Task swapping networks in distributed systems

In this paper we propose task swapping networks for task reassignments by using task swappings in distributed systems. Some classes of task reassignments are achieved by using iterative local task swappings between software agents in distributed systems. We use group-theoretic methods to find a minimum-length sequence of adjacent task swappings needed from a source task assignment to a target task assignment in a task swapping network of several well-known topologies.Comment: This is a preprint of a paper whose final and definite form is published in: Int. J. Comput. Math. 90 (2013), 2221-2243 (DOI: 10.1080/00207160.2013.772985

Task Scheduling in Multiprocessing Systems

Jobs needing to be processed in a manufacturing plant, bank customers waiting to be served by tellers, aircraft waiting for landing clearances, and program tasks to be run on a parallel or distributed computer: What do these situations have in common? They all encounter the scheduling problem that emerges whenever there is a choice concerning the order in which tasks can be performed and the assignment of tasks to servers for processing. In general, the scheduling problem assumes a set of resources and a set of consumers serviced by those resources according to a certain policy. The nature of the consumers and resources as well as the constraints on them affect the search for an efficient policy for managing the way consumers access and use the resources to optimize some desired performance measure. Thus, a scheduling system comprises a set of consumers, a set of resources, and a scheduling policy

k-Depth Look-ahead Task Scheduling in Network of Heterogeneous Processors

The objective of the task scheduling is to achieve the minimum execution time of all the tasks with their precedence requirements satis ed. Although several task scheduling heuristics for the heterogeneous environment have been presented in the literature, they overlook the various type of network and do not perform eciently on such an environment. We present the new scheduling heuristic which is named the 'k-Depth Look-ahead.' The proposed heuristic takes the network heterogeneity into consideration and our experimental study shows that the proposed heuristic generates a better schedule especially under the condition that the network resource costs high