LoCoML: A Framework for Real-World ML Inference Pipelines
Maddireddy Kritin,Kotekal Methukula Santhosh,Chandrasekar S,Karthik Vaidhyanathan
Conference on AI Engineering, CAIN, 2025
@inproceedings{bib_LoCo_2025, AUTHOR = {Maddireddy Kritin, Kotekal Methukula Santhosh, Chandrasekar S, Karthik Vaidhyanathan}, TITLE = {LoCoML: A Framework for Real-World ML Inference Pipelines}, BOOKTITLE = {Conference on AI Engineering}. YEAR = {2025}}
The widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, especially when dealing with varied technical specifications. These limitations are amplified in large-scale, collaborative projects where stakeholders contribute models with different technical specifications.
To address these challenges, we developed LoCoML, a low-code framework designed to simplify the integration of diverse ML models within the context of the Bhashini Project - a large-scale initiative aimed at integrating AI-driven language technologies such as automatic speech recognition, machine translation, text-to-speech, and optical character recognition to support seamless communication across more than 20 languages. Initial evaluations show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration. Our practical insights show that a low-code approach can be a practical solution for connecting multiple ML models in a collaborative environment.
Approach Towards Semi-Automated Certification of Low Criticality ML-Enabled Airborne Applications
Chandrasekar S,Vyakhya Gupta,Prakhar Jain,Karthik Vaidhyanathan
Conference on AI Engineering, CAIN, 2025
@inproceedings{bib_Appr_2025, AUTHOR = {Chandrasekar S, Vyakhya Gupta, Prakhar Jain, Karthik Vaidhyanathan}, TITLE = {Approach Towards Semi-Automated Certification of Low Criticality ML-Enabled Airborne Applications}, BOOKTITLE = {Conference on AI Engineering}. YEAR = {2025}}
As Machine Learning (ML) makes its way into aviation, ML-enabled systems—including low-criticality systems—require a reliable certification process to ensure safety and performance. Traditional standards, like DO-178C, which are used for critical software in aviation, don’t fully cover the unique aspects of ML. This paper proposes a semi-automated certification approach, specifically for low-criticality ML systems, focusing on data and model validation, resilience assessment, and usability assurance while integrating manual and automated processes. Key aspects include structured classification to guide certification rigor on system attributes, an Assurance Profile that consolidates evaluation outcomes into a confidence measure the ML component, and methodologies for integrating human oversight into certification activities. Through a case study with a YOLOv8-based object detection system designed to classify military and civilian vehicles in real-time for reconnaissance and surveillance aircraft, we show how this approach supports the certification of ML systems in low-criticality airborne applications.
LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World
Meghana Tedla,Shrikara A,Karthik Vaidhyanathan
IEEE International Conference on Software Architecture Companion, ICSA, 2025
@inproceedings{bib_LLMs_2025, AUTHOR = {Meghana Tedla, Shrikara A, Karthik Vaidhyanathan}, TITLE = {LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2025}}
Recently, the exponential growth in capability and pervasiveness of Large Language Models (LLMs) has led to significant work done in the field of code generation. However, this generation has been limited to code snippets. Going one step further, our desideratum is to automatically generate architectural components. This would not only speed up development time, but would also enable us to eventually completely skip the development phase, moving directly from design decisions to deployment. To this end, we conduct an exploratory study on the capability of LLMs to generate architectural components for Functions as a Service (FaaS), commonly known as serverless functions. The small size of their architectural components make this architectural style amenable for generation using current LLMs compared to other styles like monoliths and microservices. We perform the study by systematically selecting open source serverless repositories, masking a serverless function and utilizing state of the art LLMs provided with varying levels of context information about the overall system to generate the masked function. We evaluate correctness through existing tests present in the repositories and use metrics from the Software Engineering (SE) and Natural Language Processing (NLP) domains to evaluate code quality and the degree of similarity between human and LLM generated code respectively. Along with our findings, we also present a discussion on the path forward for using GenAI in architectural component generation.
