Meta's data center networks have relied on BGP for routing and interconnectivity due to its scalability and simplicity. However, as our network evolves, BGP's limitations in supporting complex network migrations have become apparent. These migrations require customized routing at each intermediate step, considering topological properties. In this paper, we highlight the unique challenges of production migration and illustrate how native BGP falls short, unable to encode both sequential and spatial conditions. To address this challenge, we introduce a novel Route Planning Abstraction (RPA) that augments the BGP protocol to support migration. It enables centralized route planning while maintaining distributed enforcement. We demonstrate the power of this abstraction by developing Centralium, a hybrid route-planning framework with a centralized controller, and over ten use cases to support various migrations in production. Our production experience with Centralium, deployed alongside BGP in large-scale data centers, has shown substantial reduction in the time and risk of network migration operations.

Expensive optical fibers provide connectivity for wide-area networks. Nowadays, the fibers are operated in a much conservative manner. By adaptively reconfiguring the fibers to exploit its signal quality, a recent proposal demonstrates a significant increase of optical link capacity. Such a reconfiguration currently accompanies a non-ignorable delay, during which the reconfigured link is not accessible, and the mentioned approach trades off the final throughput with the induced churn during the transition. This scheme can result in high traffic disturbance during the reconfiguration.To overcome the drawback of the simple churn-based update, we study the rate adaptation planning (RAP) problem under reconfiguration delay. We propose a multiple step planning with perseverance constraints. This approach leads to a smoother transition, but the optimal plan is shown NP-hard without constant factor approximation (unless P= NP). Therefore, we develop an efficient LP-based heuristic algorithm. Extensive simulations show that the algorithm gives 40 -50% higher throughput than the no-adaptive-link case. Also, the transition is much smoother: the resulting traffic fluctuation is only 40 -50% of the existing churn-based approach.

Software-defined networks (SDNs) facilitate more efficient routing of traffic flows using centralized network view. On the other hand, traditional distributed routing still enjoys the advantage of better scalability, robustness, and swift reaction to events such as failure. There are therefore significant potential benefits to adopt a hybrid operation where both distributed and centralized routing mechanisms co-exist. This hybrid operation however imposes a new challenge to network stability since a poor and inconsistent design can lead to repeated route switching when the two control mechanisms take turns to adjust the routes. In this paper, we discuss ways of solving the stability problem. We first define stability for hybrid SDNs and then establish a per-priority stabilizing framework to obtain stable routing patterns. For each priority class, we discuss three approaches to reach hybrid SDN stability: global optimization, greedy, and local search. It is argued that the proposed local search provides the best tradeoff among cost performance, computational complexity, and route disturbance. Furthermore, we design a system on a centralized controller, which utilizes those algorithms to stabilize the network. The design is implemented and extensively tested by simulations using realistic network information, including a trace of the Abilene network and data from a tier-1 Internet service providers backbone network.

A switching/forwarding fabric with high-bandwidth one-to-one and low-bandwidth many-to-many data forwarding can be achieved by combing electronic packet switches (EPS) and optical circuit switches (OCS). This hybrid solution scales well but it is not suitable for cloud computing/datacenter applications, which typically rely on one-to-many and many-to-one communications. Recently, composite-path switching (cp-switching), which adds paths between EPS and ocs is introduced to deal with this skewed traffic pattern. The state-of-the-art scheduling algorithm for CP-switches reduces, by heuristics, a CP-switching problem with one composite path to a hybrid switching problem without the composite path, and leverages existing scheduling techniques to tackle the latter problem. Unfortunately, the approach provides neither performance guarantee nor the support for multiple composite paths. In this paper, we systematically study the shortest time CP-switch scheduling problem with multiple composite paths and show that the problem can be expressed as an optimization problem with sparsity constraints. An LP-based algorithm is derived accordingly which supports multiple composite paths and more importantly, it provides performance guarantee. Simulations demonstrate that adding more composite paths can help shorten the schedule, and our approach outperforms existing methods by 30% to 70%.

Many network flows nowadays, especially in a data center environment, have associated deadlines by which they must be fully transmitted. Nevertheless, traditional transport protocols such as TCP, focus on concepts like throughput and fairness, and do not aim to satisfy flow deadlines. Motivated by this limitation, several alternative transport designs and solutions have been recently proposed. These approaches generally achieve a better performance in terms of the number of satisfied deadlines and are usually built upon various heuristics. In contrast to these previous works, this article approaches the problem directly from an optimization perspective. We first prove that the problem belongs to the class of NP-hard problems that do not even admit a constant ratio approximation solution (unless P=NP), and formulate it as a mixed integer-linear optimization program. Then, using linear programming approximations, we further develop offline and online optimization-based rate control algorithms to approach the problem. Flow-level simulation results indicate that the proposed algorithms can be near-optimal, and hence they can be served as benchmarks against which other solutions to this problem can be evaluated. We additionally performed simulations incorporating such real network features as deployment delays and packet-level granularity to evaluate the performance of the proposed algorithms in a more realistic environment.