arXiv subjects

Siddharth Agarwal

Publications and source records attributed to Siddharth Agarwal.

At least 19 recordsLinked to original sources

arXiv2025

Saarthi: An End-to-End Intelligent Platform for Optimising Distributed Serverless Workloads

FaaS offers significant advantages with its infrastructure abstraction, on-demand execution, and attractive no idle resource pricing for modern cloud applications. Despite these benefits, challenges such as startup latencies, static configurations, sub-optimal resource allocation and scheduling still exist due to coupled resource offering and workload-agnostic generic scheduling behaviour. These issues often lead to inconsistent function performance and unexpected operational costs for users and service providers. This paper introduces Saarthi, a novel, end-to-end serverless framework that intelligently manages the dynamic resource needs of function workloads, representing a significant step toward self-driving serverless platforms. Unlike platforms that rely on static resource configurations, Saarthi is input-aware, allowing it to intelligently anticipate resource requirements based on the characteristics of an incoming request payload. This input-driven approach reinforces function right-sizing and enables smart request orchestration across available function configurations. Saarthi further integrates a proactive fault-tolerant redundancy mechanism and employs a multi-objective Integer Linear Programming (ILP) model to maintain an optimal function quantity. This optimisation aims to maximise system throughput while simultaneously reducing overall operational costs. We validate the effectiveness of Saarthi by implementing it as a framework atop OpenFaaS. Our results demonstrate Saarthi's ability to achieve up to 1.45x better throughput, 1.84x reduced costs, while maintaining up to 98.3% service level targets with an overhead of up to 0.2 seconds as compared to the baseline OpenFaaS.

cs.DC↗

arXiv2025

Serv-Drishti: An Interactive Serverless Function Request Simulation Engine and Visualiser

The rapid adoption of serverless computing necessitates a deeper understanding of its underlying operational mechanics, particularly concerning request routing, cold starts, function scaling, and resource management. This paper presents Serv-Drishti, an interactive, open-source simulation tool designed to demystify these complex behaviours. Serv-Drishti simulates and visualises the journey of a request through a representative serverless platform, from the API Gateway and intelligent Request Dispatcher to dynamic Function Instances on resource-constrained Compute Nodes. Unlike simple simulators, Serv-Drishti provides a robust framework for comparative analysis. It features configurable platform parameters, multiple request routing and function placement strategies, and a comprehensive failure simulation module. This allows users to not only observe but also rigorously analyse system responses under various loads and fault conditions. The tool generates real-time performance graphs and provides detailed data exports, establishing it as a valuable resource for research, education, and the design analysis of serverless architectures.

cs.NI↗

arXiv2025

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions

The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its successful execution. This resource allocation influences both the operational expense and the performance quality of these functions. However, a noticeable lack of platform transparency forces developers to rely on expert knowledge or experience-based ad-hoc decisions to request desired function resources. This makes optimal resource configuration a non-trivial task while adhering to performance constraints. Furthermore, while commercial platforms often scale resources like CPU and network bandwidth proportional to memory, open-source frameworks permit independent configuration of function resources, introducing additional complexity for developers aiming to optimise their functions. These complexities have directed researchers to resolve developer challenges and advance towards an efficient server-less execution model. In this article, we identify different aspects of resource configuration techniques in FaaS settings and propose a taxonomy of factors that influence function design, configuration, run-time cost, and performance guarantees. We conduct an analysis of existing literature on resource configuration to present a comprehensive review of current studies on function configuration. We also identify existing research gaps and suggest future research directions to enhance function configuration and strengthen the capabilities of serverless computing environments to drive its broader adoption.

cs.SE↗

arXiv2024

Camera-Based Localization and Enhanced Normalized Mutual Information

Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localization algorithms must be designed. This work considers scenarios where the sensor data comes from images captured by an inexpensive camera mounted on the vehicle and where the vehicle contains a fine global map. Such localization algorithms typically involve finding the section in the global map that best matches the captured image. In harsh environments, both the global map and the captured image can be noisy. Because of physical constraints on camera placement, the image captured by the camera can be viewed as a noisy perspective transformed version of the road in the global map. Thus, an optimal algorithm should take into account the unequal noise power in various regions of the captured image, and the intrinsic uncertainty in the global map due to environmental variations. This article briefly reviews two matching methods: (i) standard inner product (SIP) and (ii) normalized mutual information (NMI). It then proposes novel and principled modifications to improve the performance of these algorithms significantly in noisy environments. These enhancements are inspired by the physical constraints associated with autonomous vehicles. They are grounded in statistical signal processing and, in some context, are provably better. Numerical simulations demonstrate the effectiveness of such modifications.

cs.CV↗

arXiv2024

Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. Results: Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis. Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.

cs.CV↗

arXiv2024

On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing

Function-as-a-Service (FaaS) is a cloud computing paradigm offering an event-driven execution model to applications. It features serverless attributes by eliminating resource management responsibilities from developers, and offers transparent and on-demand scalability of applications. To provide seamless on-demand scalability, new function instances are prepared to serve the incoming workload in the absence or unavailability of function instances. However, FaaS platforms are known to suffer from cold starts, where this function provisioning process introduces a non-negligible delay in function response and reduces the end-user experience. Therefore, the presented work focuses on reducing the frequent, on-demand cold starts on the platform by using Reinforcement Learning(RL). The proposed approach uses model-free Q-learning that consider function metrics such as CPU utilization, existing function instances, and response failure rate, to proactively initialize functions, in advance, based on the expected demand. The proposed solution is implemented on Kubeless and evaluated using an open-source function invocation trace applied to a matrix multiplication function. The evaluation results demonstrate a favourable performance of the RL-based agent when compared to Kubeless' default policy and a function keep-alive policy by improving throughput by up to 8.81% and reducing computation load and resource wastage by up to 55% and 37%, respectively, that is a direct outcome of reduced cold starts.

