Doctoral Thesis / Dissertation from the year 2014 in the subject Computer Science - Technical Computer Science, Sir Padampat Singhania University, language: English, abstract: Grid technology is a new paradigm which has the potential to completely change the way of computing and data access. Generally speaking, we could consider the Grid as the new enabling technology to transparently access computing and storage resources anywhere, anytime and with guaranteed Quality of Service (QoS). Grid computing has emerged to cater the need of computingon-demand due to the advent of distributed computing with sophisticated load balancing, distributed data and concurrent computing power using clustered servers. The Grid enables resource sharing and dynamic allocation of computational resources, thus increasing access to distributed data, promoting operational flexibility and collaboration, and allowing service providers to scale efficiently to meet variable demands.
The lack of adequate development methods for this kind of systems since the majority of existing Grid applications have been built without a systematic development process and are based on adhoc developments suggests the need for adapted development methodologies.
This thesis concern the resource discovery and trust management with security in large size of future grid. An automatic discovery mechanism is needed to find nodes willing to participate in the grid. For mobile grids, a decentralized discovery mechanism is vital to cope with the fluctuating topology and large number of participants.
The thesis implemented an Ant based discovery mechanism in which forward and backward ants are used to establish super-grid nodes. The criteria for selecting the super-grid nodes include distance, CPU speed, available bandwidth and residual battery power. After establishing the supergrid nodes among the grid nodes, they collect information about all the resources in a resource table. It consists of grid node id, resource availability, distance from super-grid etc. If any node wants a specific resource, it sends request to its nearest super-grid node from which the node ids matching the request, are returned.
The local and global trust values of each node can be estimated based on the factors Job Response time, percentage of correctly received data, Number of successfully finished jobs. These factors can be collected based on the feedback from the user.
The trust values can be updated based on the predictive residence time of each grid node. (ie) The node with least residence time (with high mobility) is penalized by reducing the trust value by a step value. [...]
