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PF-BTS: A Privacy-Aware Fog-enhanced Blockchain-assisted task scheduling

机译：PF-BTS：一个隐私感知的雾增强区块链辅助任务调度

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摘要

In recent years, the deployment of Cloud Computing (CC) has become more popular both in research and industry applications, arising form various fields including e-health, manufacturing, logistics and social networking. This is due to the easiness of service deployment and data management, and the unlimited provision of virtual resources (VR). In simple scenarios, users/applications send computational or storage tasks to be executed in the cloud, by manually assigning those tasks to the available computational resources. In complex scenarios, such as a smart city applications, where there is a large number of tasks, VRs, or both, task scheduling is exposed as an NP-Hard problem. Consequently, it is preferred and more efficient in terms of time and effort, to use a task scheduling automation technique. As there are many automated scheduling solutions proposed, new possibilities arise with the advent of Fog Computing (FC) and Blockchain (BC) technologies. Accordingly, such automation techniques may help the quick, secure and efficient assignment of tasks to the available VRs. In this paper, we propose an Ant Colony Optimization (ACO) algorithm in a Fog-enabled Blockchain-assisted scheduling model, namely PF-BTS. The protocol and algorithms of PF-BTS exploit BC miners for generating efficient assignment of tasks to be performed in the cloud's VRs using ACO, and award miner nodes for their contribution in generating the best schedule. In our proposal, PF-BTS further allows the fog to process, manage, and perform the tasks to enhance latency measures. While this processing and managing is taking place, the fog is enforced to respect the privacy of system components, and assure that data, location, identity, and usage information are not exposed. We evaluate and compare PF-BTS performance, with a recently proposed Blockchain-based task scheduling protocol, in a simulated environment Our evaluation and experiments show high privacy awareness of PF-BTS, along with noticeable enhancement in execution time and network load.
机译：近年来，云计算（CC）部署在研究和行业应用中都变得更加流行，而在包括电子健康，制造，物流和社交网络，包括电子健康，制造，物流和社交网络。这是由于服务部署和数据管理的容易性，以及无限制提供虚拟资源（VR）。在简单的方案中，用户/应用程序通过手动将这些任务分配给可用的计算资源，发送要在云中执行的计算或存储任务。在复杂的场景中，例如智能城市应用程序，其中有大量任务，VRS或两者，任务调度被暴露为NP难题。因此，在时间和精力方面，优选且更有效，以使用任务调度自动化技术。由于提出了许多自动调度解决方案，因此出现了新的可能性，雾计算（FC）和区块链（BC）技术出现。因此，这种自动化技术可以帮助对可用VRS的快速，安全和有效地将任务分配给可用的VRS。在本文中，我们提出了一种在启用迷雾的区块链辅助调度模型中的蚁群优化（ACO）算法，即PF-BTS。 PF-BTS的协议和算法利用BC矿工，用于使用ACO在云的VRS中生成有效分配任务，并授予矿工节点以获得最佳计划的贡献。在我们的建议中，PF-BTS还允许FOG处理，管理和执行任务以增强延迟措施。虽然发生此处理和管理，但强制执行雾来尊重系统组件的隐私，并确保数据，位置，标识和使用信息不公开。我们评估并比较PF-BTS性能，利用最近提出的基于区块链的任务调度协议，在模拟环境中，我们的评估和实验显示了PF-BTS的高隐私意识，以及执行时间和网络负载中明显的增强。

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