FRDT: Footprint Resource Discovery Tree for grids

Khanli, Leyli Mohammad; Kargar, Saeed

doi:10.1016/j.future.2010.08.002

Cited by 24 publications

(15 citation statements)

References 20 publications

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“…The job grouping concept is used to maximise resource utilisation for scheduling of jobs in grid computing. However, this approach is also not scalable like the work in Khanli and Kargar (2010) since the organisation of agents is a HST. In contrast, our approach is scalable by using multiple broker agents instead of using a HST.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the existing resource discovery and brokering systems that are related to this research include condor ( Raman et al, 1998;Han and Berry, 2008), ARMS (Cao et al, 2001;Ding et al, 2005;Dominiak et al, 2007), ACG (Chunlin and Layuan, 2003), UWAgents (Fukuda and Smith, 2006;Khanli and Kargar, 2010;Sharma et al, 2010;Li et al, 2006).…”

Section: Related Workmentioning

confidence: 99%

“…In Khanli and Kargar (2010), the authors used a footprint resource discovery tree in which, the tree edge are weighted, and all resource discovery paths are unique. In this method, the resource information is gathered upwards directing to the root of the tree, and every resource makes its footprint in the upward nodes through the root.…”

Section: Related Workmentioning

confidence: 99%

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Ontology-enhanced agent-based cloud service discovery

Kang

Sim

2016

IJCC

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Cloud computing has attracted great interest from both industrial and academic communities. However, only a few efforts have been devoted to building tools for supporting Cloud service discovery. Therefore, we present a four-stage, agent-based Cloud service discovery protocol. Additionally, two Cloud ontologies (CO-1 and CO-2) are designed to semantically define the relationship among Cloud services. Whereas CO-1 contains only Cloud concepts, CO-2 contains a set of Cloud concepts, individuals of those concepts, and the relationship among those individuals. The similarity among Cloud services is determined by concept, object property, and data type property similarity reasoning. In addition, two kinds of recommendation approaches (R1 and R2) based on attribute value prediction are presented. R1 is based on the maximum and R2 on the average similarity between the provided and the requested requirements. Empirical results show that our system achieved the best performance in finding the appropriate Cloud services with CO-2 and R2.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Ontology-enhanced agent-based cloud service discovery

Kang

Sim

2016

IJCC

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“…Resource discovery in Grid through hierarchical method by "Footprint" The "Footprint" method is introduced in [13] where the hierarchical method is adopted in resource management and discovery. In this method all edges are codified.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource discovery algorithm based on hierarchical model and Conscious search in Grid computing system

Nickbakhsh¹,

Aghaei²

2017

Journal of Soft Computing and Applications

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The distributed system of Grid subscribes the non-homogenous sources at a vast level in a dynamic manner. The resource discovery manner is very influential on the efficiency and of quality the system functionality. The "Bitmap" model is based on the hierarchical and conscious search model that allows for less traffic and low number of messages in relation to other methods in this respect. This proposed method is based on the hierarchical and conscious search model that enhances the "Bitmap" method with the objective to reduce traffic, reduce the load of resource management processing, reduce the number of emerged messages due to resource discovery and increase the resource according speed. The proposed method and the "Bitmap" method are simulated through Arena tool. This proposed model is abbreviated as "RNTL".

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“…In this work, we use a weighted tree for resource discovery (Khanli & Kargar, 2011). We only use one bitmap for the identification of available resources in nodes and also resources of children and their descendant nodes.…”

Section: Introductionmentioning

confidence: 99%