Multi-domain network resource reservation systems are being deployed, driven by the demand and substantial benefits of providing predictable network resources. However, a major lack of existing systems is their coarse granularity, due to the participating networks' concern of revealing sensitive information, which can result in substantial inefficiencies. This paper presents Mercator, a novel multi-domain network resource discovery system to provide fine-grained, global network resource information, for collaborative sciences. The foundation of Mercator is a resource abstraction through algebraic-expression enumeration (i.e., linear inequalities/equations), as a compact representation of multiple properties of network resources (e.g., bandwidth, delay, and loss rate) in multi-domain networks. In addition, we develop an obfuscating protocol, to address the privacy concerns by ensuring that no participant can associate the algebraic expressions with the corresponding member networks. We also introduce a super-set projection technique to increase Mercator's scalability. We implement a prototype Mercator and deploy it in a small federation network. We also evaluate the performance of Mercator through extensive experiments using real topologies and traces. Results show that Mercator 1) efficiently discovers available networking resources in collaborative networks on average four orders of magnitude faster, and allows fairer allocations of network resources; 2) preserves the member networks' privacy with little overhead; and 3) scales to a collaborative network of 200 member networks.

Revealing an abstract view of the network is essential for the new paradigm of developing network-aware adaptive applications that can fully leverage the available computation and storage resources and achieve better business values. In this paper, we introduce ONV, a novel abstraction of flow-based on-demand network view. The ONV models network views as linear constraints on network-related variables in application-layer objective functions, and provides “equivalent” network views that allow applications to achieve the same optimal objectives as if they have the global information. We prove the lower bound for the number of links contained in an equivalent network view, and propose two algorithms to effectively calculate on-demand equivalent network views. We evaluate the efficacy and the efficiency of our algorithms extensively with real-world topologies. Evaluations demonstrate that the ONV can simplify the network up to 80% while maintaining an equivalent view of the network. Even for a large network with more than 25 000 links and a request containing 3000 flows, the result can be effectively computed in less than 1 min on a commodity server.

High-Ievel programming and programmable data paths are two key capabilities of software-defined networking (SDN). A fundamental problem linking these two capabilities is whether a given high-level SDN program can be realized onto a given low-level SDN datapath structure. Considering all high-level programs that can be realized onto a given datapath as the programming capacity of the datapath, we refer to this problem as the SDN data path programming capacity problem. In this paper, we conduct the first study on the SDN datapath programming capacity problem, in the general setting of high-level, datapath oblivious, algorithmic SDN programs and state-of-art multi-table SDN data path pipelines. In particular, considering datapath-oblivious SDN programs as computations and datapath pipelines as computation capabilities, we introduce a novel framework called SDN characterization junctions, to map both SDN programs and datapaths into a unifying space, deriving the first rigorous result on SDN datapath programming capacity. We not only prove our results but also conduct realistic evaluations to demonstrate the tightness of our analysis.

As many applications today migrate to distributed computing and cloud platforms, their user experience depends heavily on network performance. Software Defined Networking (SDN) makes it possible to obtain a global view of the network, introducing the new paradigm of developing adaptive applications with network views. A naive approach of realizing the paradigm, such as distributing the whole network view to applications, is not practical due to scalability and privacy concerns. Existing approaches providing network abstractions are limited to special cases, such as bottlenecks exist only at networks edges, resulting in potentially suboptimal or infeasible decisions. In this paper, we introduce a novel, on-demand network abstraction service that provides an abstract network view supporting not only accurate end-to-end QoS metrics, which satisfy the requirements of many peer-to-peer applications, but also multi-flow correlation, which is essential for bandwidth-sensitive applications containing many flows to conduct global network optimization. We prove that our abstract view is equivalent to the original network view, in the sense that applications can make the same optimal decision as with the complete information. Our evaluations demonstrate that the abstraction guarantees feasibility and optimality for network optimizations and protects the network service providers' privacy. Our evaluations also show that the service can be implemented efficiently; for example, for an extreme large network with 30,000 links and abstraction requests containing 3,000 flows, an abstract network view can be computed in less than one second.

Internet of Vehicles (IoV) has gained considerable attention from industry and academia due to the development of communication technology and smart city. However, a proprietary and closed way of operating hardware in network equipment slows down the progress of new service deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, i.e., software-defined IoV (SDIV), we adopt a software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data one and a uniform way to configure heterogeneous switches. However, IoV characteristics introduce some great challenges in rule installation due to the limited size of flow tables at OpenFlow-enabled switches that are the main SDN component. It is necessary to build compact flow tables for IoV scalability. Accordingly, we develop a novel rule installation mechanism to reduce the number of rules for real-time query services in SDIV. We separate the wired data plane from the wireless one and use multicast addresses in the latter. We introduce a destination-driven model in the wired data plane to reduce the number of rules at switches. Experiments with a real data trace show that the developed approach significantly reduces the number of rules without degrading the performance of data transmissions for real-time query services in IoV.

Providing an interface for network applications to access network state, Software-Defined Networking (SDN) northbound API protocol is the foundation for the development of programmable networks with adaptive applications. However, with the growing network scale and applications' need for routing state at multi-domain level, feeding complete routing states to applications would jeopardize their scalability and network providers' privacy. Thus a good routing state abstraction is needed, which must be on-demand so that different applications can receive customized abstract state suiting their needs. Moreover, it must be minimal and equivalent, i.e., containing all the necessary information for applications to make decisions as the complete state does with no redundancy. Current routing state abstractions are not on-demand, and adopt extreme aggregation approaches (e.g., the big switch) to provide a minimal abstraction with the price of severe information loss. For instance, bottleneck links shared between flows are concealed, leading applications to make sub-optimal decisions. In this paper, we design ORSAP, the first on-demand routing state abstraction protocol, through which network applications can describe their demands while Internet service providers can provide the on-demand minimal equivalent routing state accordingly. ORSAP ensures applications' scalability, protects network providers' privacy, and significantly reduces the traffic to disseminate the information. Experiments show that with ORSAP and the abstraction engine we introduced in this paper, one can achieve a state abstraction ratio of up to 60% with an extremely low computation time even with large networks and complex application queries.

In SDN (Software-Defined Networks), applications are granted the ability to control and manage networks, such as routing, access control, and load balance, which is considered one of the most significant characters of SDN. However, these concurrent applications are lack of interactions when they operate on overlapping portions of the traffic and introduce a critical challenge of potentially conflicting flow rules (including match fields and actions). When their actions are dropping, forwarding, or diverting to the controller, these kinds of flow rule conflicts are inherent and arduous, and there exist no appropriate compositional methods of resolving them. Under the circumstances, the paper proposes a general model of abstract network states where different applications can own their different network views. When conflicting rules are detected, the model provides new network states for applications by composing original network states and conflicting rules. Therefore, applications do not need to handle underlying conflicting rules and just achieve the latest network states to install flow rules correctly.

Despite the emergence of multi-table pipelining as a key feature of next-generation SDN data-path models, there is no existing work that addresses the substantial programming challenge of utilizing multi-tables automatically. In this paper, we present Magellan, the first system that addresses the aforementioned challenge. Introducing two novel, substantial algorithms, map-explore and table-design, Magellan achieves automatic derivation and population of multi-table pipelines from a datapath-oblivious, high-level SDN program written in a general-purpose language. Comparing the flow tables generated by Magellan with those produced by standard SDN controllers including OpenDaylight and Floodlight, we show that Magellan uses between 46-68x fewer rules.