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Multi-access Edge Computing (MEC) is an enabling technology to leverage newnetwork applications, such as virtual/augmented reality, by providing fastertask processing at the network edge. This is done by deploying servers closerto the end users to run the network applications. These applications are oftenintensive in terms of task processing, memory usage, and communication; thusmobile devices may take a long time or even not be able to run themefficiently. By transferring (offloading) the execution of these applicationsto the servers at the network edge, it is possible to achieve a lowercompletion time (makespan) and meet application requirements. However,offloading multiple entire applications to the edge server can overwhelm itshardware and communication channel, as well as underutilize the mobile devices'hardware. In this paper, network applications are modeled as Directed AcyclicGraphs (DAGs) and partitioned into tasks, and only part of these tasks areoffloaded to the edge server. This is the DAG application partitioning andoffloading problem, which is known to be NP-hard. To approximate its solution,this paper proposes the FlexDO algorithm. FlexDO combines a greedy phase with apermutation phase to find a set of offloading decisions, and then chooses theone that achieves the shortest makespan. FlexDO is compared with a proposalfrom the literature and two baseline decisions, considering realistic DAGapplications extracted from the Alibaba Cluster Trace Program. Results showthat FlexDO is consistently only 3.9% to 8.9% above the optimal makespan in alltest scenarios, which include different levels of CPU availability, amulti-user case, and different communication channel transmission rates. FlexDOoutperforms both baseline solutions by a wide margin, and is three times closerto the optimal makespan than its competitor.

A flexible algorithm to offload DAG applications for edge computing