The Engineering End-to-End Remote Labs using IoT-based Retrofitting
Akshit Gureja,Aftab M. Hussain,Kandala Savitha Viswanadh,Nagesh Laxman Walchatwar,Rishabh Anup Agrawal,Shiven Sinha,Sachin Chaudhari,Karthik Vaidhyanathan,Venkatesh Choppella,Prabhakar Bhimalapuram,Harikumar Kandath
IEEE Access, ACCESS, 2024
@inproceedings{bib_The__2024, AUTHOR = {Akshit Gureja, Aftab M. Hussain, Kandala Savitha Viswanadh, Nagesh Laxman Walchatwar, Rishabh Anup Agrawal, Shiven Sinha, Sachin Chaudhari, Karthik Vaidhyanathan, Venkatesh Choppella, Prabhakar Bhimalapuram, Harikumar Kandath}, TITLE = {The Engineering End-to-End Remote Labs using IoT-based Retrofitting}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}
Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.
Software Architecture and Machine Learning (Dagstuhl Seminar 23302)
Grace Lewis,Henry Muccini,Ipek Ozkaya,Karthik Vaidhyanathan,Roland Weiss,Liming Zhu
Dagstuhl Seminar Report, DSG, 2024
@inproceedings{bib_Soft_2024, AUTHOR = {Grace Lewis, Henry Muccini, Ipek Ozkaya, Karthik Vaidhyanathan, Roland Weiss, Liming Zhu}, TITLE = {Software Architecture and Machine Learning (Dagstuhl Seminar 23302)}, BOOKTITLE = {Dagstuhl Seminar Report}. YEAR = {2024}}
This report documents the program and outcomes of Dagstuhl Seminar 23302, "Software Architecture and Machine Learning". We summarize the goals and format of the seminar, results from the breakout groups, key definitions relevant to machine learning-enabled systems that were discussed, and the research roadmap that emerged from the discussions during the seminar. The report also includes the abstracts of the talks presented at the seminar and summaries of open discussions.
ML-enabled Service Discovery for Microservice Architecture: a QoS Approach
Karthik Vaidhyanathan,Mauro Caporuscio,Stefano Florio,Henry Muccini
ACM Symposium on Applied Computing, SAC, 2024
@inproceedings{bib_ML-e_2024, AUTHOR = {Karthik Vaidhyanathan, Mauro Caporuscio, Stefano Florio, Henry Muccini}, TITLE = {ML-enabled Service Discovery for Microservice Architecture: a QoS Approach}, BOOKTITLE = {ACM Symposium on Applied Computing}. YEAR = {2024}}
Microservice architectures have gained enormous popularity due to their ability to be dynamically added/removed, replicated, and updated according to run-time needs. However, the dynamic nature of microservices introduces uncertainty, which in turn can affect the provided Quality of Service (QoS). This calls for novel service discovery mechanisms able to adapt to the variability of the QoS attributes and further perform effective service discovery and selection. To this end, this paper combines machine learning and self-adaptation techniques to perform service discovery and selection by trading off different QoS attributes. The results of our validation on a state-of-the-art microservices exemplar show that our ML-enabled approach can perform service discovery with 35% higher effectiveness with respect to existing baselines.
Architecting Digital Twin for Smart City Systems: A Case Study
Bala Likhith Kanigolla,Gaurav Pal,Karthik Vaidhyanathan,Deepak Gangadharan,Anuradha Vattem
International Conference on Software Architecture Companion, ICSA-C, 2024
@inproceedings{bib_Arch_2024, AUTHOR = {Bala Likhith Kanigolla, Gaurav Pal, Karthik Vaidhyanathan, Deepak Gangadharan, Anuradha Vattem}, TITLE = {Architecting Digital Twin for Smart City Systems: A Case Study}, BOOKTITLE = {International Conference on Software Architecture Companion}. YEAR = {2024}}
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident’s quality of life. However, implementing smart city projects comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin(DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This innovative concept offers a virtual replica of assets, processes and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of Smart City Systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights, while actuation enables control actions based on predictive analytics and optimization algorithms. To this end, in this paper, we present the architecture of WaterTwin, a DT we developed for water quality networks in smart city systems. We demonstrate our approach through the use case of a water quality network in the smart city living lab of the IIIT Hyderabad campus.
