Faithful reproduction of network experiments

The proliferation of cloud computing has compelled the research community to rethink fundamental aspects of network systems and architectures. However, the tools commonly used to evaluate new ideas have not kept abreast of the latest developments. Common simulation and emulation frameworks fail to provide scalability, fidelity, reproducibility and execute unmodified code, all at the same time. We present SELENA, a Xen-based network emulation framework that offers fully reproducible experiments via its automation interface and supports the use of unmodified guest operating systems. This allows out-of-the-box compatibility with common applications and OS components, such as network stacks and filesystems. In order to faithfully emulate faster and larger networks, SELENA adopts the technique of time-dilation and transparently slows down the passage of time for guest operating systems. This technique effectively virtualizes the availability of host’s hardware resources and allows the replication of scenarios with increased I/O and computational demands. Users can directly control the tradeoff between fidelity and running-times via intuitive tuning knobs. We evaluate the ability of SELENA to faithfully replicate the behaviour of real systems and compare it against existing popular experimentation platforms. Our results suggest that SELENA can accurately model networks with aggregate link speeds of 44 Gbps or more, while improving by four times the execution time in comparison to ns3 and exhibits near-linear scaling properties.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2658260.265827

Learning to Generate Genotypes with Neural Networks

Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topology of a neural network for reinforcement learning problems, or when a neural network is applied as a surrogate fitness function to aid the evolutionary optimisation of expensive fitness functions. In this paper we take a different approach, asking the question of whether a neural network can be used to provide a mutation distribution for an evolutionary algorithm, and what advantages this approach may offer? Two modern neural network models are investigated, a Denoising Autoencoder modified to produce stochastic outputs and the Neural Autoregressive Distribution Estimator. Results show that the neural network approach to learning genotypes is able to solve many difficult discrete problems, such as MaxSat and HIFF, and regularly outperforms other evolutionary techniques

Ocean acidification and the loss of phenolic substances in marine plants.

Rising atmospheric CO(2) often triggers the production of plant phenolics, including many that serve as herbivore deterrents, digestion reducers, antimicrobials, or ultraviolet sunscreens. Such responses are predicted by popular models of plant defense, especially resource availability models which link carbon availability to phenolic biosynthesis. CO(2) availability is also increasing in the oceans, where anthropogenic emissions cause ocean acidification, decreasing seawater pH and shifting the carbonate system towards further CO(2) enrichment. Such conditions tend to increase seagrass productivity but may also increase rates of grazing on these marine plants. Here we show that high CO(2) / low pH conditions of OA decrease, rather than increase, concentrations of phenolic protective substances in seagrasses and eurysaline marine plants. We observed a loss of simple and polymeric phenolics in the seagrass Cymodocea nodosa near a volcanic CO(2) vent on the Island of Vulcano, Italy, where pH values decreased from 8.1 to 7.3 and pCO(2) concentrations increased ten-fold. We observed similar responses in two estuarine species, Ruppia maritima and Potamogeton perfoliatus, in in situ Free-Ocean-Carbon-Enrichment experiments conducted in tributaries of the Chesapeake Bay, USA. These responses are strikingly different than those exhibited by terrestrial plants. The loss of phenolic substances may explain the higher-than-usual rates of grazing observed near undersea CO(2) vents and suggests that ocean acidification may alter coastal carbon fluxes by affecting rates of decomposition, grazing, and disease. Our observations temper recent predictions that seagrasses would necessarily be "winners" in a high CO(2) world

Enabling Performance Evaluation beyond 10 Gbps

Despite network monitoring and testing being critical for computer networks, current solutions are both extremely expensive and inflexible. This demo presents OSNT (www.osnt.org), a community-driven, high-performance, open-source traffic generator and capture system built on top of the NetFPGA-10G board which enables flexible network testing. The platform supports full line-rate traffic generation regardless of packet size across the four card ports, packet capture filtering and packet thinning in hardware and sub-msec time precision in traffic generation and capture, corrected using an external GPS device. Furthermore, it provides a software APIs to test the dataplane performance of multi-10G switches, providing a starting point for a number of different test cases. OSNT flexibility is further demonstrated through the OFLOPS-turbo platform: an integration of OSNT with the OFLOPS OpenFlow switch performance evaluation platform, enabling control and data plane evaluation of 10G switches. This demo showcases the applicability of the OSNT platform to evaluate the performance of legacy and OpenFlow-enabled networking devices, and demonstrates it using commercial switches

Current challenges in three-dimensional bioprinting heart tissues for cardiac surgery.

SUMMARY:Previous attempts in cardiac bioengineering have failed to provide tissues for cardiac regeneration. Recent advances in 3-dimensional bioprinting technology using prevascularized myocardial microtissues as 'bioink' have provided a promising way forward. This review guides the reader to understand why myocardial tissue engineering is difficult to achieve and how revascularization and contractile function could be restored in 3-dimensional bioprinted heart tissue using patient-derived stem cells

Dynamic scaling of topological ordering in classical systems

We analyze scaling behaviors of simulated annealing carried out on various classical systems with topological order, obtained as appropriate limits of the toric code in two and three dimensions. We first consider the three-dimensional Z2 (Ising) lattice gauge model, which exhibits a continuous topological phase transition at finite temperature. We show that a generalized Kibble-Zurek scaling ansatz applies to this transition, in spite of the absence of a local order parameter. We find perimeter-law scaling of the magnitude of a nonlocal order parameter (defined using Wilson loops) and a dynamic exponent z=2.70±0.03, the latter in good agreement with previous results for the equilibrium dynamics (autocorrelations). We then study systems where (topological) order forms only at zero temperature - the Ising chain, the two-dimensional Z2 gauge model, and a three-dimensional star model (another variant of the Z2 gauge model). In these systems the correlation length diverges exponentially, in a way that is nonsmooth as a finite-size system approaches the zero temperature state. We show that the Kibble-Zurek theory does not apply in any of these systems. Instead, the dynamics can be understood in terms of diffusion and annihilation of topological defects, which we use to formulate a scaling theory in good agreement with our simulation results. We also discuss the effect of open boundaries where defect annihilation competes with a faster process of evaporation at the surface

Abstract Interpretation of Stateful Networks

Modern networks achieve robustness and scalability by maintaining states on their nodes. These nodes are referred to as middleboxes and are essential for network functionality. However, the presence of middleboxes drastically complicates the task of network verification. Previous work showed that the problem is undecidable in general and EXPSPACE-complete when abstracting away the order of packet arrival. We describe a new algorithm for conservatively checking isolation properties of stateful networks. The asymptotic complexity of the algorithm is polynomial in the size of the network, albeit being exponential in the maximal number of queries of the local state that a middlebox can do, which is often small. Our algorithm is sound, i.e., it can never miss a violation of safety but may fail to verify some properties. The algorithm performs on-the fly abstract interpretation by (1) abstracting away the order of packet processing and the number of times each packet arrives, (2) abstracting away correlations between states of different middleboxes and channel contents, and (3) representing middlebox states by their effect on each packet separately, rather than taking into account the entire state space. We show that the abstractions do not lose precision when middleboxes may reset in any state. This is encouraging since many real middleboxes reset, e.g., after some session timeout is reached or due to hardware failure