Fairness-Aware VNF Mapping and Scheduling in Satellite Edge Networks for Mission-Critical Applications

Satellite Edge Computing (SEC) is seen as a promising solution for deploying network functions in orbit to provide ubiquitous services with low latency and bandwidth. Software Defined Networks (SDN) and Network Function Virtualization (NFV) enable SEC to manage and deploy services more flexibly. In this paper, we study a dynamic and topology-aware VNF mapping and scheduling strategy within an SDN/NFV-enabled SEC infrastructure. Our focus is on meeting the stringent requirements of mission-critical (MC) applications, recognizing their significance in both satellite-to-satellite and edge-to-satellite communications while ensuring service delay margin fairness across various time-sensitive service requests. We formulate the VNF mapping and scheduling problem as an Integer Nonlinear Programming problem (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">INLP</monospace>), with the objective of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimax</i> fairness among specified requests while considering dynamic satellite network topology, traffic, and resource constraints. We then propose two algorithms for solving the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">INLP</monospace> problem: Fairness-Aware Greedy Algorithm for Dynamic VNF Mapping and Scheduling (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAGD_MASC</monospace>) and Fairness-Aware Simulated Annealing-Based Algorithm for Dynamic VNF Mapping and Scheduling (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FASD_MASC</monospace>) which are suitable for low and high service arrival rates, respectively. Our extensive simulations demonstrate that both <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAGD_MASC</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FASD_MASC</monospace> approaches are very close to the optimization-based solution and outperform the benchmark solution in terms of service acceptance rates.