Design And Implementation Of Neural Computing Grid-based Platform For Resource Allocation

Neural networks have inspired many scientists to propose them as a solution for various problems because they have such good properties as parallel functioning, relatively easy implementation of complicated tasks, distributed information storage and learning abilities. Ideally these networks could accomplish an arbitrary task with correct topology and sufficient training. Many models, such as Back Propagation (BP), Hopfield, ART, have been developed and sometimes several models have to be combined to accomplish a task. To relieve the burden of implementing those models from scratch, we developed a neural computation platform (NCP) containing those facilities. The training of a particular neural network involves huge amount of data. To improve the speed of computation, we used the idea of Grid Computing to construct a distributed system.The purpose of this paper is to present an autonomous resource allocation method used in NCP.NCP uses Metacomputing Directory Service (MDS) to discover the resources. MDS provides information services in Globus project. It adapts to the environment which has tremendous resources and services and the resources are distributed owned. MDS is designed to provide a standard mechanism for publishing and discovering resource status and configuration information. It provides a uniform, flexible interface to data collected by lower-level information providers. It has a decentralized structure that allows it to scale, and it can handle static or dynamic data. NCP gets the resource information all the time by the MDS.Traditional resource allocation method requires a unified system and the allocation is carried on by fixed rules. Usually the shortest task execution time is prefered. But in the Grid environment, user actions and resource conditions are very complex, so only pursuiting computation speed can not satisfy all conditions. Instead, quality of service (QoS) is an important aspect to tasks. To accommodate such requirement, a semi-structure data model called Classified Advertisement (Classad) is adopted by NCP. Classad is flexible and extensive, which encapsulates resource queries into the data model. A Classad may contain the following items: attribute, constraint, rank. Constraint exists between computing nodes and tasks. They can both set limits to the other. Rank embodies the definition of QoS from tasks so that users can define different QoS rules under different circumstances. The most outsandingcharacteristic of Classad is that it allows computing nodes to define their own policies. Any tasks conflict with policies will be rejected by those computing nodes.The thesis has five chapters. The first chapter, we introduce the developement of the Neural Network and Grid technology. The second chapter introduces the design and implement of the resource allocation system including the main moduls, mapping alorgithm and the activation of the program. The third chapter introduces how to use the MDS. The fouth chapter introduces the data structure of Classified Advertisement. The fouth chapter demonstrates the example and in the last chapter, we summarize the whole thesis.