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Journal ArticleDOI

An adaptive meta-scheduler for data-intensive applications

TLDR
An adaptive scheduling model that considers availability of computational, storage and network resources is described and a scheduler used in the authors' campus grid is implemented.
Abstract
In data-intensive applications, such as high-energy physics, bio-informatics, we encounter applications involving numerous jobs that access and generate large datasets. Effective scheduling of such applications is a challenge, due to the need to consider for both computational resources and data storage resources. In this paper, we describe an adaptive scheduling model that considers availability of computational, storage and network resources. Based on this model we implement a scheduler used in our campus grid. The results achieved by our scheduler have been analysed by comparing with greedy algorithm that is widely used in computational grids and some data grids.

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Citations
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Scheduling Algorithms for Grid Computing: State of the Art and Open Problems

TL;DR: This survey provides a review of the subject of Grid scheduling mainly from the perspective of scheduling algorithms, and identifies the challenges and state of the art of current research.
Proceedings ArticleDOI

Improving cloud infrastructure utilization through overbooking

TL;DR: This work proposes scheduling and admission control algorithms that incorporate resource overbooking to improve utilization and demonstrates the potential for significant improvements in resource utilization while still avoiding overpassing the total capacity.
Journal ArticleDOI

Data intensive and network aware (DIANA) grid scheduling

TL;DR: A Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites is described.
Journal ArticleDOI

An Autonomic Approach to Risk-Aware Data Center Overbooking

TL;DR: This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources.
Journal ArticleDOI

A Bee Colony based optimization approach for simultaneous job scheduling and data replication in grid environments

TL;DR: This paper presents a novel Bee Colony based optimization algorithm, named Job Data Scheduling using Bee Colony (JDS-BC), which consists of two collaborating mechanisms to efficiently schedule jobs onto computational nodes and replicate datafiles on storage nodes in a system so that the two independent, and in many cases conflicting, objectives are concurrently minimized.
References
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Journal ArticleDOI

The Anatomy of the Grid: Enabling Scalable Virtual Organizations

TL;DR: The authors present an extensible and open Grid architecture, in which protocols, services, application programming interfaces, and software development kits are categorized according to their roles in enabling resource sharing.
Journal ArticleDOI

The network weather service: a distributed resource performance forecasting service for metacomputing

TL;DR: The current implementation of the NWS for Unix and TCP/IP sockets is described and examples of its performance monitoring and forecasting capabilities are provided.
Journal ArticleDOI

The data grid

TL;DR: In this paper, the authors introduce design principles for a data management architecture called the data grid, and describe two basic services that are fundamental to the design of a data grid: storage systems and metadata management.
Proceedings ArticleDOI

Decoupling computation and data scheduling in distributed data-intensive applications

TL;DR: This work develops a family of algorithms and uses simulation studies to evaluate various combinations of these algorithms to suggest that while it is necessary to consider the impact of replication, it is not always necessary to couple data movement and computation scheduling.
Book ChapterDOI

Data Management in an International Data Grid Project

TL;DR: Preliminary work and architectural design carried out in the "Data Management" work package in the International Data Grid project is reported on, which will provide Grid middleware services supporting the I/O-intensive world-wide distributed next generation experiments in High-Energy Physics, Earth Observation and Bioinformatics.