Online Joint Pricing and Scheduling in Distributed Networked Platforms

Abstract

In distributed networked platforms, such as geographically distributed data centers and electric vehicle charging networks, there are multiple resource providers that can serve user requests arriving sequentially over time. In this paper, we study an online posted pricing problem jointly with scheduling in distributed networked platforms. In such systems, user requests arrive sequentially over time, and each request (if accepted) can be served by one of multiple resource providers. Rather than centrally assigning a provider, the platform posts prices together with corresponding scheduling decisions for all providers, and each user independently selects one option or leaves based on the posted prices and its private valuation. As demand increases, posted pricing and scheduling act as complementary mechanisms for regulating system usage. However, designing effective online posted pricing policies is challenging because of uncertainty in arrivals and the intertemporal coupling across requests, where current pricing decisions affect future feasibility and performance. We propose a class of parameterized threshold-based posted pricing policies that enable tractable online decision-making with provable performance guarantees. We identify a sufficient condition that ensures feasibility and supports competitive analysis, which in turn characterizes a structured policy design space. Building on this structure, we develop a learning-based approach to improve empirical performance under realistic demand patterns. We evaluate our approach on the problem of pricing and scheduling in electric vehicle charging stations using real-world charging traces, and demonstrate that the empirical performance of our algorithm outperforms other benchmark algorithms.