In this paper, we analyze a type of platform common in the service sector, where customers can freely post tasks, and service providers pay a fee to purchase job leads and unlock the ability to communicate with these customers. We adopt a Bayesian persuasion framework to explore how the platform can leverage its informational advantage about the supplier base and pricing to strategically influence the decisions of service providers, aiming to maximize its revenue. We begin the analysis by assuming that prices are exogenously given and characterize the structure of the optimal signaling mechanism. Specifically, the platform recommends all service providers to participate when the pool size is below a certain threshold. When the pool size exceeds this threshold, the platform randomizes its recommendations between two consecutive numbers, independent of the pool size. This behavior is driven by a unique feature of our model, where the beliefs of the platform and suppliers are non-identical: a supplier's knowledge of their own participation inflates their belief about the total number of service providers. Next, we endogenize the platform's pricing decision and consider the policy space where the signaling mechanism and state-dependent pricing are jointly optimized. Notably, within this space, we find that the optimal signaling mechanism with a single price remains optimal. This result implies the superiority of the optimal signaling mechanism over the commonly adopted contingent pricing policy, where the price charged by the platform depends on the number of interested suppliers. We show that the performance discrepancy between these two policies is particularly pronounced when supply competition is intense.

Peer-assisted Video-on-Demand (VoD) systems have not only received substantial recent research attention, but also been implemented and deployed with success in large-scale real- world streaming systems, such as PPLive. Peer-assisted Video- on-Demand systems are designed to take full advantage of peer upload bandwidth contributions with a cache on each peer. Since the size of such a cache on each peer is limited, it is imperative that an appropriate cache replacement algorithm is designed. There exists a tremendous level of flexibility in the design space of such cache replacement algorithms, including the simplest alternatives such as Least Recently Used (LRU). Which algorithm is the best to minimize server bandwidth costs, so that when peers need a media segment, it is most likely available from caches of other peers? Such a question, however, is arguably non-trivial to answer, as both the demand and supply of media segments are stochastic in nature. In this paper, we seek to construct an analytical framework based on optimal control theory and dynamic programming, to help us form an in-depth understanding of optimal strategies to design cache replacement algorithms. With such analytical insights, we have shown with extensive simulations that, the performance margin enjoyed by optimal strategies over the simplest algorithms is not substantial, when it comes to reducing server bandwidth costs. In most cases, the simplest choices are good enough as cache replacement algorithms in peer-assisted VoD systems.