Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. In BoxOpt, we first model the simple reservation interface adopted in most reservation systems as a resource membership oracle. Second, we develop an efficient algorithm that constructs a resource separation oracle by a linear number of calls on resource membership oracle. Third, we develop a generic framework to construct a resource optimization oracle by iteratively calling the resource separation oracle, and then develop three novel, efficient algorithms under this generic framework, the best of which computes the optimal resource reservation by a linear number of calls on resource separation oracle. As such, BoxOpt can discover the optimal resource reservation with O(n2) calls on the resource membership oracle. We implement a prototype of BoxOpt with and demonstrate its efficiency and efficacy via extensive experiments using real network topology and a 7-day trace from a large operational federation network. Results show that (1) BoxOpt has a 100% correctness ratio by comparing with a state-of-the-art optimization solver, and (2) for 90% of requests, BoxOpt learns the optimal resource reservation within 10 seconds.

Software-Defined Networking (SDN) enables more dynamic and fine-grained network control. In particular, network operators can route traffic not only based on packet header fields, but also higher-level parameters such as user settings, traffic characteristics, and application-layer information extracted by virtualized network functions such as DPI, firewall and authentication servers. Integrating these higher-level parameters into an SDN programming framework brings substantial benefits but is still missing in the SDN community. In this paper, we articulate the challenges and then propose Trident, a novel unified SDN programming framework. Trident extends algorithmic SDN programming with a new abstraction called stream attribute, which integrates meta parameters into the match-action programming paradigm. Further, Trident adopts the idea of reactive value from function reactive programming, eliminating the complexity of manually handling dynamicity. To effectively and efficiently realize these novel ideas, Trident introduces reactive table as the basic processing unit and develops a domain-specific distributed update protocol to maintain consistency during updates. Evaluations show that Trident puts very little overhead on integrating existing network management tools and network functions, and can handle up to O(105) routing requests per second with O(100) milliseconds latency.