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Advances in space exploration have led to an explosion of tasks.Conventionally, these tasks are offloaded to ground servers for enhancedcomputing capability, or to adjacent low-earth-orbit satellites for reducedtransmission delay. However, the overall delay is determined by bothcomputation and transmission costs. The existing offloading schemes, whilebeing highly-optimized for either costs, can be abysmal for the overallperformance. The computation-transmission cost dilemma is yet to be solved.  In this paper, we propose an adaptive offloading scheme to reduce the overalldelay. The core idea is to jointly model and optimize thetransmission-computation process over the entire network. Specifically, torepresent the computation state migrations, we generalize graph nodes withmultiple states. In this way, the joint optimization problem is transformedinto a shortest path problem over the state graph. We further provide anextended Dijkstra's algorithm for efficient path finding. Simulation resultsshow that the proposed scheme outperforms the ground and one-hop offloadingschemes by up to 37.56% and 39.35% respectively on SpaceCube v2.0.

Adaptive Task Offloading for Space Missions: A State-Graph-Based Approach