Effectiveness of Distributed Stateless Network Server Selection under Strict Latency Constraints

Abstract

We consider a set of network users (nodes), each generating latency-constrained service requests corresponding to the execution of computational tasks on servers positioned either within a cloud infrastructure or at the network edge. Within this framework, we systematically assess the efficacy of a distributed stateless server selection strategy, strategically performed by individual nodes. Leveraging principles from game theory, our study allows for a comparative analysis between the optimality achieved through globally orchestrated stateless allocation and a decentralized stateless server selection mechanism driven by the self-interested objectives of individual nodes. Our emphasis on stateless server allocation, rooted in a probabilistic selection framework between edge and cloud servers, stems from prior empirical revelations demonstrating the advantageous outcomes of determining the optimal distribution of edge and cloud tasks based on static network characteristics. Importantly, this determination occurs irrespective of the real-time network state. The suboptimal nature of the selfish allocation is quantified by the so called “price of anarchy,” a metric shown to approximate unity closely. This observation substantiates the justification for a distributed strategic implementation of stateless policies. This elucidation serves as a pivotal guide for crafting algorithms governing server selection, providing a quantitative validation of the efficacy inherent in distributed self-interested approaches.