Volunteer computing: requirements, challenges, and solutions

Durrani, Muhammad; Shamsi, Jawwad Ahmed

doi:10.1016/j.jnca.2013.07.006

Cited by 69 publications

(14 citation statements)

References 25 publications

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“…According to [5,64], VC follows mostly a master-worker parallel computing model as shown in Figure 6a. In this model, the master decomposes massive tasks into small chunks and distributes these small chunks among workers.…”

Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

“…Middleware (see Figure 6b) handles all operations of a VC system. These operations compromise splitting tasks into small chunks, scheduling these chunks and aggregating the chunk results, and protecting privacy and security of VC computing devices [64]. However, it is also essential to process truthfully.…”

Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

“…Thus, claimed applications and claimed work done by the middleware of a project should be transparently stated. This truthfulness includes addressing questions like possible economic exploitation, expected results, the surrender of intellectual property, and similar aspects [64]. For example, a VC project should not claim to research about cancer but do crypto-mining instead.…”

Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

“…The COVID-19 case (Folding@home), recent reviews [5,64], and the critical discussion of the current state of VC turned up the following (not necessarily complete) shortcomings of current VC platforms.…”

Section: Open Problems In Volunteer Computingmentioning

confidence: 99%

“…• Heterogeneity [64]: Different VC devices have different power, memory and processing capabilities, as well as different communication interfaces, making it hard to classify, design, and assign device optimized work units.…”

Section: Open Problems In Volunteer Computingmentioning

confidence: 99%

See 4 more Smart Citations

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

Kratzke

2020

Future Internet

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From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer had significant idle times. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.

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Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

Section: Reference Model Of Volunteer Computingmentioning

confidence: 99%

Section: Open Problems In Volunteer Computingmentioning

confidence: 99%

Section: Open Problems In Volunteer Computingmentioning

confidence: 99%

See 3 more Smart Citations

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

Kratzke

2020

Future Internet

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Calculating Hydrodynamic Flows

Liberge

2022

Fluid–Structure Interaction

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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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Volunteer computing: requirements, challenges, and solutions

Cited by 69 publications

References 25 publications

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

Calculating Hydrodynamic Flows

A new volunteer computing model for data‐intensive applications

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