Exploiting Function-level Dependencies for Task Offloading in Edge Computing

Mobile edge computing has emerged as a promising paradigm to reduce the latency of novel computation-intensive and latency-critical application applications. Considering the increasing complexity of applications, a typical application calculation task often consists of a series of interdependent subtasks or components, and each subtask requires different specific service support for running or execution. However, existing works have overlooked the necessity of service caching. The constraints brought by service caching, computation capacity of edge servers and the dependencies between subtasks have a great influence on task offloading. In this paper, we measure the function structure of the application and find dependencies from the extracted call graph. On this basis, we investigate the problem of dependency-aware task offloading with deterministic service caching, restricted computation capacity and stochastic arrival and workload of tasks. We propose an efficient priority-based and critical path first DAG-task offloading algorithm to minimize overall service delay. Extensive experiments based on summarized call graph analysis tools show that our proposed algorithm significantly reduces the overall service delay of application compared with the benchmark algorithms.