Abstract
Autonomous driving is one of the most innovative applications nowadays. However, autonomous driving is still suffering from heavy calculation, high energy consumption and strict real-time execution constraints. Different from cloud computing, edge computing deploys calculation, storage and service on the edge of network. It is a better platform to serve efficiency and privacy oriented autonomous driving service offloading. To this end, we proposed a container-based edge offloading framework for autonomous driving. This framework builds an Offloading Decision Module, an Offloading Scheduler Module and an Edge Offloading Middleware on top of the lightweight virtualization. It provides the abstraction and management of the execution environment in the granularity of containers on edge. Therefore, it enables the privacy preserve and resource isolation for autonomous driving execution constraints. Its utility preferable offloading schedule strategy formalized the multi-application multi-edge nodes mapping problem into a multiple multidimensional knapsack problem (MMKP) and gave a utility oriented greedy algorithm (GA) for real-time solving. The experimental results show that the proposed framework has high feasibility and isolation meanwhile can guarantee millisecond-level autonomous driving offloading on edge.
Highlights
As a new way of transportation, autonomous driving technology develops rapidly
We model the problem of offloading multiple applications into edge nodes as a Multiple Multidimensional Knapsack Problem (MMKP) and gave a resourceconstrained offloading utility preferable strategy
The discussion in this chapter is based on the following assumptions: 1) It is assumed that the energy consumption generated by the operations on the on-board computing platform is known, the amount of memory required for the normal operation of the application is known, and the computing resources on the MEC server we are talking about only include the CPU
Summary
As a new way of transportation, autonomous driving technology develops rapidly. Based on the huge volume of sensor data, continuous operations of sensing, perception and decision making all demand a large amount of calculation. Tang et al.: Container Based Edge Offloading Framework for Autonomous Driving technology for service offloading As another challenge, the orchestration of lightweight virtualized runtimes is needed. To this end, in this paper, we study the demand of autonomous driving driven edge computing and propose a container-based offloading framework on edge. It includes a offloading decision module, a offloading scheduler module and a offloading middleware to manage the offloading pipeline and resource allocation for all containers in their entire life cycle The use of such framework can construct a dynamic isolated operating environment for utility maximized but secured autonomous driving application offloading on edge.
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