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Register a new userPando: Personal Volunteer Computing in Browsers
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Added by
Erick Lavoie
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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The large penetration and continued growth in ownership of personal electronic devices represents a freely available and largely untapped source of computing power. To leverage those, we present Pando, a new volunteer computing tool based on a declarative concurrent programming model and implemented using JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying number of failure-prone personal devices contributed by volunteers to parallelize the application of a function on a stream of values, by using the devices browsers. We show that Pando can provide throughput improvements compared to a single personal device, on a variety of compute-bound applications including animation rendering and image processing. We also show the flexibility of our approach by deploying Pando on personal devices connected over a local network, on Grid5000, a French-wide computing grid in a virtual private network, and seven PlanetLab nodes distributed in a wide area network over Europe.
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Volunteer computing is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe its features, architecture, and implementation.
Volunteer Computing, sometimes called Public Resource Computing, is an emerging computational model that is very suitable for work-pooled parallel processing. As more complex grid applications make use of work flows in their design and deployment it is reasonable to consider the impact of work flow deployment over a Volunteer Computing infrastructure. In this case, the inter work flow I/O can lead to a significant increase in I/O demands at the work pool server. A possible solution is the use of a Peer-to- Peer based parallel computing architecture to off-load this I/O demand to the workers; where the workers can fulfill some aspects of work flow coordination and I/O checking, etc. However, achieving robustness in such a large scale system is a challenging hurdle towards the decentralized execution of work flows and general parallel processes. To increase robustness, we propose and show the merits of using an adaptive checkpoint scheme that efficiently checkpoints the status of the parallel processes according to the estimation of relevant network and peer parameters. Our scheme uses statistical data observed during runtime to dynamically make checkpoint decisions in a completely de- centralized manner. The results of simulation show support for our proposed approach in terms of reduced required runtime.
Einstein@Home aggregates the computer power of hundreds of thousands of volunteers from 192 countries to mine large data sets. It has now found a 40.8 Hz isolated pulsar in radio survey data from the Arecibo Observatory taken in February 2007. Additional timing observations indicate that this pulsar is likely a disrupted recycled pulsar. PSR J2007+2722s pulse profile is remarkably wide with emission over almost the entire spin period; the pulsar likely has closely aligned magnetic and spin axes. The massive computing power provided by volunteers should enable many more such discoveries.
The mcplots.cern.ch web site (MCPLOTS) provides a simple online repository of plots made with high-energy-physics event generators, comparing them to a wide variety of experimental data. The repository is based on the HEPDATA online database of experimental results and on the RIVET Monte Carlo analysis tool. The repository is continually updated and relies on computing power donated by volunteers, via the LHC@HOME platform.
The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.
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