Optimal Task Offloading for Deep Neural Network Driven Application in Space-Air-Ground Integrated Network

Running intelligent applications on a satellite is in urgent need, which can help to extract useful information from massive surveillance or remote sensing data and return it to ground in time. However, the limited computing ability on a satellite prohibits it from completing the whole application by itself quickly. Within the circumstance of space-air-ground integrated network (SAGIN), we propose to offload part of the computation task from the satellite to the ground station with strong computing ability, through the introduction of airship, which can assist the satellite not only by relaying but also in computing. To save the energy consumption of the satellite and airship, task offloading policy and resource allocation, are investigated for a special task model supporting deep neural network (DNN), which is popular in intelligent application. An optimization problem is formulated, which is difficult to solve. We achieve the global optimal solution through the following operations: 1) Transform the formulated problem into two levels, with every level dealing with discrete or continuous variables exclusively; 2) Explore implicit monotonicity and convexity of concerned functions so as to solve the non-convex lower level problem optimally only with several rounds of bisection or Golden search methods; 3) Solve the upper level problem optimally by enumeration but with polynomial complexity. Numerical results verify the effectiveness of our proposed method.