This paper presents Meta's Production Wide Area Network (WAN) Entitlement solution used by thousands of Meta's services to share the network safely and efficiently. We first introduce the Network Entitlement problem, i.e., how to share WAN bandwidth across services with flexibility and SLO guarantees. We present a new abstraction entitlement contract, which is stable, simple, and operationally friendly. The contract defines services' network quota and is set up between the network team and services teams to govern their obligations. Our framework includes two key parts: (1) an entitlement granting system that establishes an agile contract while achieving network efficiency and meeting long-term SLO guarantees, and (2) a large-scale distributed run-time enforcement system that enforces the contract on the production traffic. We demonstrate its effectiveness through extensive simulations and real-world end-to-end tests. The system has been deployed and operated for over two years in production. We hope that our years of experience provide a new angle to viewing WAN network sharing in production and will inspire follow-up research.

This paper presents Facebook's design and operational experience of a Hose-based backbone network planning system. This initial adoption of the Hose model in network planning is driven by the capacity and demand uncertainty pressure of backbone expansion. Since the Hose model abstracts the aggregated traffic demand per site, peak traffic flows at different times can be multiplexed to save capacity and buffer traffic spikes. Our core design involves heuristic algorithms to select Hose-compliant traffic matrices and cross-layer optimization between the optical and IP networks. We evaluate the system performance in production and share insights from years of production experience. Hose-based network planning can save 17.4% capacity and drops 75% less traffic under fiber cuts. As the first study of Hose in network planning, our work has the potential to inspire follow-up research.

Location-based services enabled by broadband wireless access play an increasingly important role in people's daily navigation and coordination. Location-based applications frequently report user location to Internet servers, and location accuracy is essential to the utility of these services. However, regular and accurate location updates impact efficient usage of network resources and also users' privacy, which is not directly observed by the users. In this paper, we conducted a large scale measurement study to understand location accuracy and communication frequency of such applications. We found that while most location reports are accurate enough, some applications run in the background, reporting user locations with a higher accuracy and frequency than needed for the user's purpose. For example, while from the user perspective, hourly zip code updates are enough location information to obtain local weather conditions, some weather forecast applications report user location every five minutes or less and at GPS-level accuracy. We found that location reports from many phone applications are accurate enough and frequent enough to enable the inference of users home and work addresses, and potentially their identity, exacerbating user privacy concerns.

Proportional Fair (PF) is a frequently used channel- aware scheduling algorithm in 3G wireless networks. However, recent work by us and others has shown that, in practice, PF suffers from significant robustness issues that can unnecessarily starve "well-behaved" users. In this paper, we analyze these issues with the goal of developing an alternative scheduling algorithm more robust than PF. We start by identifying two scenarios in which PF can cause starvation. We analyze both scenarios and develop mechanisms that prevent such starvation. Then, we combine these mechanisms to propose our Parallel Adaptive Quantile-based (PAQ) scheduling algorithm. We use simulation experiments with synthetic and measurement-based traces of wireless channel conditions to show that PAQ is not only robust but also achieves comparable or better throughput and fairness than PF.