Object mobility for performance improvements of parallel Java applications
Journal of Parallel and Distributed Computing - Special Issue on Java on Clusters
J-Orchestra: Automatic Java Application Partitioning
ECOOP '02 Proceedings of the 16th European Conference on Object-Oriented Programming
Adaptive Offloading for Pervasive Computing
IEEE Pervasive Computing
Software, Performance and Resource Utilisation Metrics for Context-Aware Mobile Applications
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
An Adaptive Multi-Constraint Partitioning Algorithm for Offloading in Pervasive Systems
PERCOM '06 Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications
Runtime metrics collection for middleware supported adaptation of mobile applications
Proceedings of the 5th workshop on Adaptive and reflective middleware (ARM '06)
Design and Evaluation of Large Scale Loosely Coupled Cluster-based Distributed Systems
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Adaptive application offloading using distributed abstract class graphs in mobile environments
Journal of Systems and Software
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The use of adaptive object migration strategies, to enable the execution of computationally heavy applications in pervasive computing spaces requires improvements in the efficiency and scalability of existing local adaptation algorithms. The paper proposes a distributed approach to local adaptation which reduces the need to communicate collaboration metrics, and allows for the partial distribution of adaptation decision making. The algorithm's network and memory utilization is mathematically modeled and compared to an existing approach. It is shown that under small collaboration sizes, the existing algorithm could provide up to 30% less network overheads while under large collaboration sizes the proposed approach can provide over 900% less network consumption. It is also shown that the memory complexity of the algorithm is linear in contrast to the exponential complexity of the existing approach.