High level QoS-driven model for Grid applications in a simulated environment

This paper presents a model for high-level Quality of Service (QoS) maintenance within business-context domains and associated simulation results achieved via an expansion of the GridSim toolkit. Grid Computing traditionally has been linked with scientific environments, where heterogeneous resources were networked and employed for carrying out compute-intensive and data-intensive scientific experiments or applications that may have not been possible before. The natural progression is that business-oriented applications will build on this success and utilise the large number of heterogeneous Grid resources, potentially available. The success of introducing these applications into the mainstream is directly related to whether Grid Resource Providers can deliver a suitable level of QoS to the Grid Resource Consumer (GRC) and the ability of the GRC to request high level QoS such as the numbers of CPUs required or the RAM required, on demand. Moreover, we present dynamically calculated metrics for measuring QoS such as reliability, using up-to-date information on resources. We introduce a novel model, Business Grid Quality of Service (BGQoS), for a new generation of commercial and business-oriented Grid applications which may wish to make use of Grid environments. BGQoS allows GRCs to specify varying types of high level QoS requirements which are delivered via querying up-to-date resource information, matchmaking and monitoring operations. In addition, testing is required and this has posed a problem where testing on physical Grid test-beds is either impractical or not viable economically. Simulation is therefore important.

► In this paper we present a new working environment utilising Grid Computing.
► We introduce a QoS-Driven Model for meeting the QoS requirements of mainstream Grid Resource Consumers.
► We outline the communication process between the Grid Resource Consumer and the Grid Resource Providers.
► We introduce an expansion on a popular simulation toolkit used as a methodology for implementing our model.
► We conclude with results gained through experiments run using the simulation toolkit.