Boosting gLite with cloud augmented volunteer computing

Kovács, József; Marosi, Attila Csaba; Visegrádi, Ádám; Farkas, Zoltán; Kacsuk, Péter; Lovas, Róbert

doi:10.1016/j.future.2014.10.005

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Although we do not change or optimize any BOINC scheduling policies ourselves, we do benefit from any such optimizations that already exist, especially ones targeted at relatively short tasks. Complementary strategies have also been described, such as one that uses a cloud of dedicated resources to process the small fraction of tasks that do not complete on a volunteer grid in a reasonable amount of time [27]. Here we take the current BOINC scheduling policies as a given and demonstrate that reducing workunit runtimes leads to faster turnaround time for analysis batches.…”

Section: Benefits Of Fixed-length Analysesmentioning

confidence: 97%

Subdividing Long-Running, Variable-Length Analyses Into Short, Fixed-Length BOINC Workunits

Bazinet

Cummings

2015

J Grid Computing

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We describe a scheme for subdividing longrunning, variable-length analyses into short, fixedlength BOINC workunits using phylogenetic analyses as an example. Fixed-length workunits decrease variance in analysis runtime, improve overall system throughput, and make BOINC a more useful resource for analyses that require a relatively fast turnaround time, such as the phylogenetic analyses submitted by users of the GARLI web service at molecularevolution.org. Additionally, we explain why these changes will benefit volunteers who contribute their processing power to BOINC projects, such as the Lattice BOINC Project (boinc.umiacs.umd.edu). Our results, which demonstrate the advantages of relatively short workunits, should be of general interest to anyone who develops and deploys an application on the BOINC platform.

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Section: Benefits Of Fixed-length Analysesmentioning

confidence: 97%

Subdividing Long-Running, Variable-Length Analyses Into Short, Fixed-Length BOINC Workunits

Bazinet

Cummings

2015

J Grid Computing

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“…The recent 3G-Bridge [37] implementations are able to maintain a BOINC server with Clients (the pilots) running in provisioned VMs. The work is focused on the customisation and the contextualisation needed to accomplish the common user-tasks in the Clients.…”

Section: Pulling Based Systemsmentioning

confidence: 99%

“…In this work, these tools are denoted as suppliers. They repeatedly appear through the related work [28,29,30,31,32,33,34,35,36,37].…”

Section: Brokering Approachesmentioning

confidence: 99%

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Scheduling multiple virtual environments in cloud federations for distributed calculations

Montero

Huedo

Mayo-García

2017

Future Generation Computer Systems

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“…On the other hand, the provisioning mechanisms currently available in IaaS clouds are focused on setting up VMs for the consolidation of services. However, they do not prevent running pilot jobs as temporal services (Mhashilkar et al, 2014;Luckow et al, 2015;Graciani et al, 2011;Kovács et al, 2015). Thus, many satellite programs (pilots) running inside VMs branch out to monitor a set of computational user tasks that are continuously assigned by these frameworks following the master-slave scheme.…”

Section: Resource Provisioning With Pilot Jobsmentioning

confidence: 99%

A simple model to exploit reliable algorithms in cloud federations

2016

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Exploiting resources belonging to multiple cloud providers in an efficient way is still an open issue for distributed computing. Scheduling algorithms based on heuristic, probabilistic, queue theory, or complex soft computing methods are suitable to tackle the heterogeneity and dynamism present in cloud federations. Nevertheless, the available brokering tools are focused on the deployment of services on-demand. The systems able to accomplish high throughput calculations, such as the pilot-job systems, do not support the inclusion of these algorithms due to their lack of adaptability. The recently implementation of cloud drivers for the GWpilot framework allows developers to profit from its flexibility, compatibility and scheduling features. Moreover, the framework allows the personalised characterisation of cloud resources that those algorithms require, overcoming their lack of trustworthiness in the information provided by the cloud services. In this work, a simple model together with a methodology to couple scheduling software with GWpilot is presented. To demonstrate the suitability of the approach, a legacy self-scheduler specialised on reliable executions in dynamic environments has been stacked and tested on the EGI FedCloud infrastructure with the Nagano legacy application.IaaS cloud providers are heterogeneous and their real availability dynamically changes through the time (Foster et al, 2008). Thus, the allocation of virtual machines (VMs) across multiple providers is a NP-complete problem (Garey and Johnson, 1979) that can be tackled with the sub-optimal approaches that soft computing or the operative research (Pinedo, 2005) can provide. For example, meta-heuristics (Gómez-Iglesias et al, 2010), evolutionary algorithms (Nesmachnow et al, 2010), or fuzzy logic (Saleh, 2013) have demonstrated their suitability for

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Boosting gLite with cloud augmented volunteer computing

Cited by 12 publications

References 15 publications

Subdividing Long-Running, Variable-Length Analyses Into Short, Fixed-Length BOINC Workunits

Subdividing Long-Running, Variable-Length Analyses Into Short, Fixed-Length BOINC Workunits

Scheduling multiple virtual environments in cloud federations for distributed calculations

A simple model to exploit reliable algorithms in cloud federations

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