Federated clusters using the transparent remote Execution (TREx) environment

Wang, R.; Cauich, Enrique; Scherson, Isaac D.

doi:10.1109/icpads.2007.4447831

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…For showcasing of the adaptable workload, different resolutions were equalized. Resolutions of the input image varied by 1 2 , 1 4 , 1 8 , and 1 16 on execution test runs. For distributing the image, a banded, overlapping technique was used, as demonstrated in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

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Concurrent adaptive computing in heterogeneous environments (CACHE)

Duselis

Scherson

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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We introduce a computational framework for concurrent adaptive computing in heterogeneous environments for computationally intensive applications. This framework considers the presence of inter-connected computational resources which are discoverable and a workload which needs to be executed either by concurrent means or on a singular resource. The selection of resources, using a novel measurement of performance, leads to the adaptive inclusion/exclusion of resources to be used in the efficient execution of workload computations. The adaptive approach is that it makes a determination to include a proper subset of resources for system inclusion to execute a workload, which is contrary (except when the subset is allinclusive) to a greedy approach where all the resources are seized for the workload application. The selection of a subset of resources may be more efficient due to the high level of heterogeneity of the resources, where, for some resources, certain resource selections may be detrimental or have no value to send work there. Furthermore, this framework aims to lessen the unpredictability and uncontrollability of heterogeneous systems by using this analysis for resource selection.

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Section: Resultsmentioning

confidence: 99%

“…HMPI builds on the capabilities of MPI by allowing the user to define performance parameters. TREx [1] is a simple, portable environment to be used for its resource discovery and management capabilities. Piranha [14] is an adaptive environment where a master-worker relationship exists and allows for process migration.…”

Section: A Prior Workmentioning

confidence: 99%

Concurrent adaptive computing in heterogeneous environments (CACHE)

Duselis

Scherson

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

Self Cite

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“…HMPI builds on the capabilities of MPI by allowing the user to define performance parameters, while AMPI is an adaptive version which separates the mapping and parallelization of an application. TREx [16] is a simple, portable environment to be used for its resource discovery and management capabilities. Piranha [11] is an adaptive environment where a masterworker relationship exists and allows for process migration.…”

Section: Previous Workmentioning

confidence: 99%

“…The middleware distributed computing environment, resource discoverer, and resource management system used in testing was TREx [16]. A widely capable set of resources were used in testing and are shown in Table I …”

Section: Test Setup a Distributed Environment And Resourcesmentioning

confidence: 99%

Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

Duselis

Cauich

Wang

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

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This paper provides a framework for dynamic adaptive computing in heterogeneous clusters for computationally intensive applications. The framework considers a set of discoverable interconnected computational resources and either a parallel or sequential workload needing to be executed. An adaptive inclusion/exclusion algorithm is used to select the resources by using novel performance measurements and profiling techniques. Furthermore, contrary to a greedy approach where all the resources are seized for the workload application, our framework only harnesses the best fit resources measured against system-wide performance characterization, and is contingent upon the current workload definition. The intelligent selection of a subset of resources has proven to achieve better performance; especially in environments with a high level of heterogeneity where the characteristics of some resources may not achieve the best performance the cluster can provide. Additionally, this paper provides a novel analysis of the workload and cluster characteristics, exhibiting analytical starting points to be used in the resource selection.

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