The United Nations and the Human Rights Issue

This paper demonstrates a new class of bugs that is likely to occur in enterprise OpenFlow deployments. In particular, step-by-step, reactive establishment of paths can cause network-wide inconsistencies or performance- and space-related inefficiencies. The cause for this behavior is inconsistent packet processing: as the packets travel through the network they do not encounter consistent state at the OpenFlow controller. To mitigate this problem, we propose to use transactional semantics at the controller to achieve consistent packet processing. We detail the challenges in achieving this goal (including the inability to directly apply database techniques), as well as a potentially promising approach. In particular, we envision the use of multi-commit transactions that could provide the necessary serialization and isolation properties without excessively reducing network performance.QC 20140707</p

Consistent SDNs through Network State Fuzzing

The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to periodically test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly while outperforming baseline approaches.Comment: Added three extra relevant references, the arXiv later was accepted in IEEE Transactions of Network and Service Management (TNSM), 2019 with the title "Towards Consistent SDNs: A Case for Network State Fuzzing

Natural Compression for Distributed Deep Learning

Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this problem. In this work, we introduce a new, simple yet theoretically and practically effective compression technique: {\em natural compression (NC)}. Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two, which can be computed in a "natural" way by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by not more than the tiny factor \nicefrac{9}{8}, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to {\em $3$-$4\times$ improvement in overall theoretical running time}. For applications requiring more aggressive compression, we generalize NC to {\em natural dithering}, which we prove is {\em exponentially better} than the common random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect, and offer new state-of-the-art both in theory and practice.Comment: 8 pages, 20 pages of Appendix, 6 Tables, 14 Figure

Systematic Software Testing Meets Networking

Nowadays users expect and demand highly dependable network connectivity and services. However, several recent episodes demonstrate that software errors and operator mistakes continue to cause undesired disruptions and outages. It is crucial to have reliable networks, and this requirement does not change with Software Defined Networking (SDN). Unfortunately, as the network programmability enhances and software plays a greater role in it, risks that buggy software may disrupt an entire network also increase. The centralized programming model, where a single controller program manages the network, seems to reduce the likelihood of bugs. However, the system is inherently distributed and asynchronous, with events happening at different switches and end hosts, and inevitable delays affecting communication with the controller. This extended abstract presents an overview of efficient, systematic techniques for testing the SDN software stack at both its highest and lowest layer. That is, our testing techniques target at the top layer, the OpenFlow controller programs (Section 1) and, at the bottom layer, the OpenFlow agents (Section 2)—the software that each switch runs to enable remote programmatic access to its forwarding tables. The papers describing these tools have been published in [2] and [3]. Our goal here is to increase the awareness of the ever-increasing number of SDN adopters to our tools. In doing so, we hope to: (1) enable faster adoption of OpenFlow/SDN due to accelerated switch interoperability testing, and (2) decrease the chance of encountering bugs in the deployment of OpenFlow controller applications. Combined, our tools should increase the confidence in SDN as a whole.TRUEpu