Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms

Xu, Luhua; Qiao, Jianzhong; Lin, Shukuan; Qi, Ruihua

doi:10.3390/info10070244

Cited by 3 publications

(5 citation statements)

References 34 publications

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“…The works [22,23] have been described before, so we do not reiterate them here. Xu et al [24] proposed two algorithms with the goal of minimizing the maksepan of tasks, which is different to our goal. In summary, the existing algorithms cannot solve the dynamic task assignment problem under the disturbance of availability time, and the goals of existing algorithms are different from our algorithm, which aims to maximize the benefit of VCPs.…”

Section: Dynamic Task Assignment Algorithmsmentioning

confidence: 89%

“…For the reliability, there are also many prior works [22][23][24]; however, their optimization goals are different from ours. The works [22,23] have been described before, so we do not reiterate them here.…”

Section: Dynamic Task Assignment Algorithmsmentioning

confidence: 98%

“…Specifically, during the task assignment, nodes are not always available due to the reasons mentioned before, which makes the actual benefit to VCPs less than the expected benefit. Although the available time of a node is grouped by a large number of discrete time intervals, a statistical analysis shows that it is possible to obtain a relatively accurate availability interval using probabilistic law [22,24,45]. Based on this, in the LTA strategy, we first select the time interval when the node remains available to assign tasks, and we adopt the confidence interval proposed by Xu et al [24] to predict the time interval.…”

Section: The List-based Task Assignment (Lta) Strategymentioning

confidence: 99%

“…Although the available time of a node is grouped by a large number of discrete time intervals, a statistical analysis shows that it is possible to obtain a relatively accurate availability interval using probabilistic law [22,24,45]. Based on this, in the LTA strategy, we first select the time interval when the node remains available to assign tasks, and we adopt the confidence interval proposed by Xu et al [24] to predict the time interval. The definition of the confidence interval is as follows:…”

Section: The List-based Task Assignment (Lta) Strategymentioning

confidence: 99%

“…Motivated by this, in this paper, we extended the use of the reliability model proposed by Xu et al [24] and proposed a list-based task assignment (LTA) strategy. At the same time, we proved the advantage of LTA strategy in scheduling tasks with deadline constraints and considering the availability time of a worker being interrupted.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Qiao

Lin

et al. 2020

Symmetry

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As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

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Section: Dynamic Task Assignment Algorithmsmentioning

confidence: 89%

Section: Dynamic Task Assignment Algorithmsmentioning

confidence: 98%

Section: The List-based Task Assignment (Lta) Strategymentioning

confidence: 99%

Section: The List-based Task Assignment (Lta) Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Qiao

Lin

et al. 2020

Symmetry

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Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments

2022

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Scientific applications often require substantial amount of computing resources for running challenging jobs potentially consisting of many tasks from hundreds of thousands to even millions. As a result, many institutions collaborate to solve large-scale problems by creating virtual organizations (VOs), and integrate hundreds of thousands of geographically distributed heterogeneous computing resources. Over the past decade, VOs have been proven to be a powerful research testbed for accessing massive amount of computing resources shared by several organizations at almost no cost. However, VOs often suffer from providing exact dynamic resource information due to their scale and autonomous resource management policies. Furthermore, shared resources are inconsistent, making it difficult to accurately forecast resource capacity. An effective VO’s resource profiling and modeling system can address these problems by forecasting resource characteristics and availability. This paper presents effective resource profiling and performance prediction models including Adaptive Filter-based Online Linear Regression (AFOLR) and Adaptive Filter-based Moving Average (AFMV) based on the linear difference equation combining past predicted values and recent profiled information, which aim to support large-scale applications in distributed scientific computing environments. We performed quantitative analysis and conducted microbenchmark experiments on a real multinational shared computing platform. Our evaluation results demonstrate that the proposed prediction schemes outperform well-known common approaches in terms of accuracy, and actually can help users in a shared resource environment to run their large-scale applications by effectively forecasting various computing resource capacity and performance.

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The “Socialized Architecture”: A Software Engineering Approach for a New Cloud

Malo-Perisé

Merseguer

2022

Sustainability

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Today, the cloud means a revolution within the Internet revolution. However, an oligopoly sustaining the cloud may not be the best solution, since ethical problems such as privacy or even transferring data sovereignty could eventually happen. Our research, coined as the "socialized architecture," presents a novel disruptive approach to completely transform the cloud as we know it today. The approach follows ideas already working in the field of volunteer computing, since it tries to socialize spare computing power in the infraused hardware that institutions and normal people own. However, our solution is completely different to current ones, since it does not create hyper-specialized muscles in client machines. The solution is new since it proposes a software engineering approach for developing “socialized services”, which, leveraging an asynchronous interaction model, creates a network of lightweight microservices that can be dynamically allocated and replicated through the network. The use of state-of-the-art patterns, such as Command Query Responsibility Segregation, helps to isolate domain events and persistence needs, while an API Gateway addresses communication. All previous ideas were tested through a complete and functional proof of concept, which is a prototype called Circle implementing a social network. Circle has been useful to expose problems that need to be addressed. The results of the assessment confirm, in our view, that it is worth to start this new field of work.

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Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms

Cited by 3 publications

References 34 publications

Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments

The “Socialized Architecture”: A Software Engineering Approach for a New Cloud

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