • Sum of all utilities over all participants, in a given timespan • Clients subjectively value SC access • Prices change due to “supply and demand” • Individual utility = transaction price – market value • Also: Response Time (REST), Resource allocation efficiency (RAE), Communication cost (CC), Client-Resource assignment distance.

  11. Experimental Simulator • Abstracts from a concrete application and implementation. • Allows „plug-in“ of different „middleware“ resource allocation mechanisms. • Allows easy changes of • Decentralized agent strategies • Centralized allocation mechanisms.

  12. Changing node dynamics high networks In an “abstract” simulator , P2P hoc - , ad overloaded Mobile medium CDN networks GRID node density Fixed low medium high CDN P2P Stable A few, powerful A lot, modest GRID Simulation of ALNs ALN

  13. Javasim • The Catnet simulator is build over JavaSim, JavaSim is a network simulator based in autonomous components. • Javasim models almost every aspect of a real network: latency, bandwith, lost packets, routing, … • It has some of the more common internet protocols like DV, TCP, UDP, … • So our components can be easily modified to work in the real world changing the middleware to real sockets.

  14. R C SC Port 101 Port 102 Port 103 Components • On top of the physical nodes, a number of different software agents are created, which form the application layer network: • Client (C): computer program at host, requests service • Service Copy (SC): instance of service, hosted in a resource computer • Resource (R): host computer with limited storage and bandwidth • Independent on each other at javasim level • Running as programs with a socket on a computer • Configuration made at startup script UDP IP

  15. Catallactic Message Flow

  16. Components Generic behaviour on messages Using generic functions: - Bargain/RecommendedAction - Price management So changing strategies is easy Particular behaviour on some messages

  17. Configuration • We use TCl to set-up the experiments: • Topology • Node configuration: wich components (C/R/SC/MSC) should be on each node. • Application Layer Network initialitzation • Agent parameters: bandwith, price ranges, money balance, genotype, … • Current experiment parameters

  18. Output - 1

  19. Output - 2 (Catallaxy shows development over time)

  20. Output - 3

  21. Soundness of Criteria • Interdepencies • SWF and RAE are dependent • Every transaction adds to SWF • More transactions add to RAE • SWF and CC are dependent • Higher CC lowers SWF • SWF and REST are dependent • Higher REST means more transactions • More transactions add to RAE and SWF • SWF captures all costs and revenues • Dependencies are an emergent feature of the system • No direct links have been implemented: economic reasoning works „bottom-up“ in an ACE sense

  22. 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

  23. Future • Future research work: • Agent technology layer • Application-specific layer • Both are linked in a feedback loop. • Also: • A lot of influencing parameters apart from Density and Dynamism, not fully evaluated due to time constraints.

  24. END • Any questions? • More info on http://research.ac.upc.es/catnet/

More Related