Informatics Publishing Limited

S. V. Sudha ¹, K. Thanushkodi ²

Affiliations
1 Department of Information Technology, Kalignar Karunanidhi Institute of Technology, Coimbatore 641402 , Tamil Nadu, IN
2 Akshaya College of Engineering and Technology, Coimbatore - 642 109, Tamil Nadu, IN

Abstract

Coarse Grain applications require all processes run with a very gross level of synchronization among them. Any collection of concurrent processes that need to communicate or synchronize can benefit from the use of a multiprocessor architecture. This type of application can be achieved from time and space slicing, but the algorithms suffer from fragmentation, where the processors are left idle because jobs cannot be packed with 100% efficiency. This fragmentation results to reduce utilization and poor performance. Spry algorithm concentrates on the above-mentioned problems and the results of the spry algorithm are compared with First Come First Serve. Gang Scheduling and Flexible Co scheduling. Spry Algorithm is implemented with Java thread and the results are compared with the performance metrics like Turn around Time, Mean Response Time, Mean Reaction Time, Mean Slowdown, Mean Utilization and Average waiting time of the Jobs.

Keywords

Parallel Job Scheduling, Performance Metric, Mean Slowdown.

Full Text

     

S. V. Sudha ¹, K. Thanushkodi ²

Affiliations
1 Department of Information Technology, Kalignar Karunanidhi Institute of Technology, Coimbatore-641402, Tamil Nadu, IN
2 Akshaya College of Engineering and Technology, Coimbatore-642109, Tamil Nadu, IN

Abstract

The problem of scheduling a set of dependent or independent tasks to be processed in a parallel fashion is one of the most challenging problems in parallel computing. The goal of a scheduler is to assign tasks to available processors such that precedence requirements between tasks are satisfied and the overall length of time required to execute the entire program, the schedule length or make span is minimized. A Parallel Genetic Algorithm Approach has been developed to the problem of task scheduling. GA is competitive in terms of solution quality if it has sufficient resources to perform its search. The Job taken for the Scheduling is the Detection of a Moving Object in a Video Sequence. The Moving Object Segmentation is suitable for real time content-based multimedia communication systems. First a background registration technique is used to construct as reliable background image from the accumulated frame difference information. The moving object region is then separated from the background region by comparing the current frame with the constructed background image. The implementation is optimized using parallel processing and achieved on a personal computer with a 3.0 GHZ Pentium IV Processor. Good segmentation performance is demonstrated by the simulation results.

Keywords

Background Registration, Moving Object Segmentation, Genetic Algorithm, Parallel Genetic Algorithm, Fitness Function.

Full Text

     

S. V. Sudha , K. Thanushkodi

Abstract

In this paper, we introduce a fuzzy based engine for the scheduling problem. In real world scheduling problems, due to the synchronization granularity of processes, the scheduling of parallel jobs becomes ineffective. A good way of characterizing a parallel system is to consider the synchronization granularity or frequency of synchronization between processes in a system. The scientific applications of the parallel system consist of multiple processes running on different processors that communicate frequently. The performance evaluation of such systems mainly depends on how the processes are co scheduled. If the processes are not co scheduled properly, then the system will lead to severe performance penalties. The various co scheduling techniques available are First Come First Served, Gang Scheduling and Flexible Co Scheduling and nimble algorithm. The paper focuses on an attempt to develop fuzzy systems based on the scheduling criteria which decides the best scheduling algorithm for parallel jobs.

Keywords

First Come First Served, Gang Scheduling, Nimble Algorithm, Mean Response Time, Turn Around Time.

Full Text