This paper presents ModelNet, a scalable Internet emulation environment that enables researchers to deploy unmodified software prototypes in a configurable Internet-like environment and subject them to faults and varying network conditions. Edge nodes running user-specified OS and application software are configured to route their packets through a set of ModelNet core nodes, which cooperate to subject the traffic to the bandwidth, congestion constraints, latency, and loss profile of a target network topology. This paper describes and evaluates the ModelNet architecture and its implementation, including novel techniques to balance emulation accuracy against scalability. The current ModelNet prototype is able to accurately subject thousands of instances of a distrbuted application to Internet-like conditions with gigabits of bisection bandwidth. Experiments with several large-scale distributed services demonstrate the generality and effectiveness of the infrastructure.

Abstract. SDN deployments rely on switches that come from various vendors and differ in terms of performance and available features. Understanding these differences and performance characteristics is essential for ensuring successful deployments. In this paper we measure, report, and explain the performance characteristics of flow table updates in three hardware OpenFlow switches. Our results can help controller developers to make their programs efficient. Further, we also highlight differences between the OpenFlow specification and its implementations, that if ignored, pose a serious threat to network security and correctness.

Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidthintensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardwarebased techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while achieving the same traffic rates as the energy-oblivious approaches. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep.

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energyreduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimalityscalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.

The increasing adoption of Software Defined Networking, and OpenFlow in particular, brings great hope for increasing extensibility and lowering costs of deploying new network functionality. A key component in these networks is the OpenFlow agent, a piece of software that a switch runs to enable remote programmatic access to its forwarding tables. While testing high-level network functionality, the correct behavior and interoperability of any OpenFlow agent are taken for granted. However, existing tools for testing agents are not exhaustive nor systematic, and only check that the agent's basic functionality works. In addition, the rapidly changing and sometimes vague OpenFlow specifications can result in multiple implementations that behave differently. This paper presents SOFT, an approach for testing the interoperability of OpenFlow switches. Our key insight is in automatically identifying the testing inputs that cause different OpenFlow agent implementations to behave inconsistently. To this end, we first symbolically execute each agent under test in isolation to derive which set of inputs causes which behavior. We then crosscheck all distinct behaviors across different agent implementations and evaluate whether a common input subset causes inconsistent behaviors. Our evaluation shows that our tool identified several inconsistencies between the publicly available Reference OpenFlow switch and Open vSwitch implementations.