Enhancing reliability with Latin Square redundancy on desktop grids.

Computational grids are some of the largest computer systems in existence today. Unfortunately they are also, in many cases, the least reliable. This research examines the use of redundancy with permutation as a method of improving reliability in computational grid applications. Three primary avenues are explored - development of a new redundancy model, the Replication and Permutation Paradigm (RPP) for computational grids, development of grid simulation software for testing RPP against other redundancy methods and, finally, running a program on a live grid using RPP. An important part of RPP involves distributing data and tasks across the grid in Latin Square fashion. Two theorems and subsequent proofs regarding Latin Squares are developed. The theorems describe the changing position of symbols between the rows of a standard Latin Square. When a symbol is missing because a column is removed the theorems provide a basis for determining the next row and column where the missing symbol can be found. Interesting in their own right, the theorems have implications for redundancy. In terms of the redundancy model, the theorems allow one to state the maximum makespan in the face of missing computational hosts when using Latin Square redundancy. The simulator software was developed and used to compare different data and task distribution schemes on a simulated grid. The software clearly showed the advantage of running RPP, which resulted in faster completion times in the face of computational host failures. The Latin Square method also fails gracefully in that jobs complete with massive node failure while increasing makespan. Finally an Inductive Logic Program (ILP) for pharmacophore search was executed, using a Latin Square redundancy methodology, on a Condor grid in the Dahlem Lab at the University of Louisville Speed School of Engineering. All jobs completed, even in the face of large numbers of randomly generated computational host failures

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Decentralized Resource Availability Prediction in Peer-to-Peer Desktop Grids

Grid computing is a form of distributed computing which is used by an organiza­ tion to handle its long-running computational tasks. Volunteer computing (desktop grid) is a type of grid computing that uses idle CPU cycles donated voluntarily by users, to run its tasks. In a desktop grid model, the resources are not dedicated. The job (computational task) is submitted for execution in the resource only when the resource is idle. There is no guarantee that the job which has started to execute in a resource will complete its execution without any disruption from user activity (such as keyboard click or mouse move). This problem becomes more challenging in a Peer-to-Peer (P2P) model of desktop grids where there is no central server which takes the decision on whether to allocate a job to a resource. In this thesis we propose and implement a P2P desktop grid framework which does resource availability prediction. We try to improve the predictability of the system, by submitting the jobs on machines which have a higher probability of being available at a given time. We benchmark our framework and provide an analysis of our results

Effective Scheduling of Grid Resources Using Failure Prediction

In large-scale grid environments, accurate failure prediction is critical to achieve effective resource allocation while assuring specified QoS levels, such as reliability. Traditional methods, such as statistical estimation techniques, can be considered to predict the reliability of resources. However, naive statistical methods often ignore critical characteristic behavior of the resources. In particular, periodic behaviors of grid resources are not captured well by statistical methods. In this paper, we present an alternative mechanism for failure prediction. In our approach, the periodic pattern of resource failures are determined and actively exploited for resource allocation with better QoS guarantees. The proposed scheme is evaluated under a realistic simulation environment of computational grids. The availability of computing resources are simulated according to real trace that was collected from our large-scale monitoring experiment on campus computers. Our evaluation results show that the proposed approach enables significantly higher resource scheduling effectiveness under a variety of workloads compared to baseline approaches

Collaborative research: ITR: global multi-scale kinetic simulations of the earth's magnetosphere using parallel discrete event simulation

Issued as final reportNational Science Foundation (U.S.

Flexible distributed computing with volunteered resources

PhDNowadays, computational grids have evolved to a stage where they can comprise many volunteered resources owned by different individual users and/or institutions, such as desktop grids and volunteered computing grids. This brings benefits for large-scale computing, as more resources are available to exploit. On the other hand, the inherent characteristics of the volunteered resources bring some challenges for efficiently exploiting them. For example, jobs may not be able to be executed by some resources, as the computing resources can be heterogeneous. Furthermore, the resources can be volatile as the resource owners usually have the right to decide when and how to donate the idle Central Processing Unit (CPU) cycles of their computers. Therefore, in order to utilise volunteered resources efficiently, this research investigated solutions from different aspects. Firstly, this research proposes a new computational Grid architecture based on Java and Java application migration technologies to provide fundamental support for coping with these challenges. This proposed architecture supports heterogeneous resources, ensuring local activities are not affected by Grid jobs and enabling resources to carry out live and automatic Java application migration. Secondly, this research work proposes some job-scheduling and migration algorithms based on resource availability prediction and/or artificial intelligence techniques. To examine the proposed algorithms, this work includes a series of experiments in both synthetic and practical scenarios and compares the performance of the proposed algorithms with existing ones across a variety of scenarios. According to the critical assessment, each algorithm has its own distinct advantages and performs well when certain conditions are met. In addition, this research analyses the characteristics of resources in terms of the availability pattern of practical volunteer-based grids. The analysis shows that each environment has its own characteristics and each volunteered resource’s availability tends to possess weak correlations across different days and times-of-day.British Telco