Workshop on Stochasticity in Distributed Systems

Research Article

Matching distributed systems to their environment using dissipative structures

  • @INPROCEEDINGS{10.1109/COLCOM.2005.1651268,
        author={Jim Dowling and Dominik Dahlem and Jan Sacha},
        title={Matching distributed systems to their environment using dissipative structures},
        proceedings={Workshop on Stochasticity in Distributed Systems},
        keywords={Bandwidth  Delay  Distributed computing  Educational institutions  Peer to peer computing  Physics computing  Sampling methods  Search problems  Stochastic processes  Telecommunication traffic},
  • Jim Dowling
    Dominik Dahlem
    Jan Sacha
    Year: 2006
    Matching distributed systems to their environment using dissipative structures
    DOI: 10.1109/COLCOM.2005.1651268
Jim Dowling1,*, Dominik Dahlem1,*, Jan Sacha1,*
  • 1: Distributed Systems Group, Trinity College Dublin
*Contact email:,,


In contrast to a large body of theoretical work on computer systems, distributed systems are not idealised constructions, unconstrained by physical world limitations. They must be designed to account for limiting, real-world properties such as network latency, varying node capabilities, varying application behaviour and unexpected failures. These real-world properties that we describe under the general area of a system's environment have regularities or heterogeneities that can often be modelled as a stochastic process, often using well-known distributions. This paper proposes dissipative structures as a model to capture information about properties of these stochastic processes. In dissipative systems, agents (or nodes) sample information from their local environments and collectively build structures that capture knowledge of recent regularities or heterogeneities in the system's environment. Dissipative structures are a promising technique for transferring knowledge of the system's environment among agents without requiring excessive message passing. This approach offers the promise of building more efficient search algorithms based on reduced uncertainty of the system's environment.