Strategies of Domain Decomposition to Partition Mesh-Based Applications onto Computational Grids

In this paper, we evaluate strategies of domain decomposition in Grid environment to solve mesh-basedapplications. We compare the balanced distribution strategy with unbalanced distribution strategies. While the former is acommon strategy in homogenous computing environment (e.g. parallel computers), it presents some problems due tocommunication latency in Grid environments. Unbalanced decomposition strategies consist of assigning less workload toprocessors responsible for sending updates outside the host. The results obtained in Grid environments show that unbalanceddistributions strategies improve the expected execution time of mesh-based applications by up to 53%. However, this is not truewhen the number of processors devoted to communication exceeds the number of processors devoted to calculation in thehost. To solve this problem we propose a new unbalanced distribution strategy that improves the expected execution time up to43%. We analyze the influence of the communication patterns on execution times using the Dimemas simulator.Peer ReviewedPostprint (published version

Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Dynamic load balancing of parallel road traffic simulation

The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version

A resource aware distributed LSI algorithm for scalable information retrieval

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Semantic Indexing (LSI) is one of the popular techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be overcome based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This thesis presents a resource aware distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model MapReduce. It also solves the overhead issue caused by the involved clustering algorithm. The evaluations indicate that MR-LSI can gain significant enhancement compared to the other strategies on processing large scale of documents. One remarkable advantage of Hadoop is that it supports heterogeneous computing environments so that the issue of unbalanced load among nodes is highlighted. Therefore, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can improve the performance of a cluster according to heterogeneity levels. Considering dynamic Hadoop environments, a dynamic load balancing strategy with varying window size has been proposed. The algorithm works depending on data selecting decision and modeling Hadoop parameters and working mechanisms. Employing improved genetic algorithm for achieving optimized scheduler, the algorithm enhances the performance of a cluster with certain heterogeneity levels