Mirage+: a kernel implementation of distributed shared memory on a network of personal computers
Software—Practice & Experience
Shasta: a low overhead, software-only approach for supporting fine-grain shared memory
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Efficient scheduling of MPI applications on networks of workstations
Future Generation Computer Systems
Programming and Deploying Java Mobile Agents Aglets
Programming and Deploying Java Mobile Agents Aglets
Improving Load Balancing in an MPI Environment with Resource Management
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
MASIF: The OMG Mobile Agent System Interoperability Facility
MA '98 Proceedings of the Second International Workshop on Mobile Agents
Mobile agents: the next generation in distributed computing
PAS '97 Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis
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In this article we present an improvement on the loadba lancing of a parallel cluster environment, considering the MPI parallel programming paradigm and employing a mobile agent system. Our approach is to apply the mobile agent technology to provide a better scheduling, which couldre present in a cluster configuration an enhancement on the loadb alancing. MPI in cluster of heterogeneous machines could leadp arallel programmers to obtain frustrated results, mainly because of the lack of an even distribution of the workload in the cluster. As a result, before submitting a MPI application to a cluster, we use the Aglets mobile agent package to acquire a more precise information of machines' workload. Therefore, with a more precise knowledge of the load (and characteristics) in each machine, we are ready to gather lightweight workstations to form a cluster. Our empirical results indicates that it is possible to spendless elapsed time when considering the execution of a parallel application using the agent approach in comparison to an ordinary MPI environment.