A Maximal Chain Approach for Scheduling Tasks in a Multiprocessor Systems

Scheduling dependent tasks is one of the most challenging versions of the scheduling problem in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. As a result, researchers have studied restricted forms of the problem by constraining either the task graph representing the parallel tasks or the computer model. Also, in an attempt to solve the problem in the general case, a number of heuristics have been developed. In this paper, we study the scheduling problem for a fixed number of processors m. In the proposed work, we approach the problem by recursively reducing the m-processor scheduling to (m-1)-processor scheduling until we apply the optimal two-processor scheduling algorithm when m equals two. This is accomplished by identifying a maximal chain C in the task graph G and merging the (m-1) processor scheduling of (G-C) and the 1-processor scheduling of C. A number of experiments were conducted to compare the suggested approach with the standard list-scheduling algorithm. Based on the outcome of the conducted experiments, the proposed algorithms outperformed or matched the performance of the list heuristic almost all the time

Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

Demand Shaping to Achieve Steady Electricity Consumption with Load Balancing in a Smart Grid

The purpose of this paper is to study conflicting objectives between the grid operator and consumers in a future smart grid. Traditionally, customers in electricity grids have different demand profiles and it is generally assumed that the grid has to match and satisfy the demand profiles of all its users. However, for system operators and electricity producers, it is usually most desirable, convenient and cost effective to keep electricity production at a constant rate. The temporal variability of electricity demand forces power generators, especially load following and peaking plants to constantly manipulate electricity production away from a steady operating point

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

A computer simulation of processor scheduling in UNIX 4.2BSD

This project is a study of the processor scheduling system in UNIX 4.2BSD. This study involved a computer simulation of the processor scheduling system. The preliminary work for the simulation included choosing a system, choosing and running a set of test processes on that system, gathering statistics from these runs, and constructing a model of the scheduling system. The model was then tuned to perform like the real system by introducing overhead into the model. The overhead was added using several variables in the model. Tuning consisted of adjusting the values of these variables until the performance of the model was as close as possible to that of the real system. Experiments were performed on the model consisting of a rescheduling experiment that examined the handling of compute-bound processes by the scheduler and several experiments that study the effects of modifications to the scheduler