Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

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.

Automated summary

Collaborative edge computing (CEC) involves task offloading, which is a challenging problem that requires consideration of network flow scheduling, task dependencies, and competition among network flows. Existing works often do not jointly consider these factors, but this study proposes a heuristic algorithm to minimize the average completion time of tasks by addressing these issues.