Performance And Simulation Of Grid Information Service

A grid information service is an important part of a grid platform, and it influences the performance of the grid platform directly. Since a grid supports resource cooperation and sharing, it is very hard to simulate the pervasive resources and dynamic diversity of a grid in an experiment. That is, it is very difficult to evaluate the performance of grid information service. Researchers need to be able to discuss the performance characteristics of a grid information service, potential problems, interactions with application and infrastructure, trace data and so on.Analysing the characteristics of a grid information service is the first step to evaluating the grid information service. This thesis analyzes key technologies and the challenge of evaluating grid information services. A grid information simulationed modeling is then built based on four popular grid information mechanisms. Performance metrics includes: average response time in seconds; average throughput per second; total number of successful query responses; CPU_Load; resource_usage, and successful_requests.We used a popular tool for grid simulation, called JFREESIM, to model and simulate a grid information service. JFREESIM is a multiple tasks, multiple schedulers and multiple resources (MTMSMR) model, which is composed of a user interface, entities, a discrete-event infrastructure, and virtual resources. The embedded discrete-event simulation engine is SimJava. Entities are used to simulate the real components of a grid system. User can test a new grid information service model in JFREESIM to obtain test results and optimize the model.Time is a sensitive factor for some applications, such as content distribution networks, distributed network games, and high speed computing. Mass data is exchanged between providers and users, with a strict limitation on delays. Network latency and network hops are key factors that affect tasks. A grid information service based on network latency includes: an index service, information providers, resource providers and users. Network coordinates are introduced to locate each grid node. Users choose proper resources based on network latency, and the task implementation is completed by scheduling. A grid information service based on network hops is discussed to compute the network distance between a user and candidate resources, detect bad nodes, and support large-scale and multi-attrribute queries in a distributed manner. Both of these were evaluated in JFREESIM, and proved to be suitable for time-sensitive grid applications.Reliability is another important evaluation metric for grid information services. For grid information services based on resource availability, there are some important factors that need to be resolved, such as: network communication, network reliability, and resource availability. This mechanism regards the resource updating frequency as sample time, and uses the successful resource updating information to predict resource availability pessimistically for the next time. Three relative algorithms were addressed in this part. Experiments with JFREESIM showed that they are suitable for grid applications with reliability constraints.