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Increasing the amount of work completed by volunteer computing projects with task distribution policies
Abstract
Volunteer computing projects rely on volunteers running clients on their computers that contribute to projects when the computers' owners allow them to. These projects allow people to solve problems that were previously too computationally intensive to solve. However, due to the relatively small fraction of the population that participates in volunteer computing projects, it's very important to use the donated CPU cycles as efficiently as possible. Volunteer computing clients use two different methods to retrieve tasks: retrieving one task at a time when the client has no more work to do or retrieving multiple tasks at once and storing them in a buffer. We simulate these different task retrieval policies to compare the number of tasks completed by clients using the different policies. Our simulations showed that clients that retrieve one task at a time complete more tasks than clients that retrieve multiple tasks at once and buffer them. Our simulations also showed that there was not a significant gain in the amount of work that could be completed by devising a more complicated adaptive policy.
... The foremost challenge is proper distribution of tasks according to the capacity of each volunteer in order to achieve timely completion of jobs. Further, this would ensure efficient utilization of resources, reduced cost of operation, and enhanced user satisfaction (Anderson et al., 2005;Toth and Finkel, 2008). Heterogeneity-aware distribution of workload would also permit priority-based execution of tasks and improve real time behavior. ...
... Various task scheduling policies such as earliest-deadline-first, work fetch, buffer multiple tasks, weighted round robin, work send and job completion estimation policies have already been practiced in VC (Toth and Finkel, 2009;Toth, 2008;Toth and Finkel, 2008;Kondo et al., 2007;Anderson, 2011). However, most of these policies are used to reduce the overall execution time of parallel computation, without consideration of the correctness of computational results. ...
... Different middleware platforms were designed to build VC projects. However, middleware platforms like BOINC(Berkeley Open Infrastructure for Network Computing) (Volunteer computing, 2013;Yi et al., 2010;Estrada et al., 2006) and Xtremweb continued to move forward, with BOINC having become the leading framework to build volunteer computing projects (Toth, 2008;Toth and Finkel, 2008). Due to the successful development of middleware platforms, the following projects were initiated: ...
... Most of the studies found in the literature intend to optimize the BOINC project mainly focusing on the efficient use of the computational resources like CPU, memory and disk usage [5,6]. These studies try to optimize the way the volunteer computer architectures are exploited, or how can be the computational tasks more efficiently resolved [7,8,9]. But as we can see with the current increase in the number of participating volunteers, the genareted traffic on the Boinc servers is immense and, as pointed out in this work, letting volunteers to distribute some of the load between them can a solution. ...
We propose a new peer-to-peer solution for exchanging the application code of BOINC projects between clients. Our proposed add-on is integrated into the original client-server architecture and the architecture is fully compatible with the original architecture. The proposed modifications will significantly reduce the central server's data traffic for projects where the code-data ratio is high.
... In order to do this, we developed a discrete event simulator that would use the traces we collected as input to a trace-based simulation. The information in this chapter and Chapter 6 was published in [96]. ...
Abstract The price of computers has dropped drastically overthe past years enabling many
Volunteer Computing is a kind of distributed computing that harnesses the aggregated spare computing re-
sources of volunteer devices. It provides a cheaper and greener alternative computing infrastructure that can
complement the dedicated, centralized, and expensive data centres. The aggregated idle computing resources
of devices ranging from desktop computers to routers and smart TVs are being utilized to provide the much needed computing infrastructure for compute intensive tasks such as scientific simulations and big data analysis. However, the use of Volunteer Computing is still dominated by scientific applications and only a very
small fraction of the potential volunteer nodes are participating. This paper provides a comprehensive survey of Volunteer Computing, covering key technical and operational issues such as security, task distribution, resource management, and incentive models. The paper also presents a taxonomy of Volunteer Computing
systems, together with discussions of the characteristics of specific systems in each category. In order to harness the full potentials of Volunteer Computing and make it a reliable alternative computing infrastructure for general applications, we need to improve the existing techniques and device new mechanisms. Thus, this paper also sheds light on important issues regarding the future research and development of Volunteer Computing systems with the aim of making them a viable alternative computing infrastructure.
Volunteer computing projects have been used to make significant advances in knowledge since the 1990s. These projects use
idle CPU cycles donated by people to solve computationally intensive problems in medicine, the sciences and other disciplines.
