Harnessing Edge Computing Resources for Accelerating Industrial Tasks

Cloud-edge collaboration, as an emerging computing paradigm, aims to solve the shortcomings of remote transmission of conventional cloud computing. More precisely, it combines the powerful resource service capability of cloud computing with the advantages of low latency and relatively low energy consumption of edge computing to achieve the goal of optimization of various applications. However, with the rapid growth of computation-intensive industrial tasks, the overload problem of edge networks is becoming increasingly serious. Prior studies usually assume that the real-time state of edge resources has been known when selecting the offloading strategy so as to classify and execute tasks, but do not consider the fragmentation and heterogeneity features of edge computing resources. In light of these, we first generalize and model the computing resources of the edge nodes uniformly and then propose new heterogeneous task classification and recognition methods empowered by edge intelligence. We conduct intensive experiments to justify that our proposed design can minimize the data transmission delay caused by repeated computational tasks while saving energy consumption.