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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).

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Allocation in application layer networks

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)

Problem and objective
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.

Application layer networks aln
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, ….

Aln lifecycle
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

Catallaxy basics
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”


  • 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

Catallaxy properties

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


Dynamic Co-Evolution

Income expectations and price relations stabilize development


Tragedy of commons

Free riding

Catallaxy properties

Catnet properties
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)

Agents state
Agents State

  • Agents genotype:

    • Acquisitiveness

    • Satisfaction

    • Price Step

    • Price Next

    • Weight Memory

    • Reputation

  • For each service:

    • Price Distribution

  • For each negotiation:

    • Negotiation History

Parameters to measure
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.

Experimental simulator
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.

Simulation of alns






In an “abstract” simulator





, ad







node density








A few, powerful

A lot, modest


Simulation of ALNs



  • 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.





Port 101

Port 102

Port 103


  • 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




Generic behaviour on messages

Using generic functions:

- Bargain/RecommendedAction

- Price management

So changing strategies is easy

Particular behaviour on some messages


  • 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

Output 2
Output - 2

(Catallaxy shows development over time)

Soundness of criteria
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


  • 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


  • 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.


  • Any questions?

  • More info on