It is important to use the donated cycles as efficiently as possible because participation in volunteer computing is low and
the number of volunteer computing projects keeps increasing. Task retrieval policies, policies describing when a volunteered
computer requests additional work from a server, can have an effect on the number of wasted CPU cycles and consequently, the
number of tasks completed by clients. We present the results of simulating different task retrieval policies for clients under
realistic conditions, including clients running on computers with one single-core CPU, clients running on computers with multi-core
CPUs, and clients running on computers that are put into a power save mode by environmentally conscious owners.
Public-resource computing (PRC) projects use volunteered computational resources in order to accomplish some goal (1). Because the projects are so computationally intensive and only a small percentage of the public participates in them, improvements in efficiency are very important. One way to improve the efficiency of PRC projects is to decrease the amount of time wasted duplicating work unnecessarily. One cause of this waste is distributing tasks to clients in a sub- optimal manner. This work focuses on file-based tasks that require clients to download data files to perform the tasks. We propose a method of distributing tasks and compare the waste it produces to the waste created by the other currently used techniques.
Search for Extraterrestrial Intelligence (SETI) is a scientific area whose goal is to detect intelligent life outside Earth. One approach, known as radio SETI, uses radio telescopes to listen for narrow-bandwidth radio signals from space. Such signals are not known to occur naturally, so a detection would provide evidence of extraterrestrial technology. SETI@home uses millions of computers in homes and offices around the world to analyze radio signals from space. This article describes the design and implementation of SETI@home and discusses its relevance to future distributed systems. It is introduced as a new step in experimenting public-resource computing. To attract and keep users, a project must explain and justify its goals, providing compelling views of local and global progress. Screensaver graphics are an excellent medium for displays that also provide a form of viral marketing. Moreover, the success of public-resource computing projects has the ancillary benefit of increasing public awareness of science and democratizing, to an extent, the allocation of research resources.
Global computing achieves high throughput computing by harvesting
a very large number of unused computing resources connected to the
Internet. This parallel computing model targets a parallel architecture
defined by a very high number of nodes, poor communication performance
and continuously varying resources. The unprecedented scale of the
global computing architecture paradigm requires us to revisit many basic
issues related to parallel architecture programming models, performance
models, and class of applications or algorithms suitable for this
architecture. XtremWeb is an experimental global computing platform
dedicated to provide a tool for such studies. The paper presents the
design of XtremWeb. Two essential features of this design are
multi-applications and high-performance. Accepting multiple applications
allows institutions or enterprises to set up their own global computing
applications or experiments. High-performance is ensured by scalability,
fault tolerance, efficient scheduling and a large base of volunteer PCs.
We also present an implementation of the first global application
running on XtremWeb
Volunteer computing uses computational resources that would otherwise be unused, to solve computationally intensive projects [1]. We have collected data from several different types of computers about the durations of periods when the computers were able and unable to participate in volunteer computing projects. We found that those periods differed significantly, indicating that a single method of task distribution for a volunteer computing project may not be adequate to make the best use of the donated CPU cycles. The data we have collected will also be useful in future work, to analyze portions of volunteer computing clients in an attempt to reduce the wasted resources and increase the productivity of volunteer computing and to compare the amount of work that can be completed by using different types of volunteer computing clients.
To avoid having to restart a job from the beginning in case of random failure, it is standard practice to save periodically sufficient information to enable the job to be restarted at the previous point at which information was saved. Such points are referred to as checkpoints, and the saving of such information at these points is called checkpointing [1].
OXFORD, U.K.--
What started out as a way for SETI to plow through its piles of radio-signal data from deep space has turned into a powerful
research tool as computer users across the globe donate their screen-saver time to projects as diverse as climate-change prediction,
gravitational-wave searches, and protein folding.
BOINC (Berkeley Open Infrastructure for Network Computing) is a software system that makes it easy for scientists to create and operate public-resource computing projects. It supports diverse applications, including those with large storage or communication requirements. PC owners can participate in multiple BOINC projects, and can specify how their resources are allocated among these projects. We describe the goals of BOINC, the design issues that we confronted, and our solutions to these problems.
Mersenne Prime Search
- Gimps
GIMPS. " Mersenne Prime Search ". http://www.mersenne.org/prime.htm Accessed 7/13/05.
Folding@Home Distributed Computing
- Folding@home
Folding@Home. " Folding@Home Distributed Computing. " http://folding.stanford.edu/ Updated 2006. Accessed 10/26/06.
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