Computer
Interfacing Condor and PVM to harness the cycles of workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
SETI@home: an experiment in public-resource computing
Communications of the ACM
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
A Grid-based Parallel Approach of the Multi-Objective Branch and Bound
PDP '07 Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing
P2P design and implementation of a parallel branch and bound algorithm for grids
International Journal of Grid and Utility Computing
A Task-Based Fault-Tolerance Mechanism to Hierarchical Master/Worker with Divisible Tasks
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Grid'BnB: a parallel branch and bound framework for grids
HiPC'07 Proceedings of the 14th international conference on High performance computing
Hierarchical master-worker skeletons
PADL'08 Proceedings of the 10th international conference on Practical aspects of declarative languages
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Well-suited to embarrassingly parallel applications, the master-worker (MW) paradigm has largely and successfully used in parallel distributed computing. Nevertheless, such a paradigm is very limited in scalability in large computational grids. A natural way to improve the scalability is to add a layer of masters between the master and the workers making a hierarchical MW (HMW). In most existing HMW frameworks and algorithms, only a single layer of masters is used, the hierarchy is statically built and the granularity of tasks is fixed. Such frameworks and algorithms are not adapted to grids which are volatile, heterogeneous and large scale environments. In this paper, we revisit the HMW paradigm to match such characteristics of grids. We propose a new dynamic adaptive multi-layer hierarchical MW (AHMW) dealing with the scalability, volatility and heterogeneity issues. The construction and deployment of the hierarchy and the task management (deployment, decomposition of work, distribution of tasks, ...) are performed in a dynamic collaborative distributed way. The framework has been applied to the parallel Branch and Bound algorithm and experimented on the Flow-Shop scheduling problem. The implementation has been performed using the ProActive grid middleware and the large experiments have been conducted using about 2000 processors from the Grid'5000 French nation-wide grid infrastructure. The results demonstrate the high scalability of the proposed approach and its efficiency in terms of deployment cost, decomposition and distribution of work and exploration time. The results show that AHMW outperforms HMW and MW in scalability and efficiency in terms of deployment and exploration time.