Leveraging Generative AI for Architecture Knowledge Management
Rudra Dhar,Karthik Vaidhyanathan,Vasudeva Varma Kalidindi
International Conference on Software Architecture Companion, ICSA-C, 2024
@inproceedings{bib_Leve_2024, AUTHOR = {Rudra Dhar, Karthik Vaidhyanathan, Vasudeva Varma Kalidindi}, TITLE = {Leveraging Generative AI for Architecture Knowledge Management}, BOOKTITLE = {International Conference on Software Architecture Companion}. YEAR = {2024}}
While documenting Architectural Knowledge (AK) is crucial, it is frequently neglected in many projects, and existing manual tools are underutilized. Although undocumented, Archi-tecture Knowledge (AK) is dispersed across various sources such as source code, documentation, and runtime logs. To address this, automated tools for efficient AK extraction and documentation are essential. Even after generating AK, navigating through vast the Architectural Records can be overwhelming. Building on that, we propose an automated Architectural Knowledge Management (AKM) System using Information Extraction and Generative AI, which generates AK from various source for a given system and answers architectural queries with respect to the given system. The development of an efficient Architectural Knowledge Management (AKM) system, which is both effective and user-friendly, entails the resolution of numerous challenges. It requires consolidating diverse AK data sources scattered across code, dia-grams, repository commits, and online platforms. The integration of Multimodal AI for AK extraction, incorporation of global AK, and leveraging Generative AI for AK documentation further compounds the problem. Moreover, generating contextually appropriate query responses adds another layer of complexity. To this end, we performed an initial exploratory study on generating Architectural Design Decisions using generative Large Language Models (LLM) in the context of Architecture Decision Records (ADR). Our initial results have been promising indicating the potential impact of GenAI for architectural knowledge management.
POSEIDON : Efficient Function Placement at the
Edge using Deep Reinforcement Learning
Prakhar Jain,Prakhar Singhal,Divyansh Pandey,Giovanni Quattrocchi,Karthik Vaidhyanathan
International Conference on Service-Oriented Computing, ICSOC, 2024
@inproceedings{bib_POSE_2024, AUTHOR = {Prakhar Jain, Prakhar Singhal, Divyansh Pandey, Giovanni Quattrocchi, Karthik Vaidhyanathan}, TITLE = {POSEIDON : Efficient Function Placement at the
Edge using Deep Reinforcement Learning}, BOOKTITLE = {International Conference on Service-Oriented Computing}. YEAR = {2024}}
Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures.
However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption; on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.
Reimagining Self-Adaptation in the Age of Large Language Models
Raghav Donakanti,Prakhar Jain,Kulkarni Shubham Shantanu,Karthik Vaidhyanathan
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_Reim_2024, AUTHOR = {Raghav Donakanti, Prakhar Jain, Kulkarni Shubham Shantanu, Karthik Vaidhyanathan}, TITLE = {Reimagining Self-Adaptation in the Age of Large Language Models}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability
to generalize, etc. Recent advancements in Generative AI (GenAI)
open up new possibilities as it is trained on massive amounts
of data, potentially enabling the interpretation of uncertainties
and synthesis of adaptation strategies. In this context, this
paper presents a vision for using GenAI, particularly Large
Language Models (LLMs), to enhance the effectiveness and
efficiency of architectural adaptation. Drawing parallels with
human operators, we propose that LLMs can autonomously gen-
erate similar, context-sensitive adaptation strategies through its
advanced natural language processing capabilities. This method
allows software systems to understand their operational state
and implement adaptations that align with their architectural
requirements and environmental changes. By integrating LLMs
into the self-adaptive system architecture, we facilitate nuanced
decision-making that mirrors human-like adaptive reasoning. A
case study with the SWIM exemplar system provides promising
results, indicating that LLMs can potentially handle different
adaptation scenarios. Our findings suggest that GenAI has signifi-
cant potential to improve software systems’ dynamic adaptability
and resilience.
