Lightweight Asynchronous Snapshots for Distributed Dataflows

Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput. One of the most fundamental challenges of this paradigm is providing processing guarantees under potential failures. Existing approaches rely on periodic global state snapshots that can be used for failure recovery. Those approaches suffer from two main drawbacks. First, they often stall the overall computation which impacts ingestion. Second, they eagerly persist all records in transit along with the operation states which results in larger snapshots than required. In this work we propose Asynchronous Barrier Snapshotting (ABS), a lightweight algorithm suited for modern dataflow execution engines that minimises space requirements. ABS persists only operator states on acyclic execution topologies while keeping a minimal record log on cyclic dataflows. We implemented ABS on Apache Flink, a distributed analytics engine that supports stateful stream processing. Our evaluation shows that our algorithm does not have a heavy impact on the execution, maintaining linear scalability and performing well with frequent snapshots.Comment: 8 pages, 7 figure

Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms

Monitoring Partially Synchronous Distributed Systems using SMT Solvers

In this paper, we discuss the feasibility of monitoring partially synchronous distributed systems to detect latent bugs, i.e., errors caused by concurrency and race conditions among concurrent processes. We present a monitoring framework where we model both system constraints and latent bugs as Satisfiability Modulo Theories (SMT) formulas, and we detect the presence of latent bugs using an SMT solver. We demonstrate the feasibility of our framework using both synthetic applications where latent bugs occur at any time with random probability and an application involving exclusive access to a shared resource with a subtle timing bug. We illustrate how the time required for verification is affected by parameters such as communication frequency, latency, and clock skew. Our results show that our framework can be used for real-life applications, and because our framework uses SMT solvers, the range of appropriate applications will increase as these solvers become more efficient over time.Comment: Technical Report corresponding to the paper accepted at Runtime Verification (RV) 201

Transport efficiency of metachronal waves in 3d cilia arrays immersed in a two-phase flow

The present work reports the formation and the characterization of antipleptic and symplectic metachronal waves in 3D cilia arrays immersed in a two-fluid environment, with a viscosity ratio of 20. A coupled lattice-Boltzmann-Immersed-Boundary solver is used. The periciliary layer is confined between the epithelial surface and the mucus. Its thickness is chosen such that the tips of the cilia can penetrate the mucus. A purely hydrodynamical feedback of the fluid is taken into account and a coupling parameter $\alpha$ is introduced allowing the tuning of both the direction of the wave propagation, and the strength of the fluid feedback. A comparative study of both antipleptic and symplectic waves, mapping a cilia inter-spacing ranging from 1.67 up to 5 cilia length, is performed by imposing the metachrony. Antipleptic waves are found to systematically outperform sympletic waves. They are shown to be more efficient for transporting and mixing the fluids, while spending less energy than symplectic, random, or synchronized motions