Volunteer Computing Systems Research Articles

Using Heuristic Search Techniques to Reduce Task Migrations in Peer-to-Peer Volunteer Computing Networks

Massive computations in today's computer applications necessitate the use of high-performance computing environments. Unfor-tunately, high costs and power management must be addressed while operating these environments. Volunteer computing (VC) enables the creation of a global network of computing devices capable of accumulating their computing power to outperform any supercomputer. VC refers to the use of underutilized computing resources donated by thousands of volunteers who want to actively participate in solving common research problems. However, VC systems experience unexpected and sudden loss of connections between volunteers' computing resources and the main server. In this case, the server must redistribute the work to new devices as they become available. This process is known as task migration, and it is already used in various volunteer frameworks to address the unavailability of computing resources. However, there is a tendency to limit the number of migrations since they are considered a technically complex and time-consuming process. In this paper, we employ heuristic search algorithms to reduce task migrations caused by loss of connections in Peer-to-Peer volunteer networks by locating an alternate network path to send output files to the server when the direct link is no longer available. The simulation results demonstrate that using a heuristic search algorithm eliminates all task migrations caused by loss of connections, resulting in less total execution time and power consumption.

Joint QoS-aware and Cost-efficient Task Scheduling for Fog-cloud Resources in a Volunteer Computing System

Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high Quality of Service (QoS) and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered as one of the main critical challenges in a heterogeneous VCS. Due to this, in this article, we design two task scheduling algorithms for VCSs, named Min-CCV and Min-V . The main goal of the proposed algorithms is jointly minimizing the computation, communication, and delay violation cost for the Internet of Things (IoT) requests. Our extensive simulation results show that proposed algorithms are able to allocate tasks to volunteer fog/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.

USAGE OF SESSION METRICS TO TASKS SCHEDULING IN BROWSER-BASED VOLUNTEER COMPUTING SYSTEMS

Context. The study is devoted to the development an adaptive method for scheduling tasks in distributed browser-based volunteer computing systems. which is constructed on the session metrics of a volunteer user. This allows server to allocate tasks to each volunteer for computing, depending on the characteristics of his computing processes. More productive clients will receive more tasks, maximizing resource utilization, reducing server load and facilitating scaling; less productive clients will receive fewer tasks, solving the problem of excessive blocking of input data.Objective. The aim of the study is increasing the efficiency of distributed browser-based volunteer computing systems by using session metrics to tasks scheduling.Method. The session metrics that determine the behavior of volunteer users are described. The indicators that characterize the progress of the computational process on user systems were introduced. The formula to calculate the amount of input data for distribution to client is proposed. It takes into account the capacity of the client's system and the tendency of processes on its system. Simulation was done using different methods to calculate the amount of input data on the basis of the existing system of volunteer computing. To evaluate the performance of such systems, the total number of user hits to the server part and the total number of calculated results were used.Results. The proposed method is applied in the existing browser-based volunteer computing system. A comparison of the results of the proposed method with the existing ones showed a decrease in server load and an increase of the number of calculated results when session metrics are used.Conclusions. The simulation confirmed the convenience and efficiency of session metrics usage to tasks scheduling. The presented approach provides scalability of distributed browser-based volunteer computing systems. The prospects of further research are to use the users’ statistics and information from their browsers to calculate the amount of input data for distribution to client.

High Performance Computing in Resource Poor Settings: An Approach based on Volunteer Computing

High Performance Computing (HPC) systems aim to solve complex computing problems (in a short amount of time) that are either too large for standard computers or would take too long. They are used to solve computational problems in many fields such as medical science (for drug discovery, breast cancer detection in images, etc.), climate science, physics, mathematical science, etc. Existing solutions such as HPC Supercomputer, HPC Cluster, HPC Cloud or HPC Grid are not adapted for resource poor settings (mainly for developing countries) because their fees are generally beyond the funding (particularly for academics) and the administrative complexity to access to HPC Grid creates a higher barrier. This paper presents an approach allowing to build a Volunteer Computing system for HPC in resource poor settings. This solution does not require any additional investment in hardware, but relies instead on voluntary machines already owned by the private users. The experiment has been made on the mathematical problem of solving the matrices multiplication using Volunteer Computing system. Given the success of this experiment, the enrollment of other volunteers has already started. The goal being to create a powerful Volunteer Computing system with the maximum number of computers.

