Profile-Driven Component Placement for Cluster-Based Online Services
IEEE Distributed Systems Online
Optimal Resource-Aware Deployment Planning for Component-Based Distributed Applications
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
A Style-Aware Architectural Middleware for Resource-Constrained, Distributed Systems
IEEE Transactions on Software Engineering
Constraint-Based deployment of distributed components in a dynamic network
ARCS'06 Proceedings of the 19th international conference on Architecture of Computing Systems
Adaptive component allocation in scudware middleware for ubiquitous computing
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Improving availability in large, distributed component-based systems via redeployment
CD'05 Proceedings of the Third international working conference on Component Deployment
A decentralized redeployment algorithm for improving the availability of distributed systems
CD'05 Proceedings of the Third international working conference on Component Deployment
CBay: encheres pour le redéploiement de composants sur l'internet des machines
Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
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Smart item environments consist of networked nodes with heterogeneous hardware equipment and intermittent network connections. Using a common component technology allows for flexible distribution of components for processing of smart item data. Finding a good deployment plan for a new set of components in an infrastructure is called Component Placement Problem. We propose an approach for finding suitable deployment plans for components with special regard to the characteristics of smart item environments. Our method evaluates deployment plans in terms of both resource consumption and availability. From the analysis of the solution space we conclude that the number of network link uses is an important criterion for the quality of a deployment plan regarding both cost and availability. Based on this finding, we have derived a heuristic that creates deployment plans, which have a low number of link uses and are thus more likely of high quality.