Joint Service Caching and Resource Allocation Over Different Timescales in Satellite Edge Computing Networks

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.