Performance Analysis of Heterogeneous Multi-Cluster Systems

Javadi, Bahman; Akbari, Mohammad Kazem; Abawajy, Jemal

doi:10.1109/icppw.2005.70

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…There are many studies on scheduling multisite jobs, particularly in a grid environment, taking into account co-allocation [41], network topology [42] or neither [43][44][45][46]. We use manual scheduling in this work to show the applicability to multiscale models but automatic scheduling is essential to make full use of distributed computing.…”

Section: Discussionmentioning

confidence: 99%

Performance of distributed multiscale simulations

Borgdorff

Belgacem

Bona-Casas

et al. 2014

Phil. Trans. R. Soc. A.

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Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.

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Section: Discussionmentioning

confidence: 99%

Performance of distributed multiscale simulations

Borgdorff

Belgacem

Bona-Casas

et al. 2014

Phil. Trans. R. Soc. A.

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“…There are many resource provisioning techniques both static and dynamic provisioning each having its own pros and cons. The provisioning techniques are used to improve QoS parameters [1,3,5,13], minimize cost for cloud user and maximize revenue for the Cloud Service Provider [17,25,30,32,38], improve response time [2], deliver services to cloud user even in presence of failures [11,14,15,31], improve performance [26,28,29,33,34,36] reduces SLA violation [27,37], efficiently uses cloud resources [4,6,9,10,16,19,20,22,24,40], reduces power consumption [7,21].…”

Section: Resource Provisioning Strategiesmentioning

confidence: 99%

Resource Scheduling Techniques in Cloud Computing Environment : A Survey

Raghuvanshi¹,

Dubey²

2018

IJSRSET

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Cloud Computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications and services) that can be rapidly provisioned and released. Resource Provisioning means the selection, deployment, and run-time management of software (e.g., database server management systems, load balancers) and hardware resources (e.g., CPU, storage, and network) for ensuring guaranteed performance for applications. Resource Provisioning is an important and challenging problem in the large-scale distributed systems such as Cloud computing environments. There are many resource provisioning techniques, both static and dynamic each one having its own advantages and also some challenges. These resource provisioning techniques used must meet Quality of Service (QoS) parameters like availability, throughput, response time, security, reliability etc., and thereby avoiding Service Level Agreement (SLA) violation. In this paper, survey on Static and Dynamic Resource Provisioning Techniques is made.

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“…More and more multi-clusters under heterogeneous network environment in which the performance issue was of primary importance on. Bahman Javadi et al [12,13] proposed an analytical model for studying the capabilities and potential performance of interconnection networks for multi-cluster systems. In this following discussion, our emphasis is on minimizing the communication costs for data parallel programs on cluster grid and on enhancing data distribution of communication localities with heterogeneous network.…”

Section: Introductionmentioning

confidence: 99%

Data Distribution Methods for Communication Localization in Multi-clusters with Heterogeneous Network

Chen

Hsu

Chiu

2009

2009 International Conference on Computational Science and Engineering

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Grid computing integrates scattered clusters, servers, storages and networks in different geographic locations to form a virtual super-computer. Along with the development of grid computing, dealing with the data distribution requires a method which is faster and more effective for parallel applications in order to reduce data exchange between clusters. In this paper, we present two methods to reduce inter-cluster communication cost based on the consideration to different kinds of communication cost and a simple logic mapping technology. Our theoretical analyses and simulation results show the proposed methods are better than the methods without reordering processor and considering the communication cost.The performance evaluation shows that the proposed methods not only reduce communication cost successfully but also achieve a great improvement.

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Performance Analysis of Heterogeneous Multi-Cluster Systems

Abstract: Abstract

Cited by 7 publications

References 15 publications

Performance of distributed multiscale simulations

Performance of distributed multiscale simulations

Resource Scheduling Techniques in Cloud Computing Environment : A Survey

Data Distribution Methods for Communication Localization in Multi-clusters with Heterogeneous Network

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