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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