Towards Architecting Sustainable MLOps: A Self-Adaptation Approach
Hiya Bhatt,Shrikara A,Adyansh Kakran,Karthik Vaidhyanathan
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_Towa_2024, AUTHOR = {Hiya Bhatt, Shrikara A, Adyansh Kakran, Karthik Vaidhyanathan}, TITLE = {Towards Architecting Sustainable MLOps: A Self-Adaptation Approach}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
In today’s dynamic technological landscape, sustainability has emerged as a pivotal concern, especially with respect to architecting Machine Learning enabled Systems (MLS). Many ML models fail in transitioning to production, primarily hindered by uncertainties due to data variations, evolving requirements, and model instabilities. Machine Learning operations (MLOps) offers a promising solution by enhancing adaptability and technical sustainability in MLS. However, MLOps itself faces challenges related to environmental impact, technical maintenance, and economic concerns. Over the years, self-adaptation has emerged as a potential solution to handle uncertainties. This paper introduces a novel approach employing self-adaptive principles integrated into the MLOps architecture through a MAPE-K loop to bolster MLOps sustainability. By autonomously responding to uncertainties, including data, model dynamics, and environmental variations, our approach aims to address the sustainability concerns of a given MLOps pipeline identified by an architect at design time. Further, we implement the method for a Smart City use case to display the capabilities of our approach.
Can LLMs Generate Architectural Design Decisions? - An Exploratory Empirical study
Rudra Dhar,Karthik Vaidhyanathan,Vasudeva Varma Kalidindi
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_Can__2024, AUTHOR = {Rudra Dhar, Karthik Vaidhyanathan, Vasudeva Varma Kalidindi}, TITLE = {Can LLMs Generate Architectural Design Decisions? - An Exploratory Empirical study}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artefact of AKM is the Architecture Decision Records (ADR), which documents key design decisions. ADRs are documents that capture decision context, decision made and various aspects related to a design decision, thereby promoting transparency, collaboration, and understanding. Despite their benefits, ADR adoption in software development has been slow due to challenges like time constraints and inconsistent uptake. Recent advancements in Large Language Models (LLMs) may help bridge this adoption gap by facilitating ADR generation. However, the effectiveness of LLM for ADR generation or understanding is something that has not been explored. To this end, in this work, we perform an exploratory study which aims to investigate the feasibility of using LLM for the generation of ADRs given the decision context. In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context. Our results indicate that in a 0-shot setting, state-of-the-art models such as GPT-4 generate relevant and accurate Design Decisions, although they fall short of human-level performance. Additionally, we observe that more cost-effective models like GPT-3.5 can achieve similar outcomes in a few-shot setting, and smaller models such as Flan-T5 can yield comparable results after fine-tuning. To conclude, this exploratory study suggests that LLM can generate Design Decisions, but further research is required to attain human-level generation and establish standardized widespread adoption.
