Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Machine Learning
C++ Network Programming: Systematic Reuse with ACE and Frameworks, Vol. 2
C++ Network Programming: Systematic Reuse with ACE and Frameworks, Vol. 2
Optimal Resource-Aware Deployment Planning for Component-Based Distributed Applications
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Improving grid resource allocation via integrated selection and binding
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Performance modeling and system management for multi-component online services
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
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The component placement problem involves mapping a component to a particular location and maximising component utility in grid and cloud systems. It is also an NP hard resource allocation and deployment problem, so many common grid and cloud computing libraries, such as MPICH and Hadoop, do not address this problem, even though large performance gains can occur by optimising communications between nodes. This paper provides four contributions to research on the component placement problem for grid and cloud computing environments. First, we present the multi-agent distributed adaptive resource allocation (MADARA) toolkit, which is designed to address grid and cloud allocation and deployment needs. Second, we present a heuristic called the comparison-based iteration by degree (CID) heuristic, which we use to approximate optimal deployments in MADARA. Third, we analyse the performance of applying the CID heuristic to approximate common grid and cloud operations, such as broadcast, gather and reduce. Fourth, we evaluate the results of applying genetic programming mutation to improve our CID heuristic.