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. Nevertheless, 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 continuously 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

Prototyping symbolic execution engines for interpreted languages

Symbolic execution is being successfully used to automatically test statically compiled code. However, increasingly more systems and applications are written in dynamic interpreted languages like Python. Building a new symbolic execution engine is a monumental effort, and so is keeping it up-to-date as the target language evolves. Furthermore, ambiguous language specifications lead to their implementation in a symbolic execution engine potentially differing from the production interpreter in subtle ways. We address these challenges by flipping the problem and using the interpreter itself as a specification of the language semantics. We present a recipe and tool (called Chef) for turning a vanilla interpreter into a sound and complete symbolic execution engine. Chef symbolically executes the target program by symbolically executing the interpreter's binary while exploiting inferred knowledge about the program's high-level structure. Using Chef, we developed a symbolic execution engine for Python in 5 person-days and one for Lua in 3 person-days. They offer complete and faithful coverage of language features in a way that keeps up with future language versions at near-zero cost. Chef-produced engines are up to 1000 times more performant than if directly executing the interpreter symbolically without Chef

In search of different categories of abstract concepts: a fMRI adaptation study

Concrete conceptual knowledge is supported by a distributed neural network representing different semantic features according to the neuroanatomy of sensory and motor systems. If and how this framework applies to abstract knowledge is currently debated. Here we investigated the specific brain correlates of different abstract categories. After a systematic a priori selection of brain regions involved in semantic cognition, i.e. responsible of, respectively, semantic representations and cognitive control, we used a fMRI-adaptation paradigm with a passive reading task, in order to modulate the neural response to abstract (emotions, cognitions, attitudes, human actions) and concrete (biological entities, artefacts) categories. Different portions of the left anterior temporal lobe responded selectively to abstract and concrete concepts. Emotions and attitudes adapted the left middle temporal gyrus, whereas concrete items adapted the left fusiform gyrus. Our results suggest that, similarly to concrete concepts, some categories of abstract knowledge have specific brain correlates corresponding to the prevalent semantic dimensions involved in their representation

Arabidopsis defense against the pathogenic fungus drechslera gigantea is dependent on the integrity of the unfolded protein response

Drechslera gigantea Heald & Wolf is a worldwide-spread necrotrophic fungus closely related to the Bipolaris genus, well-known because many member species provoke severe diseases in cereal crops and studied because they produce sesterpenoid phytoxins named ophiobolins which possess interesting biological properties. The unfolded protein response (UPR) is a conserved mechanism protecting eukaryotic cells from the accumulation of unfolded/misfolded proteins in the endoplasmic reticulum (ER). In plants, consolidated evidence supports the role of UPR in the tolerance to abiotic stress, whereas much less information is available concerning the induction of ER stress by pathogen infection and consequent UPR elicitation as part of the defense response. In this study, the infection process of D. gigantea in Arabidopsis thaliana wild type and UPR-defective bzip28 bzip60 double mutant plants was comparatively investigated, with the aim to address the role of UPR in the expression of resistance to the fungal pathogen. The results of confocal microscopy, as well as of qRT-PCR transcript level analysis of UPR genes, proteomics, microRNAs expression profile and HPLC-based hormone analyses demonstrated that ophiobolin produced by the fungus during infection compromised ER integrity and that impairment of the IRE1 /bZIP60 pathway of UPR hampered the full expression of resistance, thereby enhancing plant susceptibility to the pathogen

Inter-domain networking innovation on steroids: Empowering IXPs with SDN capabilities

While innovation in inter-domain routing has remained stagnant for over a decade, Internet Exchange Points (IXPs) are consolidating their role as economically advantageous interconnection points for reducing path latencies and exchanging ever increasing amounts of traffic. As such, IXPs appear as a natural place to foster network innovation and assess the benefits of Software-Defined Networking (SDN), a recent technological trend that has already boosted innovation within data-center networks. In this paper, we give a comprehensive overview of use cases for SDN at IXPs, which leverage the superior vantage point of an IXP to introduce advanced features like load-balancing and DDoS mitigation. We discuss the benefits of SDN solutions by analyzing real-world data from one of the largest IXPs. We also leverage insights into IXP operations to not only shape benefits for members but also for operators.This research is (in part) supported by European Union’s Horizon 2020 research and innovation programme under the ENDEAVOUR project (grant agreement 644960).This is the author accepted manuscript. The final version is available from IEEE via https://doi.org/ 10.1109/MCOM.2016.758827

A hydroalcoholic gel-based disinfection system for deteriogenic fungi on the contemporary mixed media artwork Poesia by Alessandro Kokocinski

The disinfection of deteriogenic microorganisms and the removal of induced chromatic alterations in artworks are still open challenges in the field of conservation. For this purpose, a new alcoholic hydrogel was tested to remove an extensive fungal attack from a multimaterial collage by the artist Alessandro Kokocinski and to mitigate chromatic changes caused by the contamination of its poster paper and plywood support layers. A Gellan gum-based hydrogel was used, which was modified by adding a high concentration of alcohol (66.7% ethanol), to give the system an effective disinfecting agent in addition to the detergent capacity of the gel for water-sensitive works of art. It was successfully tested on samples mimicking the complex stratigraphy of the artwork under study. To create replica mock-ups, the artwork materials and stratigraphy were investigated through diagnostic and laboratory techniques such as multispectral imaging, X-ray fluorescence spectroscopy, Fourier transform infrared spectroscopy, and pyrolysis coupled with gas-chromatography-mass spectrometry. The treatment was shown to have a disinfecting effect on the test samples and did not alter their structure, allowing us to apply the method to the artwork. Here, the hydrogel successfully removed and inhibited fungal proliferation in addition to mitigating the color changes caused by fungi

CherryPick: Tracing Packet Trajectory in Software-defined Datacenter Networks

SDN-enabled datacenter network management and debugging can benefit by the ability to trace packet trajectories. For ex-ample, such a functionality allows measuring traffic matrix, de-tecting traffic anomalies, localizing network faults, etc. Exist-ing techniques for tracing packet trajectories require either large data collection overhead or large amount of data plane resources such as switch flow rules and packet header space. We present CherryPick, a scalable, yet simple technique for tracing packet trajectories. The core idea of our technique is to cherry-pick the links that are key to representing an end-to-end path of a packet, and to embed them into its header on its way to destination. Pre-liminary evaluation on a fat-tree topology shows that CherryPick requires minimal switch flow rules, while using header space close to state-of-the-art techniques