In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.

MOTIVATION: MinION is a portable nanopore sequencing device that can be easily operated in the field with features including monitoring of run progress and selective sequencing. To fully exploit these features, real-time base calling is required. Up to date, this has only been achieved at the cost of high computing requirements that pose limitations in terms of hardware availability in common laptops and energy consumption. RESULTS: We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed new versions of two key components used in convolutional neural networks for speech recognition and base calling. In our components, we propose a new way of factorization of a full convolution into smaller operations, which decreases memory access operations, memory access being a bottleneck on this device. DeepNano-coral achieves real-time base calling during sequencing with the accuracy slightly better than the fast mode of the Guppy base caller and is extremely energy efficient, using only 10 W of power. AVAILABILITY AND IMPLEMENTATION: https://github.com/fmfi-compbio/coral-basecaller. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Oxford Nanopore MinION is a portable DNA sequencer that is marketed as a device that can be deployed anywhere. Current base callers, however, require a powerful GPU to analyze data produced by MinION in real time, which hampers field applications. RESULTS: We have developed a fast base caller DeepNano-blitz that can analyze stream from up to two MinION runs in real time using a common laptop CPU (i7-7700HQ), with no GPU requirements. The base caller settings allow trading accuracy for speed and the results can be used for real time run monitoring (i.e. sample composition, barcode balance, species identification, etc.) or prefiltering of results for more detailed analysis (i.e. filtering out human DNA from human-pathogen runs). AVAILABILITY AND IMPLEMENTATION: DeepNano-blitz has been developed and tested on Linux and Intel processors and is available under MIT license at https://github.com/fmfi-compbio/deepnano-blitz. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ensuring network reliability is important for satisfying service-level objectives. However, diagnosing network anomalies in a timely fashion is difficult due to the complex nature of network configurations. We present Monocle - a system that uncovers forwarding problems due to hardware or software failures in switches, by verifying that the data plane corresponds to the view that an SDN controller installs via the control plane. Monocle works by systematically probing the switch data plane; the probes are constructed by formulating the switch forwarding table logic as a Boolean satisfiability (SAT) problem. Our SAT formulation quickly generates probe packets targeting a particular rule considering both existing and new rules. Monocle can monitor not only static flow tables (as is currently typically the case), but also dynamic networks with frequent flow table changes. Our evaluation shows that Monocle is capable of fine-grained monitoring for the majority of rules, and it can identify a rule suddenly missing from the data plane or misbehaving in a matter of seconds. In fact, during our evaluation Monocle uncovered problems with two hardware switches that we were using in our evaluation. Finally, during network updates Monocle helps controllers cope with switches that exhibit transient inconsistencies between their control and data plane states.

Ensuring network reliability is important for satisfying service-level objectives. However, diagnosing network anomalies in a timely fashion is difficult due to the complex nature of network configurations. We present Monocle --- a system that uncovers forwarding problems due to hardware or software failures in switches, by verifying that the data plane corresponds to the view that an SDN controller installs via the control plane. Monocle works by systematically probing the switch data plane; the probes are constructed by formulating the switch forwarding table logic as a Boolean satisfiability (SAT) problem. Our SAT formulation quickly generates probe packets targeting a particular rule considering both existing and new rules. Monocle can monitor not only static flow tables (as is currently typically the case), but also dynamic networks with frequent flow table changes. Our evaluation shows that Monocle is capable of finegrained monitoring for the majority of rules, and it can identify a rule suddenly missing from the data plane or misbehaving in a matter of seconds. Also, during network updates Monocle helps controllers cope with switches that exhibit transient inconsistencies between their control and data plane states.

We present Monocle, a system that systematically monitors the network data plane, and verifies that it corresponds to the view that the SDN controller builds and tries to enforce in the switches. Our evaluation shows that Monocle is capable of fine-grained per-rule monitoring for the majority of rules. In addition, it can help controllers to cope with switches that exhibit transient inconsistencies between their control plane and data plane states.

In this paper, we first show that transient, but grave problems such as violations of security policies can occur with real switches even when using consistent updates to Software Defined Networks. Next, we present techniques that are effective in ameliorating this problem. Our key insight is in creating a transparent layer that relies on control and data plane measurements to confirm rule updates only when the rule is visible in the data plane.

Ensuring correct network behavior is hard. Previous state of the art has demonstrated that analyzing a network containing middleboxes is hard. In this paper, we show that even using only statically configured switches, and asking the simplest possible question - “Will this concrete packet reach the destination?” - can make the problem intractable. Moreover, we demonstrate that this is a fundamental property because a network can perform arbitrary computations. Namely, we show how to emulate the Rule 110 cellular automaton using only basic network switches with simple features such as packet matching, header rewriting and round-robin loadbalancing. This ultimately means that analyzing dynamic network behavior can be as hard as analyzing an arbitrary program.

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.