Cost-Efficient Delay-Bounded Dependent Task Offloading With Service Caching at Edges

We are now embracing an era of edge computing and artificial intelligence, and the combination of the two has spawned a new field of research called edge intelligence. Massive amounts of data is generated at the edge of network, which relies on artificial intelligence to realize its potential. Meanwhile, artificial intelligence is able to flourish when processing diverse edge data. However, the computation and storage resources of edge servers are not unlimited. For some large-scale intelligent applications, it is difficult to meet their service quality requirements by directly offloading the entire application to a nearby server for processing. Due to the heterogeneity of server resources in edge environments, how to balance the workload among edge servers to provide better services also becomes complicated. The goal of this paper is to minimize the total cost of offloading large-scale applications consisting of many dependent tasks in an edge system. We formulate the Dependent task Offloading with Service Caching (DOSC) problem, which is proved to be NP-hard. A dynamic planning-based algorithm is introduced to solve fixed-DOSC, in which some services are pre-configured on the edge server, and other services can not be downloaded from the remote cloud. We also present a theoretical analysis on the performance guarantee of the dynamic planning-based algorithm. Then, we propose a near-optimal algorithm using the Gibbs sampling to solve the general DOSC problem. Testbed experiments and trace-driven simulations are conducted to verify the performance of our algorithm. Our algorithm, shown to be the most effective in terms of cost, considers both service caching and task dependencies when task offloading in comparison to other baseline algorithms.