Faculty Publications
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Publications by NITK Faculty
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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.Item Affective Feedback Synthesis Towards Multimodal Text and Image Data(Association for Computing Machinery, 2023) Kumar, P.; Bhatt, G.; Ingle, O.; Goyal, D.; Raman, B.In this article, we have defined a novel task of affective feedback synthesis that generates feedback for input text and corresponding images in a way similar to humans responding to multimodal data. A feedback synthesis system has been proposed and trained using ground-truth human comments along with image-text input. We have also constructed a large-scale dataset consisting of images, text, Twitter user comments, and the number of likes for the comments by crawling news articles through Twitter feeds. The proposed system extracts textual features using a transformer-based textual encoder. The visual features have been extracted using a Faster region-based convolutional neural networks model. The textual and visual features have been concatenated to construct multimodal features that the decoder uses to synthesize the feedback. We have compared the results of the proposed system with baseline models using quantitative and qualitative measures. The synthesized feedbacks have been analyzed using automatic and human evaluation. They have been found to be semantically similar to the ground-truth comments and relevant to the given text-image input. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.