Browsing by Author "Shashank, P."

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    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.
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    Towards a Federated Learning Approach for NLP Applications
    (Springer Science and Business Media Deutschland GmbH, 2021) Prabhu, O.S.; Gupta, P.K.; Shashank, P.; Chandrasekaran, K.; Divakarla, D.
    Traditional machine learning involves the collection of training data to a centralized location. This collected data is prone to misuse and data breach. Federated learning is a promising solution for reducing the possibility of misusing sensitive user data in machine learning systems. In recent years, there has been an increase in the adoption of federated learning in healthcare applications. On the other hand, personal data such as text messages and emails also contain highly sensitive data, typically used in natural language processing (NLP) applications. In this paper, we investigate the adoption of federated learning approach in the domain of NLP requiring sensitive data. For this purpose, we have developed a federated learning infrastructure that performs training on remote devices without the need to share data. We demonstrate the usability of this infrastructure for NLP by focusing on sentiment analysis. The results show that the federated learning approach trained a model with comparable test accuracy to the centralized approach. Therefore, federated learning is a viable alternative for developing NLP models to preserve the privacy of data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.