On the load distribution and performance of meta-computing systems

Savvas, Ilias Κ.; Kechadi, Tahar

doi:10.1109/ispdc.2003.1267667

Cited by 5 publications

(1 citation statement)

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“…This set is consisting of resource representatives that are "close enough" to r i . The notion of neighborhood can be extended to any distance measure (norm) by mapping the space of system factors (parameters) to a vector space [9,10,2]. For example, two representative nodes can be characterized as neighbors if their Euclidean distance is less than a predefined value (i.e.…”

Section: Workers' Clusteringmentioning

confidence: 99%

On Resource Clustering Techniques for Grid Resource Discovery

Savvas¹,

Kakarontzas²

2007

16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007)

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Grids offer the potential of harnessing vast amounts of computational resources during the execution of demanding computations. These resources are geographically distributed, owned by different organizations and are highly heterogeneous. All these create an uncertain environment in all phases of a Grid Scheduling Process (GSP). In this work, we focus on the resource discovery process during which clients of the grid discover possibly suitable resources available for their computation. We propose a network of resource representatives, which maintain the moreor-less static characteristics of available workers they represent (e.g. OS, CPU type etc.). We show that clustering algorithms is a promising approach that can be used for the efficient discovery of suitable resources for a given task set.

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Section: Workers' Clusteringmentioning

confidence: 99%