Grid Computing Projects means distributed computing infrastructure. We ensure grid computing as resource based secure to satisfy the requirement of each client. We implement grid computing projects as an innovative technology to enhance productivity and efficiency of virtual organization for research scholars. Our main aid of grid computing projects is strengthening, accessing and manage grid resources in distributed computing networks. We implement grid computing projects with representing load distribution, grid security, and component management and analyze centralized communication process based on Elsevier papers. We developed more than 70+ projects for grid computing with efficient distributed algorithm to minimize delay in heavy traffic route grid.
We adopt following factors in grid computing are:
Important elements of grid computing projects are data integrity, authentication, security and security policy updates. We concentrated on grid computing security issues and implement various security policy by grid computing projects. Our main aspect of grid computing is to attain scalability, automaticity, complex management, global data access, extensibility and adaptive adjusting resource management to attain interoperability.
We offer grid computing as distributed computing network for academic projects. We handle file sharing among multiple server an challenging task in grid environment. We implement adaptive replica synchronization method to overcome distributed file system problem. By this method, we employ chunk list of data structure which hold information about relevant replicas. We ensure version based update technique to attain data consistency, proactive replica synchronization, data granularity.
We implement gridsim as open source software product to simulate grid environment. We use gridsim tools to measure performance of security, quality of service, parameter in grid computing.
We solve grid computing problem by simulated annealing algorithm. By this algorithm, we can reduce data overhead and traffic overhead. We simulate simulated annealing scheduling algorithm in gridsim.
We perform sharing, aggregation of large distributed heterogeneous resources, selection, to solve computational problems in grid computing. We need dynamic resource sharing in load balancing mechanism based on resource demand. We introduce load balancing algorithm in grid computing to manage load distribution.
We implement this algorithm in grid application for student project to measure the performance of resource utilization; task scheduling in distributed web based computing. We use this algorithm to analyze the efficiency of scheduling algorithm.Grid Computing Projects
We proposed two elements in grid computing are resource allocation and task scheduling. We utilized market based resource allocation model to solve resource management problem. Resource allocation model composed of online reverse action algorithms which dynamically adjust resource based on task length.