Survey and Taxonomy of Volunteer Computing

Volunteer Computing is a kind of distributed computing that harnesses the aggregated spare computing resources 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 article provides a comprehensive survey of Volunteer Computing, covering key technical and operational issues such as security, task distribution, resource management, and incentive models. The article also presents a taxonomy of Volunteer Computing systems, together with discussions of the characteristics of specific systems in each category. 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 article 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.

Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model

This article describes how in volunteer cloud computing systems, some resources are volunteered by the hosts. These systems became more powerful and attractive because they provide a highest power computing. However, to satisfy the user requirements and the system performance in this kind of the system is a crucial challenge. In this article, the authors propose a new architecture for the volunteer cloud computing systems to allow balancing the load between volunteer clouds in a decentralized manner, and between resources inside a volunteer cloud in centralized manner. Moreover, their proposal shows more advantages: First, selecting a resource according to the user requirements and to the system performance. Second, estimating the volunteer resource failure probability by using the stochastic process Markov chain model. Experimental results using the PeerSim Simulator is established to verify the efficacy of the proposed system and promising results are obtained.

A Reliable Volunteer Computing System with Credibility-based Voting

A Reliable Volunteer Computing System with Credibility-based Voting

Understanding Collaboration in Volunteer Computing Systems

Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices' local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.

Internet-scale support for map-reduce processing

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. This limits the scalability of the solution due to its dependence on the server. Our goal is to create a fault-tolerant VC platform that supports complex applications, by using a distributed model which improves performance and reduces the burden on the server. In this paper we present VMR, a VC system able to run MapReduce applications on top of volunteer resources, spread throughout the Internet. VMR leverages users’ bandwidth through the use of inter-client communication, and uses a lightweight task validation mechanism. 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 server bandwidth use by two orders of magnitude and showing no discernible overhead.

Green flexible opportunistic computing with task consolidation and virtualization

Energy efficiency and high computing power are basic design considerations across modern-day computing solutions due to different concerns such as system performance, operational cost, and environmental issues. Desktop Grid and Volunteer Computing System (DGVCS) so called opportunistic infrastructures offer computational power at low cost focused on harvesting idle computing cycles of existing commodity computing resources. Other than allowing to customize the end user offer, virtualization is considered as one key techniques to reduce energy consumption in large-scale systems and contributes to the scalability of the system. This paper presents an energy efficient approach for opportunistic infrastructures based on task consolidation and customization of virtual machines. The experimental results with single desktops and complete computer rooms show that virtualization significantly improves the energy efficiency of opportunistic grids compared with dedicated computing systems without disturbing the end-user.

Convergence analysis of evolutionary algorithms in the presence of crash-faults and cheaters

This paper analyzes the fault-tolerance nature of Evolutionary Algorithms (EAs) when executed in a distributed environment subjected to malicious acts. More precisely, the inherent resilience of EAs against two types of failures is considered: (1) crash faults, typically due to resource volatility which lead to data loss and part of the computation loss; (2) cheating faults, a far more complex kind of fault that can be modeled as the alteration of output values produced by some or all tasks of the program being executed. This last type of failure is due to the presence of cheaters on the computing platform. Most often in Global Computing (GC) systems such as BOINC, cheaters are attracted by the various incentives provided to stimulate the volunteers to share their computing resources: cheaters typically seek to obtain rewards with little or no contribution to the system. In this paper, the Algorithm-Based Fault Tolerance (ABFT) aspects of EAs against the above types of faults is characterized. Whereas the inherent resilience of EAs has been previously observed in the literature, for the first time, a formal analysis of the impact of the considered faults over the executed EA including a proof of convergence is proposed in this article.By the variety of problems addressed by EAs, this study will hopefully promote their usage in the future developments around distributed computing platform such as Desktop Grids and Volunteer Computing Systems or Cloud systems where the resources cannot be fully trusted.

