Algorithms for clustering data
Algorithms for clustering data
Pattern-oriented software architecture: a system of patterns
Pattern-oriented software architecture: a system of patterns
Component software: beyond object-oriented programming
Component software: beyond object-oriented programming
Software product lines: practices and patterns
Software product lines: practices and patterns
Pattern-Oriented Software Architecture: Patterns for Concurrent and Networked Objects
Pattern-Oriented Software Architecture: Patterns for Concurrent and Networked Objects
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Architectural Mismatch: Why Reuse Is So Hard
IEEE Software
Architectural Mismatch: Why Reuse Is So Hard
IEEE Software
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Grid Information Services for Distributed Resource Sharing
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Matchmaking frameworks for distributed resource management
Matchmaking frameworks for distributed resource management
Pattern-Oriented Software Architecture: Patterns for Resource Management
Pattern-Oriented Software Architecture: Patterns for Resource Management
Ten actions when Grid scheduling: the user as a Grid scheduler
Grid resource management
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Software—Practice & Experience
Peer-to-Peer resource discovery in Grids: Models and systems
Future Generation Computer Systems
Agent-Based Resource Discovery and Selection for Dynamic Grids
WETICE '06 Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Elastic Components: Addressing Variance of Quality Properties in Components
EUROMICRO '07 Proceedings of the 33rd EUROMICRO Conference on Software Engineering and Advanced Applications
On Resource Clustering Techniques for Grid Resource Discovery
WETICE '07 Proceedings of the 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Quality Interplay in Regular vs. Irregular Grid Topologies
WETICE '08 Proceedings of the 2008 IEEE 17th Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Brain Meets Brawn: Why Grid and Agents Need Each Other
Proceedings of the 2005 conference on Towards the Learning Grid: Advances in Human Learning Services
Grid resource discovery based on semantically linked virtual organizations
Future Generation Computer Systems
On the load distribution and performance of meta-computing systems
ISPDC'03 Proceedings of the Second international conference on Parallel and distributed computing
A survey of economic models in grid computing
Future Generation Computer Systems
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Grids provide access to a vast amount of computational resources for the execution of demanding computations. These resources are geographically distributed, owned by different organizations and are vastly heterogeneous. The aforementioned factors introduce uncertainty in all phases of a Grid Scheduling Process (GSP). This work describes a synergistic multidisciplinary approach which aims at addressing this uncertainty. It proposes a network of resource representatives (RRs), which maintain the more or less static characteristics of available workers they represent. Clustering techniques are used for the efficient searching in the network of RRs by client agents. After the discovery of possibly suitable resources, client agents and resource agents negotiate directly for the selection of the best available resource set. Finally, according to the characteristics of the selected resource set and its current state, we propose a component-based application configuration approach based on component variants, that adjusts the application for the forthcoming execution phase in the selected resource set. We evaluate our approach using simulation and we show that it outperforms centralized index approaches for large computational grids.