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Item type:Publication, Access status: without files , Latency-aware placement of stream processing operators(Springer, 2024); ; ; The rise of the Internet of Things and Fog computing has increased substantially the number of interconnected devices at the edge of the network. As a result, a large amount of computations is now performed in the fog generating vast amounts of data. To process this data in near real time, stream processing is typically employed due to its efficiency in handling continuous streams of information in a scalable manner. However, most stream processing approaches do not consider the underlying network devices as candidate resources for processing data. Moreover, many existing works do not take into account the incurred network latency of performing computations on multiple devices in a distributed way. Consequently, the fog computing resources may not be fully exploited by existing stream processing approaches. To avoid this, we formulate an optimization problem for utilizing the existing fog resources, and we design heuristics for solving this problem efficiently. Furthermore, we integrate our heuristics into Apache Storm, and we perform experiments that show latency-related benefits compared to alternatives.Publicationtype: Conference Paper9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Parareal with a physics-informed neural network as coarse propagatorParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal’s single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.Publicationtype: Conference Paper43 73