Big Data Processing by Volunteer Computing Supported by Intelligent Agents

Balicki, Jerzy; Korłub, Waldemar; Paluszak, Jacek

doi:10.1007/978-3-319-19941-2_26

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Cited by 3 publications

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“…The use of VC systems for Big Data processing has been studied in [34]. In this article, the authors describe an architecture of intelligent agents to optimize Big Data processing.…”

Section: Data Center Owners Of Cloud Service Providersmentioning

confidence: 99%

A heterogeneous mobile cloud computing model for hybrid clouds

Monsalve

Garcı́a-Carballeira

Calderón

2018

Future Generation Computer Systems

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“…The use of VC systems for Big Data processing has been studied in [34]. In this article, the authors describe an architecture of intelligent agents to optimize Big Data processing.…”

Section: Data Center Owners Of Cloud Service Providersmentioning

confidence: 99%

A heterogeneous mobile cloud computing model for hybrid clouds

Monsalve

Garcı́a-Carballeira

Calderón

2018

Future Generation Computer Systems

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“…In fact, the solution described in Czarnul et al proposes a multi‐level VC architecture, and it is similar to the approach introduced in Calderón et al, since both have volunteer users computing parallel executions. The use of VC systems for Big Data processing has been studied in Balicki et al In this article, the authors describe an architecture of intelligent agents to optimize Big Data processing. In Bruno and Ferreira, the authors present a VC solution called FreeCycles, which supports MapReduce jobs.…”

Section: Background and Related Workmentioning

confidence: 99%

A new volunteer computing model for data‐intensive applications

Monsalve

Garcı́a-Carballeira

Calderón

2017

Concurrency and Computation

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Summary Volunteer computing is a type of distributed computing in which ordinary people donate computing resources to scientific projects. BOINC is the main middleware system for this type of distributed computing. The aim of volunteer computing is that organizations be able to attain large computing power thanks to the participation of volunteer clients instead of a high investment in infrastructure. There are projects, like the ATLAS@Home project, in which the number of running jobs has reached a plateau, due to a high load on data servers caused by file transfer. This is why we have designed an alternative, using the same BOINC infrastructure, in order to improve the performance of BOINC projects that have reached their limit due to the I/O bottleneck in data servers. This alternative involves having a percentage of the volunteer clients running as data servers, called data volunteers, that improve the performance of the system by reducing the load on data servers. In addition, our solution takes advantage of data locality, leveraging the low network latencies of closer machines. This paper describes our alternative in detail and shows the performance of the solution, applied to 3 different BOINC projects, using a simulator of our own, ComBoS.

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