Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Development of Region-Specific New Generation Attenuation Relations for North India Using Artificial Neural Networks(Springer Science and Business Media Deutschland GmbH, 2021) Huang, H.; Ramkrishnan, R.; Kolathayar, S.; Garg, A.; Yadav, J.S.Present study focuses on developing region-specific New Generation Ground Motion Prediction Models using Artificial intelligence technique for North India purely based on a measured ground motion data from specific region. Simple single hidden layered feed forward multilayer perceptron networks with back-propagation learning algorithm are used. A total of 280 data points of recorded strong motion data from the Kangra and Uttar Pradesh (UP) arrays, made available by the Program for Excellence in Strong Motion Studies (PESMOS), were used to train these networks. The first model predicts Moment Magnitude for a given Hypocentral Distance and Peak Ground Acceleration. The second model predicts Peak Ground Acceleration (PGA) for a given Hypocentral Distance (HPD) and Moment Magnitude (MM). Performance analysis, Uncertainty analysis and analysis of interactive effects have been done to test the reliability of the generated models. Optimization analysis was also performed to predict possible inputs of the models for a given set of outputs. Models have performed reasonably well for the given amount of non-linearity in the data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Hummingbird: Leveraging Heterogeneous System Architecture for deploying dynamic NFV chains(Institute of Electrical and Electronics Engineers Inc., 2022) Chaurasia, A.K.; Raman, B.; Gupta, P.K.; Prabhu, O.; Shashank, P.; Garg, A.Network Function Virtualization has gained traction as a network function deployment alternative due to its flexibility and cost benefits. The telecommunication (telecom) operators and infrastructure providers are looking for high throughput, low latency NFV deployment model to avail the benefits of NFV. Moreover, NFV is one of the core technology for the next-generation communication network such as 5G. Furthermore, telecom operators employ groups of network functions(NFs) that process packets in linear order so that the output of one NF becomes an input for another, thus forming the network function chain (NFC). However, these NFCs should be flexible, as all telecom packets do not necessarily need to be processed by the same set of NFs. It has been earlier shown that GPU increases the throughput of NFV chains. To the best of our knowledge, none of the GPU-based frameworks supports dynamic NFV chains. Furthermore, discrete GPUs are expensive and consume a fair amount of energy. This paper presents the design and evaluation of Hummingbird, a framework to support high throughput, dynamically routed NFV chain on Heterogeneous System Architecture (HSA). Though HSAs are affordable and power-efficient, they lack high throughput GPU-CPU synchronization. Furthermore, current technology does not provide a zero-copy mechanism for network IO between GPU and NIC for HSAs. Hummingbird addressed those challenges. As per our knowledge, this is the first such framework that provides high throughput dynamic NFV chaining, with NFs chained across GPU and CPU and designed in conformance to OpenCL 2.0 standard. Hummingbird achieves 6x throughput per-core and 3.5x throughput per unit of energy consumption compared to state-of-the-art NFV deployment framework G-net, which uses powerful and costly discrete GPU. © 2022 IEEE.