View - Universidad de Almería

Proceedings of GO 2005, pp. 141 – 146.A Distributed Global Optimisation Environment for theEuropean Space Agency Internal NetworkDario Izzo and Mihály Csaba MarkótAdvanced Concepts Team, European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands.{Dario.Izzo, Mihaly.Csaba.Markot}@esa.intAbstractKeywords:Global optimisation problems arise daily in almost all operational and managerial phases of a spacemission. Large computing power is often required to solve these kind of problems, together withthe development of algorithms tuned to the particular problem treated. In this paper a generic distributedcomputing environment built for the internal European Space Agency network but adaptableto generic networks is introduced and used to distribute different global optimisation techniques.Differential Evolution, Particle Swarm Optimisation and Monte Carlo Method have beendistributed so far and tested upon different problems to show the functionality of the environment.Support for both simple and multi-objective optimisation has been implemented, and the possibilityof implementing other global optimisation techniques and integrating them into one single globaloptimiser has been left open. The final aim is that of obtaining a distributed global multi-objectiveoptimiser that is able to ‘learn’ and apply the best combination of the available solving strategieswhen tackling a generic “black-box" problem.Distributed computing, idle-processing, global optimisation, differential evolution, particle swarmoptimisation, Monte Carlo methods.1. The Distributed Computing EnvironmentOur distributed computing architecture follows the scheme of a generic server-client model[10]: it consists of a central computer (server) and a number of user computers (clients). Thearchitecture is divided into three layers on both the server and the client side (Figure 1). Thispromotes the modular development of the whole system: for example, the computation layerof the clients (involving the optimisation solver modules) can be developed and maintainedindependently of the other client layers.The main tasks of the server are the following: pre-processing the whole computing (optimisation)problem by disassembling it into subproblems; distributing sets of subproblemsamong the clients; and generating the final result of the computation by assembling solutionsreceived from the clients.On the opposite side, the client computers ask for subproblems, solve them and send backthe results to the server. As with many current distributed applications (employing commonuser desktop machines), our approach is also based on the utilisation of the idle time ofthe clients. More precisely, a client only asks for subproblems when there is no user activitydetected either on the mouse or on the keyboard. In our environment we hide the client computationsbehind a screen saver, similarly to the most widely-known distributed computingproject, the SETI@home project (http://setiathome.ssl.berkeley.edu).As shown in Figure 1, the basic functionalities of the individual layers are the same forboth the client and the server. The uppermost layers are visible to the client users and for theserver administrator, respectively. These layers contain the screen saver (client) and a server