||The technologies of multi-core processors and networks have been continuously growing in recent years. It is such a provider that the applications can be operated on Grid.
Grid computing practice includes the internet services and connections of a boundless number of ubiquitous computational resources. The computational scheme can be easiest through of as a massively utility computing power, such as whatever provides energy power to human society for each product every day. In the similar utility style, Grid openly explores and is capable of adding an infinite number of computation nodes and applications resolutions within any pervasive and ubiquitous Grid infrastructure. Grid also is a fashion of parallel and distributed system that possesses the aggregation, selection, and sharing of geographically separateness autonomous computation nodes for adaptive execution relationship to applications requirement, cost, utilization, and performance.
In fact, key services in Grid computing such as resource discovery, monitoring and scheduling are inherently much more complicated in a Grid computing environment where the resource pool is vast, dynamic and architecturally diverse. So, many coarse-grained distributed and parallel applications can gain benefit from the Grid infrastructure, such as collaborative engineering, data exploration, high-performance computing, etc. Therefore, Grid is used to collect the power of widely separateness autonomous computational nodes, so as to provide valuable services to users.
An efficient Grid scheduling system is an essential nucleus in Grid environment in achieving said motion. The scheduling problems in Grid are dynamic as the jobs and resources in this environment vary over time. Hence, the scheduling system is the key issue of Grid computing, and the associated algorithm has a direct effect on the performance of the Grid operation. An alternative is to choose an appropriate scheduling algorithm to be used in a given Grid environment subject to the characteristics of the jobs, nodes and networks. The Adaptive feature based scheduling algorithms is a new trend in Grid scheduling which are applicable in dynamic Grid environments, and the submitting jobs is ordered and dispatched according to a priority methodology. The goal of scheduling is to achieve a highest possible throughput and to satisfy the application requirement equipped with the available computing resources.
In this dissertation, the motivation from the survey is utilized to develop a more efficient scheduling algorithm where a nucleus in Grid computing environment plays the initial role, an efficient prediction-based adaptive scheduling scheme for Grid computing is then proposed. The scheme can logically be divided into two mainly phases. The one hand is scheduling system. The adaptive scheduling system is adopted to represent the performance of Grid nodes, the task workloads, and the schedules. The scheduling strategy dose adopt dependent on accuracy of the prediction system and can adapt to the Grid computing environment. The other hand is prediction system. In order to provide higher accuracy, the prediction system selects a good predictor for the scheduling system. According to the accuracy of the proposed prediction system, the system selects a proper strategy so as to schedule tasks. The scheme can be used with a suitable scheduling algorithm that needs the prediction information if necessary. The experimental results of the simulation show that the proposed scheme is able to perform scheduling well in the Grid computing.