Exploratory Study of oneM2M-based Interoperability Architectures for IoT: A Smart City Perspective
Vjs Pranavasri,Leo Francis,Gaurav Pal,Ushasri Mogadali,Anuradha Vattem,Karthik Vaidhyanathan,Deepak Gangadharan
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_Expl_2024, AUTHOR = {Vjs Pranavasri, Leo Francis, Gaurav Pal, Ushasri Mogadali, Anuradha Vattem, Karthik Vaidhyanathan, Deepak Gangadharan}, TITLE = {Exploratory Study of oneM2M-based Interoperability Architectures for IoT: A Smart City Perspective}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
The advent of the Internet of Things (IoT) has ushered in transformative possibilities for smart cities, with the potential to revolutionize urban living through enhanced connectivity and data-driven decision-making. However, the effective realization of IoT in smart cities hinges upon the seamless interoperability of diverse devices and systems. To address this critical need, the oneM2M standards initiative has emerged as a foundational framework for IoT interoperability. In this research paper, we perform an exploratory analysis of three prominent open-source oneM2M based interoperability systems—Mobius, OM2M, and ACME. We leverage an existing large-scale system provided by our Smart City Living Lab deployed at IIIT Hyderabad, sprawling a 66-acre campus featuring over 370 nodes across eight verticals. We investigate the architectural characteristics of each solution, considering their strengths and limitations in facilitating IoT interoperability. Through this analysis, our paper aims to provide valuable insights for stakeholders seeking to implement IoT interoperability solutions in the context of smart cities. By evaluating the strengths and limitations of Mobius, OM2M, and ACME, we seek to offer guidance for selecting the most suitable solution. Our analysis reveals that the optimal framework choice depends on specific quality constraints: Mobius excels in performance, while ACME offers advantages in ease of setup for smaller-scale implementations.
SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems
Arya Pravin Marda,Kulkarni Shubham Shantanu,Karthik Vaidhyanathan
Software Engineering for Adaptive and Self-Managing Systems, SEAMS, 2024
@inproceedings{bib_SWIT_2024, AUTHOR = {Arya Pravin Marda, Kulkarni Shubham Shantanu, Karthik Vaidhyanathan}, TITLE = {SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems}, BOOKTITLE = {Software Engineering for Adaptive and Self-Managing Systems}. YEAR = {2024}}
Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept that addresses these uncertainties by facilitating dynamic ML model switching, showing promise in improving QoS in MLS. Leveraging this concept, this paper introduces SWITCH, an exemplar developed to enhance self-adaptive capabilities in such systems through dynamic model switching in runtime. SWITCH is designed as a comprehensive web service catering to a broad range of ML scenarios, with its implementation demonstrated through an object detection use case. SWITCH provides researchers with a flexible platform to apply and evaluate their ML model switching strategies, aiming to enhance QoS in MLS. SWITCH features advanced input handling, real-time data processing, and logging for adaptation metrics supplemented with an interactive real-time dashboard for enhancing system observability. This paper details SWITCH's architecture, self-adaptation strategies through ML model switching, and its empirical validation through a case study, illustrating its potential to improve QoS in MLS. By enabling a hands-on approach to explore adaptive behaviors in ML systems, SWITCH contributes a valuable tool to the SEAMS community for research into self-adaptive mechanisms for MLS and their practical applications.
EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems
Meghana Tedla,Kulkarni Shubham Shantanu,Karthik Vaidhyanathan
IEEE International Conference on Software Architecture Companion, ICSA, 2024
@inproceedings{bib_EcoM_2024, AUTHOR = {Meghana Tedla, Kulkarni Shubham Shantanu, Karthik Vaidhyanathan}, TITLE = {EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems}, BOOKTITLE = {IEEE International Conference on Software Architecture Companion}. YEAR = {2024}}
The sustainability of Machine Learning-Enabled Systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in Machine Learning-Enabled Systems (MLS), where runtime uncertainties can significantly impact model performance and energy consumption. This variability, alongside the fluctuating energy demands of ML models during operation, necessitates a dynamic approach. Addressing these challenges, we introduce EcoMLS approach, which leverages the Machine Learning Model Balancer concept to enhance the sustainability of MLS through runtime ML model switching. By adapting to monitored runtime conditions, EcoMLS optimally balances energy consumption with model confidence, demonstrating a significant advancement towards sustainable, energy-efficient machine learning solutions. Through an object detection exemplar, we illustrate the application of EcoMLS, showcasing its ability to reduce energy consumption while maintaining high model accuracy throughout its use. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations, contributing a valuable perspective to the ongoing discourse on energy-efficient machine learning.
Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
Kulkarni Shubham Shantanu,Arya Pravin Marda,Karthik Vaidhyanathan
Automated Software Engineering, ASE, 2023
@inproceedings{bib_Towa_2023, AUTHOR = {Kulkarni Shubham Shantanu, Arya Pravin Marda, Karthik Vaidhyanathan}, TITLE = {Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching}, BOOKTITLE = {Automated Software Engineering}. YEAR = {2023}}
Machine Learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of Machine Learning-Enabled Systems (MLS). However, numerous software engineering challenges persist in propelling these MLS into production, largely due to various run-time uncertainties that impact the overall Quality of Service (QoS). These uncertainties emanate from ML models, software components, and environmental factors. Self-adaptation techniques present potential in managing run-time uncertainties, but their application in MLS remains largely unexplored. As a solution, we propose the concept of a Machine Learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. Subsequently, we introduce AdaMLS, a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. AdaMLS employs lightweight unsupervised learning for dynamic model switching, thereby ensuring consistent QoS. Through a self-adaptive object detection system prototype, we demonstrate AdaMLS's effectiveness in balancing system and model performance. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in QoS guarantees, heralding the advancement towards self-adaptive MLS with optimal OoS in dynamic environments.
A Tool based Experiment to Teach Elicitation and Specification of Virtual Reality Product Requirements
Sai Anirudh Karre,Karthik Vaidhyanathan,Raghu Babu Reddy Y
ACM Conference on Global Computing Education, CompEd, 2023
@inproceedings{bib_A_To_2023, AUTHOR = {Sai Anirudh Karre, Karthik Vaidhyanathan, Raghu Babu Reddy Y}, TITLE = {A Tool based Experiment to Teach Elicitation and Specification of Virtual Reality Product Requirements}, BOOKTITLE = {ACM Conference on Global Computing Education}. YEAR = {2023}}
Students need to understand the assessment of requirements correctness while building software systems. It helps produce products that meet the stakeholder objectives. This poster illustrates an experiment conducted as a tool-based collaborative assignment for assessing the correctness of software products using a Virtual Reality (VR) application as part of a CS300-level course at our University.
Software Architecture for Multi-User Multiplexing to Enhance Scalability in IoT-Based Remote Labs
Akshit Gureja,Rishabh Anup Agrawal,Sachin Chaudhari,Karthik Vaidhyanathan,Venkatesh Choppella
World Forum on Internet of Things, WF-IoT, 2023
@inproceedings{bib_Soft_2023, AUTHOR = {Akshit Gureja, Rishabh Anup Agrawal, Sachin Chaudhari, Karthik Vaidhyanathan, Venkatesh Choppella}, TITLE = {Software Architecture for Multi-User Multiplexing to Enhance Scalability in IoT-Based Remote Labs}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2023}}
Remote Labs refer to an end-to-end system, including hardware and software built to access scientific equipment and resources remotely. The software platform built for such purposes needs to be robust enough to handle the communication of inputs and outputs between the client and the hardware nodes with minimal latency and simultaneously provide a seamless user experience. This paper highlights the importance of scalability in Remote Labs and presents multi-user multiplexing as a solution, to essentially provide users with concurrent access to the hardware node for experiments which can generate outputs instantaneously. The paper discusses the inefficiency of existing web-based Remote Labs with 4-layered architectures and proposes the use of WebSocket with a 3-layer software architecture to enhance user experience, accelerate input-output communication and implement multi-user multiplexing. To showcase the effectiveness of the proposed architecture over existing implementations using Blynk IoT platform as the middleware, a comprehensive communication pipeline was developed from scratch to perform Kirchhoff’s Voltage Law (KVL) experiment remotely.
