Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing
Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing
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DOI：
10.1109/jiot.2020.3030926
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发表时间：
2021-03
影响因子：
10.6
通讯作者：
Yuvraj Sahni;Jiannong Cao;Lei Yang;Yusheng Ji
中科院分区：
文献类型：
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作者：
Yuvraj Sahni;Jiannong Cao;Lei Yang;Yusheng Ji
协同边缘计算（CEC）是最近流行的范例，其使得能够在不同边缘设备之间共享数据和计算资源。任务卸载是CEC中需要解决的一个重要问题，因为我们需要决定每个任务的执行时间和位置。然而，这是具有挑战性的，以解决任务卸载在CEC的任务可以卸载到多跳相邻设备，导致网络流之间的带宽竞争。大多数现有的作品没有共同考虑网络流调度，可能会导致网络拥塞和效率低下的性能方面的完成时间。另一个挑战是制定和解决的问题，考虑相关任务和冲突的网络流之间的依赖关系。最近很少有作品考虑多跳计算卸载，然而，这些作品专注于独立的任务，并没有共同考虑网络流的依赖关系。在这项工作中，我们以数学方式阐述了CEC中联合卸载由相关子任务组成的多个任务和网络流调度的问题，以最小化任务的平均完成时间。我们提出了一个联合依赖的任务卸载和流调度启发式（JDOFH），考虑了任务有向无环图和网络流的开始时间的依赖关系。使用模拟真实的应用程序任务图和模拟任务图的性能比较表明，JDOFH导致高达85%的平均完成时间相比，基准解决方案，不作出联合决策的改善。
Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of data and computation resources among different edge devices. Task offloading is an important problem to address in CEC as we need to decide when and where each task is executed. However, it is challenging to solve task offloading in CEC as tasks can be offloaded to a multihop neighboring device leading to bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling that can lead to network congestion and inefficient performance in terms of completion time. Another challenge is to formulate and solve the problem considering the dependencies among dependent tasks and conflicting network flows. Few recent works have considered multihop computation offloading; however, these works focus on independent tasks and do not jointly consider the dependencies with network flows. In this work, we mathematically formulate the problem of jointly offloading multiple tasks consisting of dependent subtasks and network flow scheduling in CEC to minimize the average completion time of tasks. We have proposed a joint dependent task offloading and flow scheduling heuristic (JDOFH) that considers both dependencies in task directed acyclic graph and start time of network flows. Performance comparison done using simulation for both real application task graph and simulated task graphs shows that JDOFH leads to up to 85% improvement in average completion time compared to benchmark solutions which do not make a joint decision.