In this paper, we demonstrate the use of stochastic dynamic programming to solve over-constrained scheduling problems. In particular, we propose a decision method for efficiently calculating, prior to start of execution, the optimal decision for every possible situation encountered in sequential, predictable, overconstrained scheduling domains. We present our resuits using an example problem fr...

We present an account of scheduling component based applications to run on a computational Grid. We discuss the structure of a Grid, where scheduling fits within the architecture and examine the aims and purpose of a scheduler. A classification for Grid schedulers is given, and applied to a number of existing scheduling algorithms. We examine the scheduling service within the ICENI Grid Middlew...

The paper presents a mathematical model of a computing multi-agent system. The dynamics of a MAS is described in terms of stochastic process theory. Then the optimal scheduling problem for the MAS is formulated together with a result on the existence of optimal strategies. Finally some sufficient conditions for optimality are presented as a way of verification of heuristic scheduling strategies.

Co-flows model a modern scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic co-flow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of co-flow tasks. This paper gives an approximation algorithm for stochastic nonpreemptive co-flow scheduling. Th...

We present first constant performance guarantees for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release dates on identical parallel machines we derive policies with a guaranteed performance ratio of 2 which matches the currently best known result for the corresponding deterministic online problem. Our policies apply to the recentl...

We address the problem of minimizing the total weighted completion time in stochastic machine scheduling. A set of jobs has to be processed on identical parallel machines, job processing times are drawn from some known probability distribution, and the aim is to find a scheduling strategy that minimizes the objective in expectation. The overall driving question is: What is the performance of si...