Abstract — In a grid-computing environment, resource selection and scheduling depend on the network topology connecting the computation nodes. This paper presents a method to hierarchically group compute nodes distributed across the internet into logical clusters, and determine the relative location of the clusters. At inter-domain level, distance from landmarks (a small group of distributed reference nodes) is the basis for converting the location of nodes inside a complex network structure onto a simple geometric space. The position of compute nodes in this geometric space is the basis for partitioning nodes into clusters. For compute nodes within an administrative domain, minimum RTT is used as the metric to partition nodes into clusters...
Clustering algorithms help to understand the hidden information present in datasets. A dataset may c...
Clustering is one of the most important analysis tasks in spatial databases. We study the problem of...
Large quantities of location-sensing data are generated from location-based social network services....
Abstract. Hierarchical approaches, where nodes are clustered based on their net-work distances, have...
We present a new, easy to understand algorithm and programming environment allowing for the interact...
In large-scale computational Grids, discovery of heterogeneous resources as a working group is cruci...
Clustering is an essential way to extract meaningful information from massive data without human int...
Recent research has shown that spatial clustering features have presented in many large scale distri...
This paper deals with graph clustering algorithm which partitions a set of vertices in graphs into s...
Abstract. The most commonly used method to tackle the graph partitioning problem in practice is the ...
peer reviewedHierarchical approaches, where nodes are clustered based on their network distances, ha...
Selecting nodes based on their position in the network is a basic building block for many distribut...
International audienceThis article presents a novel method for computing distances between hosts in ...
http://www.springerlink.com/index/TTJJL61R1EXDLCMCInternational audienceRecently, many works focus o...
Clustering is one of the most important analysis tasks in spatial databases. We study the problem of...
Clustering algorithms help to understand the hidden information present in datasets. A dataset may c...
Clustering is one of the most important analysis tasks in spatial databases. We study the problem of...
Large quantities of location-sensing data are generated from location-based social network services....
Abstract. Hierarchical approaches, where nodes are clustered based on their net-work distances, have...
We present a new, easy to understand algorithm and programming environment allowing for the interact...
In large-scale computational Grids, discovery of heterogeneous resources as a working group is cruci...
Clustering is an essential way to extract meaningful information from massive data without human int...
Recent research has shown that spatial clustering features have presented in many large scale distri...
This paper deals with graph clustering algorithm which partitions a set of vertices in graphs into s...
Abstract. The most commonly used method to tackle the graph partitioning problem in practice is the ...
peer reviewedHierarchical approaches, where nodes are clustered based on their network distances, ha...
Selecting nodes based on their position in the network is a basic building block for many distribut...
International audienceThis article presents a novel method for computing distances between hosts in ...
http://www.springerlink.com/index/TTJJL61R1EXDLCMCInternational audienceRecently, many works focus o...
Clustering is one of the most important analysis tasks in spatial databases. We study the problem of...
Clustering algorithms help to understand the hidden information present in datasets. A dataset may c...
Clustering is one of the most important analysis tasks in spatial databases. We study the problem of...
Large quantities of location-sensing data are generated from location-based social network services....