Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

Kołodziej, Joanna; Khan, Samee U.

doi:10.1007/978-3-642-38496-7_7

Cited by 26 publications

(21 citation statements)

References 43 publications

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“…, f (r,j) } (F j ⊆ F batch ) that are distributed on a subset DH j of the data nodes DH. We assume that each data host can serve multiple data files at a time and data replication is a priori defined as a separate replication process [6].…”

Section: Data-aware Etc Matrix Modelmentioning

confidence: 99%

“…, r}) from the data host dh (p,j) ∈ D j to the computational node m i . This parameter can be calculated as follows [6]:…”

Section: Data-aware Task Execution Time Modelmentioning

confidence: 99%

“…Based on our previous work [6], we have integrated the data transmission and data nodes location criteria with the traditional scheduling objectives, namely makespan and flowtime. We provided a simple empirical analysis with genetic-based schedulers, that have been also tested in our previous works for similar class of problems, where data access and processing were ignored (see [5] for details).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Kołodziej¹,

Szmajduch²,

Khan³

et al. 2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. In this work we present two implementations of classical genetic-based data-aware schedulers of independent tasks submitted to the grid environment. The results of a simple empirical analysis confirm the high effectiveness of the genetic algorithms in solving very complex data intensive combinatorial optimization problems.

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Section: Data-aware Etc Matrix Modelmentioning

confidence: 99%

“…, r}) from the data host dh (p,j) ∈ D j to the computational node m i . This parameter can be calculated as follows [6]:…”

Section: Data-aware Task Execution Time Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Kołodziej¹,

Szmajduch²,

Khan³

et al. 2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the impact of placement strategies on the future executions of the grid has not yet been studied in great depth (Hamrouni et al, 2015b). Indeed, as discussed in Hockauf et al (1998) and Kolodziej and Khan (2013), changes in the behaviour of the distributed systems do not come all at once but rather gradually. This is also supported by the temporal locality notion (Abad et al, 2012).…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Objective assessment of the performance of data grid replication strategies based on distribution quality

Hamrouni

Hamdeni

Charrada

2016

IJWET

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Abstract:Replication is an important issue for the data grid performance. Indeed, it has for main purposes to improve data access efficiency, provide high availability, decrease bandwidth consumption, improve fault tolerance and enhance scalability. In this regard, where to place a given replica represents an important step in a replication process. In this context, a significant number of placement strategies has been proposed in the literature. Each placement strategy produces a specific replicas distribution through the grid sites. This distribution will directly affect the performance of the grid. In this paper, we propose a new evaluation metric called the distribution quality, and denoted DisQ. This metric quantifies, at a given point in time, the quality of a given distribution of replicas through the grid nodes. DisQ helps to know beforehand that a given distribution will contribute in obtaining interesting data grid performance or it will degrade them. Using DisQ, we propose a correction for the evaluation metrics of replication strategies to make them more reliable. We use the OptorSim simulator to validate our theoretical contributions.

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“…(a) computational Grid (e.g., TeraGrid, ChinaGrid, and APACGrid [107]) that combines the computational power of distributed resources including clusters, desktops, and supercomputers that provide services for high performance computing [55][56][57], (b) access Grid [106,125] that offers limited specific resources for a short period of time, (c) data Grid (e.g., LHCGrid, GriPhyN [107]) is used by data-intensive tasks that consists of distributed data repositories providing the facilities to store enormous amount of data that can be accessed, moved, and processed as if they were small files [65,138], and [122] . .…”

Section: Introductionmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

Cited by 26 publications

References 43 publications

Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Objective assessment of the performance of data grid replication strategies based on distribution quality

Survey on Grid Resource Allocation Mechanisms

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