EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing
EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing
复制标题
EEDTO：一种节能动态任务卸载算法，用于支持区块链的物联网-边缘-云编排计算
DOI：
10.1109/jiot.2020.3033521
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发表时间：
2021-02-15
影响因子：
10.6
通讯作者：
Xu, Minxian
中科院分区：
文献类型：
--
作者：
Wu, Huaming;Wolter, Katinka;Xu, Minxian
随着计算密集型和延迟敏感型移动的应用的激增，物联网（IoT）设备上需要具有严格延迟要求的大量计算资源。一个有前途的解决方案是将复杂的计算任务从物联网设备卸载到移动边缘计算（MEC）或移动的云计算（MCC）服务器。MEC服务器更接近物联网设备，因此延迟更低，而MCC服务器可以提供灵活和可扩展的计算能力，以支持复杂的应用程序。为了解决有限的计算能力和高延迟之间的权衡，同时确保卸载过程中的数据完整性，我们考虑了一个区块链场景，其中边缘计算和云计算可以合作实现安全的任务卸载。我们进一步提出了一个支持区块链的物联网边缘云计算架构，该架构受益于MCC和MEC，其中MEC服务器提供更低的延迟计算服务，而MCC服务器提供更强的计算能力。此外，我们开发了一种节能的动态任务卸载（EEDTO）算法，通过在线方式选择最佳计算位置，无论是在物联网设备，MEC服务器还是MCC服务器上，目标是联合最小化能耗和任务响应时间。李亚普诺夫优化技术被应用于控制不同类型的应用和无线环境的动态变化所引起的计算和通信成本。在优化过程中，每个任务的最佳计算位置被自适应地选择，而不需要未来的系统信息作为先验知识。与已有的MEC和MCC协同卸载方案相比，EEDTO算法能够以较低的计算复杂度实现节能的卸载决策。
With the proliferation of compute-intensive and delay-sensitive mobile applications, large amounts of computational resources with stringent latency requirements are required on Internet-of-Things (IoT) devices. One promising solution is to offload complex computing tasks from IoT devices either to mobile-edge computing (MEC) or mobile cloud computing (MCC) servers. MEC servers are much closer to IoT devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications. To address the tradeoff between limited computing capacity and high latency, and meanwhile, ensure the data integrity during the offloading process, we consider a blockchain scenario where edge computing and cloud computing can collaborate toward secure task offloading. We further propose a blockchain-enabled IoT-Edge-Cloud computing architecture that benefits both from MCC and MEC, where MEC servers offer lower latency computing services, while MCC servers provide stronger computation power. Moreover, we develop an energy-efficient dynamic task offloading (EEDTO) algorithm by choosing the optimal computing place in an online way, either on the IoT device, the MEC server or the MCC server with the goal of jointly minimizing the energy consumption and task response time. The Lyapunov optimization technique is applied to control computation and communication costs incurred by different types of applications and the dynamic changes of wireless environments. During the optimization, the best computing location for each task is chosen adaptively without requiring future system information as prior knowledge. Compared with previous offloading schemes with/without MEC and MCC cooperation, EEDTO can achieve energy-efficient offloading decisions with relatively lower computational complexity.