Volunteer computing is an Internet-based distributed computing system 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 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 one of the main critical challenges in a heterogeneous VCS. Due to this, in this paper, 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 by around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.
翻译:志愿计算是一种基于互联网的分布式计算系统,志愿人员在其中分享管理大规模任务所需的额外可用资源。然而,自愿计算系统中的计算装置在处理能力、货币成本和传递潜伏数据方面高度动态和多样化。为了确保高服务质量和低费用满足不同要求,必须高效使用所有可用的计算资源。任务时间安排是一个NP-硬性的问题,被认为是多元VCS的主要挑战之一。由于这一点,我们为自愿计算系统设计了两个任务时间安排算法,即Min-CCV和Min-V。提议的算法的主要目标是共同尽量减少计算、通信和延迟违反Times互联网(IoT)要求的成本。我们广泛的模拟结果表明,拟议的算法能够更有效地分配用于自愿雾/库资源的任务,而不是最先进的技术。具体地说,我们的算法将最后期限满意度提高了大约99.5%,与基于遗传的算法相比,将总成本从15至53%降低到53%。