5,495 research outputs found
Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids
Peer ReviewedPostprint (published version
Economic-based Distributed Resource Management and Scheduling for Grid Computing
Computational Grids, emerging as an infrastructure for next generation
computing, enable the sharing, selection, and aggregation of geographically
distributed resources for solving large-scale problems in science, engineering,
and commerce. As the resources in the Grid are heterogeneous and geographically
distributed with varying availability and a variety of usage and cost policies
for diverse users at different times and, priorities as well as goals that vary
with time. The management of resources and application scheduling in such a
large and distributed environment is a complex task. This thesis proposes a
distributed computational economy as an effective metaphor for the management
of resources and application scheduling. It proposes an architectural framework
that supports resource trading and quality of services based scheduling. It
enables the regulation of supply and demand for resources and provides an
incentive for resource owners for participating in the Grid and motives the
users to trade-off between the deadline, budget, and the required level of
quality of service. The thesis demonstrates the capability of economic-based
systems for peer-to-peer distributed computing by developing users'
quality-of-service requirements driven scheduling strategies and algorithms. It
demonstrates their effectiveness by performing scheduling experiments on the
World-Wide Grid for solving parameter sweep applications
04451 Abstracts Collection -- Future Generation Grids
The Dagstuhl Seminar 04451 "Future Generation Grid" was held in the International
Conference and Research Center (IBFI), Schloss Dagstuhl from 1st
to 5th November 2004. The focus of the seminar was on open problems and
future challenges in the design of next generation Grid systems. A total of 45
participants presented their current projects, research plans, and new ideas in
the area of Grid technologies. Several evening sessions with vivid discussions
on future trends complemented the talks. This report gives an overview of the
background and the findings of the seminar
A comparison of resource allocation process in grid and cloud technologies
Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision
Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid
Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However,
stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average
makespan values
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