A Distributed Iterative Algorithm for Optimal Scheduling in Grid Computing

The paper studies a distributed iterative algorithm for optimal scheduling in grid computing. Grid user's requirements are formulated as dimensions in a quality of service problem expressed as a market game played by grid resource agents and grid task agents. User benefits resulting from taking decisions regarding each Quality of Service dimension are described by separate utility functions. The total system quality of service utility is defined as a linear combination of the discrete form utility functions. The paper presents distributed algorithms to iteratively optimize task agents and resource agents functioning as sub-problems of the grid resource QoS scheduling optimization. Such constructed resource scheduling algorithm finds a multiple quality of service solution optimal for grid users, which fulfils some specified user preferences. The proposed pricing based distributed iterative algorithm has been evaluated by studying the effect of QoS factors on benefits of grid user utility, revenue of grid resource provider and execution success ratio

Cost-Efficient Scheduling for Deadline Constrained Grid Workflows

Cost optimization for workflow scheduling while meeting deadline is one of the fundamental problems in utility computing. In this paper, a two-phase cost-efficient scheduling algorithm called critical chain is presented. The proposed algorithm uses the concept of slack time in both phases. The first phase is deadline distribution over all tasks existing in the workflow which is done considering critical path properties of workflow graphs. Critical chain uses slack time to iteratively select most critical sequence of tasks and then assigns sub-deadlines to those tasks. In the second phase named mapping step, it tries to allocate a server to each task considering task's sub-deadline. In the mapping step, slack time priority in selecting ready task is used to reduce deadline violation. Furthermore, the algorithm tries to locally optimize the computation and communication costs of sequential tasks exploiting dynamic programming. After proposing the scheduling algorithm, three measures for the superiority of a scheduling algorithm are introduced, and the proposed algorithm is compared with other existing algorithms considering the measures. Results obtained from simulating various systems show that the proposed algorithm outperforms four well-known existing workflow scheduling algorithms