Scalable and Interoperable Distributed Architecture for IoT in Smart Cities
Vjs Pranavasri,Leo Francis,Ushasri Mogadali,Gaurav Pal,Vaddhiparthy s V S L N Surya Suhas,Anuradha Vattem,Karthik Vaidhyanathan,Deepak Gangadharan
Technical Report, arXiv, 2023
@inproceedings{bib_Scal_2023, AUTHOR = {Vjs Pranavasri, Leo Francis, Ushasri Mogadali, Gaurav Pal, Vaddhiparthy s V S L N Surya Suhas, Anuradha Vattem, Karthik Vaidhyanathan, Deepak Gangadharan}, TITLE = {Scalable and Interoperable Distributed Architecture for IoT in Smart Cities}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
The increase of IoT devices and the emergence of smart cities have revolutionized urban development, offering numerous benefits while addressing environmental concerns. This has caused an increase in the usage of IoT frameworks, and the need for efficient architecture and standardized ontology is imminent. In this regard, we propose a distributed, multi-layered data platform architecture comprising the Data Monitoring Layer (DML), Data Storage Layer (DSL), Data Enhancement Layer (DEnL), and Data Exchange Layer (DEL). Our architecture achieves interoperability, facilitates data transfer between nodes, enables telemetry data retrieval, and ensures cross-platform and cross-device compatibility. It addresses the challenges of handling increased sensor data and user demands by providing high throughput and scalability support. We investigated Smart City Living Lab at IIIT Hyderabad an existing large-scale system deployed within a 66-acre campus. This system consists of 291 nodes. By studying this deployed system, we were able to gather valuable real-world data, allowing us to analyze the challenges and potential solutions related to data architecture. Our results show improvements of up to 41.23\% in throughput and a decrease in latency by 29.19\% for data insertion from the sensor nodes. The retrieval by the data client gives an increase of over 800\% in both throughput and number of requests through DENL. These metrics are compared to a centralised data platform architecture. We conclude by discussing the implications of our findings and suggesting future work.
Emoticontrol: Emotions-based Control of User-Interfaces Adaptations
Mina Alipour,Mahyar T Moghaddam,Karthik Vaidhyanathan,Mikkel Baun Kjærgaard
Proceedings of the ACM on Human-Computer Interaction, PACMHCI, 2023
@inproceedings{bib_Emot_2023, AUTHOR = {Mina Alipour, Mahyar T Moghaddam, Karthik Vaidhyanathan, Mikkel Baun Kjærgaard}, TITLE = {Emoticontrol: Emotions-based Control of User-Interfaces Adaptations}, BOOKTITLE = {Proceedings of the ACM on Human-Computer Interaction}. YEAR = {2023}}
Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience …
Toward Changing Users behavior with Emotion-based Adaptive Systems
Mina Alipour,Mahyar Tourchi Moghaddam,Karthik Vaidhyanathan,Mikkel Baun Kjærgaard
ACM Conference on User Modeling, Adaptation and Personalization, UMAP, 2023
@inproceedings{bib_Towa_2023, AUTHOR = {Mina Alipour, Mahyar Tourchi Moghaddam, Karthik Vaidhyanathan, Mikkel Baun Kjærgaard}, TITLE = {Toward Changing Users behavior with Emotion-based Adaptive Systems}, BOOKTITLE = {ACM Conference on User Modeling, Adaptation and Personalization}. YEAR = {2023}}
Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities. Emotions are integral parts of human nature, and ignoring that can lead the interactive systems to failure, low quality, or discomfort. User interfaces (UIs) are increasingly becoming adaptive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions. However, the previous approaches proposed for supervising such adaptations are not effectively adopted in real-life problems. This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions. The research includes literature analysis, surveys, and further adopting an iterative process in implementation and experimentation. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.
