Task scheduling in heterogeneous computing environment such as grid computing is a critical and challenging problem. Based on traditional list scheduling we present a static task scheduling algorithm LBP (Level and Branch Priority) adapted to heterogeneous hosts in grid computing. The contribution of LBP algorithm lies mainly on working out a new method determining task priority in task ready l...

The scheduling of tasks within a parallel program onto the underlying available processors has been studied for some time. To date, solutions to this problem generally assume that communication only occurs at the start or end of each parallel task, i.e., the child task can only start its execution when all its parent tasks complete and have sent data to it. This is termed \non-preemptive task s...

Algorithms for the scheduling of tasks within a parallel program onto available processors generally assume that communication only occurs at the start or end of each task, i.e., a task can only commence execution when all necessary data has been sent to it. This is termed \non-preemptive task scheduling". This paper examines the problem of the preemptive parallel program which is represented b...

Presently, there is little assistance available to developers who would like to create and integrate formal software models with customized visualizations of the architecture and operation of such models. To help solve this problem, the rube framework presents methodologies and development tools for both formal software modeling and for the creation of customized, three-dimensional software mod...

This paper reports on a novel decentralised technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralised...

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, it has been one of challenging problems that how each task should be scheduled after a task allocation by a task clustering. We propose a task scheduling method after task clustering, leveraging Worst Schedule Length (WSL) as an upper bou...

We present a novel characterization of how a program stresses cache. This characterization permits fast performance prediction in order to simulate and assist task scheduling on heterogeneous clusters. It is based on the estimation of stack distance probability distributions. The analysis requires the observation of a very small subset of memory accesses, and yields a reasonable to very accurat...

An optimal scheduling algorithm is described that feasibly schedules a set of m periodic tasks on n processors before their respective deadlines, if the task set satisses certain conditions. The complexity of this scheduling algorithm in terms of the number of scheduled tasks and the number of processors and upper bounds on the number of preemptions in a given time interval and for any single t...

Limited energy resources dictate the design of many embedded applications composed of small, modular tasks, scheduled periodically. In this model, the embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almost-always-sleeping (AAS) systems. In this paper, we ...

In this paper we present the GraphHTN algorithm, a hybrid planning algorithm that does Hierarchical Task-Network (HTN) planning using a combination of HTN-style problem reduction and Graphplan-style planning-graph generation. We also present experimental results comparing GraphHTN with ordinary HTN decomposition (as implemented in the UMCP planner) and ordinary Graphplan search (as implemented ...