An optimal data partitioning in parallel/distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd-Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimen...

This paper presents the design and preliminary evaluation of hierarchical partitioning and load-balancing techniques for distributed Structured Adaptive Mesh Refinement (SAMR) applications. The overall goal of these techniques is to enable the load distribution to reflect the state of the adaptive grid hierarchy and exploit it to reduce synchronization requirements, improve load-balance, and en...

This article proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during systems contingencies, thereby mitigating disasters. Towards best microgrid candidates, this work formulates chance-constrained optimal network partitioni...

Cluster and grid computing has made hierarchical and heterogeneous computing systems increasingly common as target environments for large-scale scientific computation. A cluster may consist of a network of multiprocessors. A grid computation may involve communication across slow interfaces. Modern supercomputers are often large clusters with hierarchical network structures. For maximum efficien...

This paper is aimed to propose a grid density clustering algorithm for spatio-temporal data that is based on the adaptation of the grid density based clustering algorithm. The algorithm is based on AGRID+ algorithm with 7 steps: partitioning, computing distance threshold, calculating densities, compensating densities, calculating density threshold (DT), clustering and removing noises. The adapt...

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. It is hard to analyze and model the Grid reliability because of its largeness, complexity and stiffness. Therefore, this chapter introduces the Grid computing technology, presents different types of failures in grid system, models ...

OBJECTIVES In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. METHODS A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. ...

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. It is hard to analyze and model the Grid reliability because of its largeness, complexity and stiffness. Therefore, this chapter introduces the Grid computing technology, presents different types of failures in grid system, models ...

Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topo...