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.

中文翻译：

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志愿计算系统中雾云资源的联合 QoS 感知和经济高效的任务调度

志愿计算是一种基于 Internet 的分布式计算，其中志愿者共享他们额外的可用资源来管理大规模任务。然而，计算设备在志愿计算系统 (VCS)在处理能力、货币成本和数据传输延迟方面，它们是高度动态和异构的。为了确保两者的高服务质量 (QoS)对于不同的请求成本低，所有可用的计算资源都必须得到有效利用。任务调度是一个 NP 难题，被认为是异构 VCS 中的主要关键挑战之一。因此，在本文中，我们为 VCS 设计了两种任务调度算法，分别命名为最小CCV和最小V. 所提出算法的主要目标是联合最小化计算、通信和延迟违规成本物联网 (IoT)要求。我们广泛的模拟结果表明，所提出的算法能够比最先进的算法更有效地将任务分配给志愿雾/云资源。具体来说，与基于遗传的算法相比，我们的算法将截止日期满意度任务率提高了 99.5% 左右，并将总成本降低了 15% 到 53%。