Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids

In many data grid applications, data can be decomposed into multiple independent sub datasets and distributed for parallel execution. This property has been successfully employed using Divisible Load Theory (DLT) , which has been proven to be a powerful tool for modeling divisible load problems in large scale data grid. Load balancing in such environment plays a critical role in achieving high utilization of resources to schedule the applications efficiently through join consideration of communication and computation time. There are some scheduling models, which have been studied, such as Constraint DLT (CDLT), Task Data Present (TDP) and Genetic Algorithm (GA). However, there has been no optimal solution reached. At the same time, effective schedulers are not only required to minimize the maximum completion time (makespan) of the jobs, but also the execution time of the schedulers.This thesis proposes several load balancing models for scheduling divisible load on large scale data grids, when both processor and communication link speed are heterogeneous. The proposed models can be decomposed into three stages. The first stage is to develop new DLT based models for multiple sources scheduling. Closed form solutions for the load allocation are derived. The new models are called Adaptive DLT (ADLT) and A2DLT models. In the second stage, an Iterative DLT (IDLT) model is proposed. Recursive numerical equations are derived to find the optimal workload assigned to the grid node. The closed form solutions are derived for the optimal load allocation. Although the IDLT model is proposed for single source, it has been applied in the case of multiple sources. The third stage integrates the proposed DLT based models with GA algorithm to solve the time consuming problem. In addition, the integration of the proposed DLT model with Simulated Annealing (SA) algorithm has been also developed. The experimental results have proven that the proposed models yield better perform ance than previous models in terms of makespan and scheduler execution time. The ADLT and A2DLT models have reduced the makespan by 21% and 37% respectively compared to CDLT model. The IDLT model is capable of producing almost optimal solution for single source scheduling with low time complexity. In addition, the integration of the proposed DLT model with GA and SA algorithms has also significantly improved the performance. The SA is 64.70% better than GA in terms of makespan. Thus, the proposed models can balance the processing loads efficiently so that they can be integrated in the existing data grid schedulers to improve the performance

Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

As optical technology advances, there is a considerable interest in using this technology to implement interconnection networks and switches. Optical multistage interconnection network is popular in switching and communication applications. It has been used in telecommunication and parallel computing systems for many years. A major problem known as crosstalk is introduced by optical multistage interconnection network, which is caused by coupling two signals within a switching element. It is important to focus on an efficient solution to ,avoid crosstalk, which is routing traffic through an N x N optical network to avoid coupling two signals within each switching element.Under the constraint of avoiding crosstalk, we are interested in realising a permutation that will use the minimum number of passes to send all messages. This routing problem is an NPhard problem. Many algorithms are designed by many researchers to perform this routing such as window method, sequential algorithm, degree-descending algorithm, simulated annealing algorithm, genetic algorithm and ant colony algorithm.This thesis explores two approaches, sequential and parallel approaches. The first approach is to develop an efficient sequential algorithm for the window method. Reduction of the execution time of the algorithm in sequential platform, led to a massive improvement of the algorithm speed. Also an improved simulated annealing is proposed to solve the routing problem. The efficient combination of simulated annealing algorithm with the best heuristic algorithms gave much better result in a very minimal time. Parallelisation is another approach in our research. Three parallel strategies of the window method are developed in this research. The parallel window method with low communication overhead decreased 86% of the time compared to sequential window method. The parallel simulated annealing algorithm is also developed and it reduces 64% of the time compared to sequential simulated annealing

A new load balancing scheduling model in data grid application

Scheduling an application in data grid is significantly complex and very challenging because of its heterogeneous in nature of the grid system. Thus, Divisible Load Theory (DLT) is a powerful model for modeling data intensive grid problem where both communication and computation load is partitionable. Previously, Task Data Present (TDP) model was proposed based on DLT model. This paper presents an Adaptive TDP (ATDP) model to reduce the makespan. New equations for calculating the load allocation are derived. Experimental results showed that the proposed model can balance the load efficiently

Improved genetic algorithm for scheduling divisible data grid application

Data Grid technology promises geographically distributed scientists to access and share physically distributed resources such as computing resources, networks, storages, and most importantly data collections for large scale data intensive problems. In many Data Grid applications, Data can be decomposed into multiple independent sub datasets and distributed for parallel execution and analysis. In this paper, we exploit this property and propose an Improved Genetic Algorithm (IGA) for scheduling divisible data grid applications. A good heuristic approach used to generate the initial population. Experimental results show that the proposed IGA gives better performance compared to the Genetic Algorithm (GA)