Secure accounting and payment infrastructure for grid computing

In this paper, we propose an architecture of accounting and payment services for service oriented grid computing systems. The proposed accounting and payment services provide the mechanisms for service providers to be paid for authorized use of their resources. It supports the recording of usage data, secure storage of that data, analysis of that data for purposes of billing and so forth. It allows a variety of payment methods, it is scalable, secure, convenient, and reduce the overall cost of payment processing while taking into account, requirements of Grid computing systems.

Analytical communication networks model for enterprise grid computing

This paper addresses the problem of performance analysis based on the communication modeling of large-scale heterogeneous distributed systems, with an emphasis on enterprise Grid computing systems. The study of communication layers is important, as the overall performance of a distributed system often critically hinges on the effectiveness of this part. We propose an analytical model that is based on probabilistic analysis and queuing networks. The proposed model considers the processor as well as network heterogeneity of the enterprise Grid system. The model is validated through comprehensive simulations, which demonstrate that the proposed model exhibits a good degree of accuracy for various system sizes, and under different working conditions.

Analytical performance modeling of the enterprise grid computing

This paper addresses the problem of performance modeling of large-scale heterogeneous distributed systems with emphases on enterprise grid computing systems. To this end, we present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation a cost-effective large-scale heterogeneous distributed computing system. The model is validated through comprehensive simulation.

An autonomic management system for service oriented enterprise grid computing

Grid computing and service oriented architectures improve the way computational tasks are performed. Through this research a management system, utilising the autonomic characteristics of self discovery and negotiation, self configuration and self healing, was designed and implemented, ultimately removing the need for users to know the intricacies of these systems.

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

GLOA : a new job scheduling algorithm for grid computing

The purpose of grid computing is to produce a virtual supercomputer by using free resources available through widespread networks such as the Internet. This resource distribution, changes in resource availability, and an unreliable communication infrastructure pose a major challenge for efficient resource allocation. Because of the geographical spread of resources and their distributed management, grid scheduling is considered to be a NP-complete problem. It has been shown that evolutionary algorithms offer good performance for grid scheduling. This article uses a new evaluation (distributed) algorithm inspired by the effect of leaders in social groups, the group leaders' optimization algorithm (GLOA), to solve the problem of scheduling independent tasks in a grid computing system. Simulation results comparing GLOA with several other evaluation algorithms show that GLOA produces shorter makespans.

Tecniche di resource discovery nel grid computing

In questa tesi vengono analizzate le principali tecniche di Resource Discovery in uso nei sistemi di Grid Computing, valutando i principali vantaggi e svantaggi di ogni soluzione. Particolare attenzione verrà riposta sul Resource Discovery ad Agenti, che si propone come architettura capace di risolvere in maniera definitiva i classici problemi di queste reti. All'interno dell'elaborato, inoltre, ogni tecnica presentata verrà arricchita con una sua implementazione pratica: tra queste, ricordiamo MDS, Chord e l'implementazione Kang.

A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.

RSA-Grid: A grid computing based framework for power system reliability and security analysis

Grid computing is an emerging technology for providing the high performance computing capability and collaboration mechanism for solving the collaborated and complex problems while using the existing resources. In this paper, a grid computing based framework is proposed for the probabilistic based power system reliability and security analysis. The suggested name of this computing grid is Reliability and Security Grid (RSA-Grid). Then the architecture of this grid is presented. A prototype system has been built for further development of grid-based services for power systems reliability and security assessment based on probabilistic techniques, which require high performance computing and large amount of memory. Preliminary results based on prototype of this grid show that RSA-Grid can provide the comprehensive assessment results for real power systems efficiently and economically.