arXiv subjects

Devki Nandan Jha

Publications and source records attributed to Devki Nandan Jha.

5 recordsLinked to original sources

arXiv2026

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda

The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, the computational demands of these models, particularly in training and inference, present significant challenges. Traditional systems are often unable to meet these requirements, necessitating the integration of cloud-native and distributed architectures. This paper explores the role of cloud platforms and distributed systems in supporting the scalability, efficiency, and optimization of LLMs. We discuss the complexities of LLM deployment, including data management, resource optimization, and the need for microservices, autoscaling, and hybrid cloud-edge solutions. Additionally, we examine emerging research trends, such as serverless inference, quantum computing, and federated learning, and their potential to drive the next phase of LLM innovation. The paper concludes with a roadmap for future developments, emphasizing the need for continued research, standardization, and cross-sector collaboration to sustain the growth of LLMs in both research and enterprise applications.

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arXiv2026

Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems

Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastructure. These benefits are crucial for applications requiring real-time data processing or strict security measures. Despite these advantages, edge devices operating within edge clusters are often underutilized. This inefficiency is mainly due to the absence of a holistic performance profiling mechanism which can help dynamically adjust the desired system configuration for a given workload. Since edge computing environments involve a complex interplay between CPU frequency, power consumption, and application performance, a deeper understanding of these correlations is essential. By uncovering these relationships, it becomes possible to make informed decisions that enhance both computational efficiency and energy savings. To address this gap, this paper evaluates the power consumption and performance characteristics of a single processing node within an edge cluster using a synthetic microbenchmark by varying the workload size and CPU frequency. The results show how an optimal measure can lead to optimized usage of edge resources, given both performance and power consumption.

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arXiv2026

EVECTOR: An orchestrator for analysing attacks in electric vehicles charging system

Electric Vehicle (EV) charging infrastructure is critical for the widespread adoption of EVs, ensuring efficient and secure charging processes. Evaluating the security and performance of EV charging systems in real-world infrastructure poses significant challenges due to the diversity of information exchange between vehicles and charging stations/Electric Vehicle Supply Equipment (EVSE), including complex network protocols, scale of deployment and a variety of potential threats. Existing simulation frameworks are unable to handle complex security scenarios across these differing data exchange protocols. In this paper, we propose a novel EV orchestration framework: EVECTOR, which addresses the limitations of existing simulation systems by enabling both quantitative and qualitative analyses of EV charging scenarios. EVECTOR also provides a flexible attack orchestrator to simulate realistic attack behaviours on EV charging infrastructure. We validate the EVECTOR framework through two case studies: (a) cyber-physical attacks such as broken wire; and (b) cyber-specific attacks such as frame fuzzification. The case studies highlight the effectiveness of EVECTOR in providing deeper insights into the security and performance of EV charging systems.

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arXiv2020

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application. We outline core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used in individual stage.

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arXiv2019

IoTSim-Edge: A Simulation Framework for Modeling the Behaviour of IoT and Edge Computing Environments

This paper proposes a novel simulator IoTSim-Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim-Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim-Edge is described using three test cases. The results show the varying capability of IoTSim-Edge in terms of application composition, battery-oriented modeling, heterogeneous protocols modeling and mobility modeling along with the resources provisioning for IoT applications.

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