Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing
Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing
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DOI：
10.1109/jsac.2019.2894306
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发表时间：
2019-03-01
影响因子：
16.4
通讯作者：
Assi, Chadi
中科院分区：
文献类型：
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作者：
Alameddine, Hyame Assem;Sharafeddine, Sanaa;Assi, Chadi
多路访问边缘计算(MEC)最近作为一种新的范式出现，以便于在网络边缘、接近终端设备的位置访问高级计算功能，从而支持各种新兴行业垂直市场所需的丰富的延迟敏感型服务。物联网(IoT)设备是高度无处不在和互联的，由于其电池、计算和存储能力有限，可以将其计算任务卸载到MEC服务器上托管的应用程序来处理。这类向物联网设备分流任务提供服务的物联网应用托管在计算能力有限的边缘服务器上。考虑到卸载任务需求的异构性(不同的计算要求、延迟等)和有限的MEC能力，我们共同决定任务卸载(任务到应用程序分配)和调度(它们的执行顺序)，这产生了一个具有组合性质的挑战性问题。此外，我们还共同决定了托管应用程序的计算资源分配问题，我们将该问题称为动态任务卸载和调度问题，包括前面提到的三个子问题。我们对该问题进行了数学描述，并针对其复杂性，基于基于逻辑的Bders分解技术，设计了一种新颖的思想分解。这种技术解决了一个约束较少的松弛的主问题和一个子问题，子问题的解决允许生成切割，这将迭代地引导主问题收紧其搜索空间。最终，主问题和子问题都将收敛，以产生最优解。我们表明，对于所研究的实例，该技术在运行时间上提供了几个数量级(超过140倍)的改进。这种方法的另一个优点是它能够提供具有性能保证的解决方案。最后，我们使用这种方法来突出不同垂直行业作为多个系统参数的函数的有洞察力的性能趋势，重点是对延迟敏感的用例。
Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.