Expanding the volunteer computing scenario: A novel approach to use parallel applications on volunteer computing

Nowadays, high performance computing is being improved thanks to different platforms like clusters, grids, and volunteer computing environments. Volunteer computing is a type of distributed computing paradigm in which a large number of computers, volunteered by members of the general public, provide computing and storage resources for the execution of scientific projects.Currently, volunteer computing is used for high throughput computing, but it is not used for parallel applications based on MPI due to the difficulty in communicating among computing peers. As Sarmenta and Hirano (1999) [2] said, there are several research issues to be solved in volunteer computing platforms, one of them is ‘implementing other types of applications’. In fact, the BOINC team (the well known volunteer computing development platform) is requesting ‘MPI-like support’ for volunteer computing as one of its hot topics.This paper proposes an extension of the usual capabilities of volunteer computing and desktop grid systems by allowing the execution of parallel applications, based on MPI, on these types of platforms. The goal of this paper is to describe a method to transform and adapt MPI applications in order to allow the execution of this type of applications on volunteer computing platforms and desktop grids. The proposed approach does not require an MPI installation on the client’s side, which keeps the software to be installed on the client quite lightweight. The paper describes the prototype built, and also shows the evaluation made of this prototype, in order to test the viability of the idea proposed in this article, with promising results.

Optimizing Data Distribution in Volunteer Computing Systems using Resources of Participants

Many scientific projects use BOINC middleware to build a volunteer computing project. BOINC uses centralized data servers to distribute data to its users and some projects can attract thousands of participants. Such large numbers of users coupled with large datasets can cause a bottleneck for the centralized organization of the BOINC data servers, which has a knock-on effect on the performance of the project as a whole by limiting the throughput of jobs. Alternative methods have been proposed, such as the Attic file system, which decentralize data distribution to BOINC participants. This has been shown to scale but does not attempt to optimize the use of the various distributed data centres being used. We describe performance techniques based on trust algorithms that when layered on the Attic file system can significantly improve data availability and access time through intelligent selection of the data center for each user, based on the optimization of three parameters: trust, current connection speed and availability.

Expected-Credibility-Based Job Scheduling for Reliable Volunteer Computing

This paper presents a proposal of an expected-credibility-based job scheduling method for volunteer computing (VC) systems with malicious participants who return erroneous results. Credibility-based voting is a promising approach to guaranteeing the computational correctness of VC systems. However, it relies on a simple round-robin job scheduling method that does not consider the jobs' order of execution, thereby resulting in numerous unnecessary job allocations and performance degradation of VC systems. To improve the performance of VC systems, the proposed job scheduling method selects a job to be executed prior to others dynamically based on two novel metrics: expected credibility and the expected number of results for each job. Simulation of VCs shows that the proposed method can improve the VC system performance up to 11%; It always outperforms the original round-robin method irrespective of the value of unknown parameters such as population and behavior of saboteurs.

Generalized Spot-Checking for Reliable Volunteer Computing

While volunteer computing (VC) systems reach the most powerful computing platforms, they still have the problem of guaranteeing computational correctness, due to the inherent unreliability of volunteer participants. Spot-checking technique, which checks each participant by allocating spotter jobs, is a promising approach to the validation of computation results. The current spot-checking is based on the implicit assumption that participants never distinguish spotter jobs from normal ones; however generating such spotter jobs is still an open problem. Hence, in the real VC environment where the implicit assumption does not always hold, spot-checking-based methods such as well-known credibility-based voting become almost impossible to guarantee the computational correctness. In this paper, we generalize spot-checking by introducing the idea of imperfect checking. This generalization allows to guarantee the computational correctness under the situation that spot-checking is not fully-reliable and participants may distinguish spotter jobs. Moreover, we develop a generalized formula of the credibility, which enables credibility-based voting to utilize check-by-voting technique. Simulation results show that check-by-voting improves the performance of credibility-based voting, while guaranteeing the same level of computational correctness.

Optimal Spot-checking for Computation Time Minimization in Volunteer Computing

This paper proposes an optimization technique for spot-checking to minimize the computation time in volunteer computing (VC) systems with non-reliable participants. Credibility-based voting with spot-checking is a promising approach to the high-performance and reliable VC systems. In this approach, spot-check rate has a significant impact on the performance, which must be set before the computation. Therefore, the estimation of the optimal spot-check rate is the major concern to minimize the computation time. The key idea for the estimation is to represent the mathematical expectation of the computation time as a function of spot-check rate. Extensive simulation has shown that the proposed technique always obtains an approximate estimate of the optimal spot-check rate and minimizes the computation time with an uncertainty of 1%.