Volunteer Computing

In this paper, we discuss how Peer-to-Peer data distribution techniques can be adapted to Desktop Grid computing environments, particularly to the BOINC platform. To date, Desktop Grid systems have focused primarily on utilizing spare CPU cycles, yet have neglected to take advantage of client network capabilities. Leveraging client bandwidth will not only benefit current projects by lowering their overheads but will also facilitate Destkop Grid adoption by data-heavy applications. In this paper, weoutline two approaches to Peer-to-Peer data sharing that could be adapted for volunteer computing platforms: the highly successful BitTorrent protocol; and, a more complex, yet secure and customizable, hierarchal Peer-to-Peer data center approach.

Volunteer computing is a form of network based distributed computing, which allows public participants to share their idle computing resources, and helps run computationally expensive projects. Many existing volunteer computing platforms consist of millions of users, providing huge amount of memory and processing. Since the rapid growth in the volunteer computing projects, more researchers have been attracted to study and improve the existing volunteer computing system. However, the progress of concurrently running projects has slowed down due to the increasing competition of volunteers. Moreover, because of high computational needs and low participation rate of volunteers, attracting more volunteers and using their resources more efficiently have become extremely important, if volunteer computing is to remain a feasible method. In order to competently use the huge number of volunteered resources, workers' analysis and efficient task retrieval policies are important. The purpose of this paper is to assess the strengths and requirements of current volunteer computing platforms. The paper analyses different issues relating to volunteer computing such as analysis of workers, the effectiveness of workers, how their communication and computation can be modeled and how the effectiveness of task distribution and results verification policies are analyzed. At the end, some research directions in the form of partial results, and their intermediate verification have been shown, which may improve the performance of the overall system. Moreover, this survey will enable the research community to study the available schemes used in volunteer computing and help them fill gaps in existing research.

In this paper we report on our work-in-progress on a new job description language intended for use in a browser-based voluntary computing platform. The language has workflow-control features, will enable the automatic data distribution and allows job creators to react to special events and failures.

Cloud computing provides an approach to accessing shared computing resources. However, a traditional cloud is composed of powerful but energy-hungry workstations. The growth of the population of mobile devices such as smart phones and tablets provides huge amount of idling computing power. In this paper, we describe the design and implementation of a mobile computing system prototype named GEMCloud that utilizes energy efficient mobile devices (e.g., smartphones and tablets) as computing resources. We evaluate the computing power and energy efficiency of the mobile devices through comprehensive experiments. The results show that a cloud computing system with enough mobile devices working cooperatively is able to save 55% to 98% of the energy consumption of conventional serverbased clouds while providing comparable computing speed.

The SETI@home project has recently completed its third year of active data analysis. Over 4 million volunteers have joined the search, providing a combined total of over 1 million CPU-years of processing power. SETI@home performs a sensitive search for extraterrestrial signals in a 2.5 MHz band centered on 1420 MHz. SETI@home searches a wide parameter space including 14 octaves of signal bandwidth and 15 octaves of pulse period with Doppler drift corrections from -50 Hz/s to +50 Hz/s. We will briefly describe the SETI@home project and the algorithms used in the SETI@home client. We will describe the post-processing methods we use to reject RFI and select candidate signals from the nearly 4 billion ``hits'' returned by SETI@home clients.

There is an increasing demand for assessing the value of volunteering. This trend is connected with using of plenty of methods, offering significantly various results. Even though there are qualified estimates, there are all point estimates. These estimates are not taking into account the deviation apart from the chosen replacement wage. The article wants to emphasize, that the deviation of these estimates compared to real value, could be significant. That it is the reason why it is coming with the concept of interval representation. The interval range of the economic value of volunteering. It can help us to understand that we do not know the exact value of volunteering. The ones who are computing the point should be aware of that. Choosing the replacement wages can influence the value of volunteering significantly. The article is showing how different methods can be used, compared and how interpret theirs results.

Cycle sharing over the Internet has increased in popularity during the last decade, with increasingly powerful machines being made available to existing projects. In this paper, we present GiGi-MR, a framework that allows non-expert users to run CPU-intensive jobs on top of volunteer resources over the Internet. GiGi-MR has several distinctive features: it allows non-expert users to easily partition their jobs in several parallel tasks; such Bag-of-Tasks (BoT) are executed in parallel as a set of MapReduce applications; the volunteer resources that provide the best match for the tasks being executed are chosen (using attenuated bloom filters); it provides a portable checkpointing fault-tolerance mechanism based on virtualization; it does not rely exclusively on a central server (or servers) at all times (thus minimizing the bottleneck effect); it deals with malicious participants (possibly byzantine) using an efficient partial replication mechanism to validate the results obtained; and it is compatible with BOINC (one of the most popular open-source software platforms for computing using volunteered resources). We describe GiGi-MR’s architecture and evaluate its performance by executing several MapReduce applications on a wide area testbed. Furthermore, we use micro-benchmarks to assess each one of GiGi-MR’s components independently. The system’s overhead is minimal. When compared to an unmodified volunteer computing system, GiGi-MR obtains a performance increase of over 60 % in application turnaround time, while reducing the bandwidth used by an order of magnitude.

Grid computing technology improves the computing experiences at organizations by effectively integrating distributed computing resources. However, just a small fraction of currently available Grid infrastructures focuses on reutilization of existing commodity computing resources. This paper introduces InteGrade, a novel objectoriented middleware Grid infrastructure that focuses on leveraging the idle computing power of shared desktop machines. Its features include support for a wide range of parallel applications and mechanisms to assure that the owners of shared resources do not perceive any loss in the quality of service. A prototype implementation is under construction and the current version is available for download.

Volunteer Computing systems (VC) harness computing resources of machines from around the world to perform distributed independent tasks. Existing infrastructures follow a master/worker model, with a centralized architecture, which limits the scalability of the solution given its dependence on the server. We intend to create a distributed model, in order to improve performance and reduce the burden on the server.

In this paper we present VMR, a VC system able to run MapReduce applications on top of volunteer resources, over the large scale Internet. We describe VMR's architecture and evaluate its performance by executing several MapReduce applications on a wide area testbed.
Our results show that VMR successfully runs MapReduce tasks over the Internet. When compared to an unmodified VC system, VMR obtains a performance increase of over 60% in application turnaround time, while reducing the bandwidth use by an order of magnitude.

In this paper we show how we applied BitTorrent data distribution techniques to the BOINC middleware. Our goal was to decentralize BOINC’s data model to take advantage of client network capabilities. To achieve this, we developed a prototype that adds BitTorrent functionality for task distribution and conducted small-scale tests of the environment. Additionally, we measured the impact of the BitTorrent components in both the BOINC client and server, and compared it with the original implementation. Our preliminary results indicate that the BitTorrent client had a negligible influence on the BOINC client’s computation time, even in the case where it was seeding extensively. The BOINC server, on the contrary, showed an unexpectedly low bandwidth output when seeding the file, as well as spikes on CPU usage. Current results show the BitTorrent scenario allows clients to share the burden of data distribution on BOINC with almost no negative influence on compute time. This paper will discuss the tests that were performed, how they were evaluated, as well as some improvements that could be made to future tests to enhance server-side efficiency.