cs.DC↗

arXiv2024

Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions

In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc configuration decisions. Recent research has highlighted that a function's input characteristics, such as input size, type and number of inputs, significantly impact its resource demand, run-time performance and costs with fluctuating workloads. This correlation further makes memory configuration a non-trivial task. On that account, an input-aware function memory allocator not only improves developer productivity by completely hiding resource-related decisions but also drives an opportunity to reduce resource wastage and offer a finer-grained cost-optimised pricing scheme. Therefore, we present MemFigLess, a serverless solution that estimates the memory requirement of a serverless function with input-awareness. The framework executes function profiling in an offline stage and trains a multi-output Random Forest Regression model on the collected metrics to invoke input-aware optimal configurations. We evaluate our work with the state-of-the-art approaches on AWS Lambda service to find that MemFigLess is able to capture the input-aware resource relationships and allocate upto 82% less resources and save up to 87% run-time costs.

cs.DC↗

arXiv2024

A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions

FaaS introduces a lightweight, function-based cloud execution model that finds its relevance in a range of applications like IoT-edge data processing and anomaly detection. While cloud service providers offer a near-infinite function elasticity, these applications often experience fluctuating workloads and stricter performance constraints. A typical CSP strategy is to empirically determine and adjust desired function instances or resources, known as autoscaling, based on monitoring-based thresholds such as CPU or memory, to cope with demand and performance. However, threshold configuration either requires expert knowledge, historical data or a complete view of the environment, making autoscaling a performance bottleneck that lacks an adaptable solution. RL algorithms are proven to be beneficial in analysing complex cloud environments and result in an adaptable policy that maximizes the expected objectives. Most realistic cloud environments usually involve operational interference and have limited visibility, making them partially observable. A general solution to tackle observability in highly dynamic settings is to integrate Recurrent units with model-free RL algorithms and model a decision process as a POMDP. Therefore, in this paper, we investigate model-free Recurrent RL agents for function autoscaling and compare them against the model-free PPO algorithm. We explore the integration of a LSTM network with the state-of-the-art PPO algorithm to find that under our experimental and evaluation settings, recurrent policies were able to capture the environment parameters and show promising results for function autoscaling. We further compare a PPO-based autoscaling agent with commercially used threshold-based function autoscaling and posit that a LSTM-based autoscaling agent is able to improve throughput by 18%, function execution by 13% and account for 8.4% more function instances.

cs.DC↗

arXiv2024

A self-supervised text-vision framework for automated brain abnormality detection

Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., 'there is an acute stroke', 'there is hydrocephalus' etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors.

cs.CV↗

arXiv2024

Letter to the Editor: What are the legal and ethical considerations of submitting radiology reports to ChatGPT?

This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports. While acknowledging the potential of LLMs in generating labels for computer vision, concerns are raised about the ethical implications of using patient data without explicit approval, highlighting the necessity of stringent data protection measures under GDPR.

cs.CV↗

arXiv2024

Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis

Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line CT or MR neuroimaging. A bivariate random-effects model was used for meta-analysis where appropriate. PROSPERO: CRD42021269563. Results: Only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies. 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial haemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% CI 0.85 - 0.94) and 0.90 (95% CI 0.83 - 0.95) respectively. Other AI studies using CT and MRI detected target conditions other than haemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.

eess.IV↗

arXiv2023

Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices

The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology has recently gained notable attention for its ability not only to economically expand memory capacity and bandwidth but also to decouple memory technologies from a specific memory interface of the CPU. However, since CXL memory devices have not been widely available, they have been emulated using DDR memory in a remote NUMA node. In this paper, for the first time, we comprehensively evaluate a true CXL-ready system based on the latest 4th-generation Intel Xeon CPU with three CXL memory devices from different manufacturers. Specifically, we run a set of microbenchmarks not only to compare the performance of true CXL memory with that of emulated CXL memory but also to analyze the complex interplay between the CPU and CXL memory in depth. This reveals important differences between emulated CXL memory and true CXL memory, some of which will compel researchers to revisit the analyses and proposals from recent work. Next, we identify opportunities for memory-bandwidth-intensive applications to benefit from the use of CXL memory. Lastly, we propose a CXL-memory-aware dynamic page allocation policy, Caption to more efficiently use CXL memory as a bandwidth expander. We demonstrate that Caption can automatically converge to an empirically favorable percentage of pages allocated to CXL memory, which improves the performance of memory-bandwidth-intensive applications by up to 24% when compared to the default page allocation policy designed for traditional NUMA systems.

cs.PF↗

arXiv2023

Enabling Kernel Bypass Networking on gem5

Full-system simulation of computer systems is critical to capture the complex interplay between various hardware and software components in future systems. Modeling the network subsystem is indispensable to the fidelity of the full-system simulation due to the increasing importance of scale-out systems. The network software stack has undergone major changes over the last decade, and kernel-bypass networking stacks and data-plane networks are rapidly replacing the conventional kernel network stack. Nevertheless, the current state-of-the-art architectural simulators still use kernel networking which precludes realistic network application scenarios. In this work, we enable kernel bypass networking stack on gem5, the state-of-the-art full-system architectural simulator. We extend gem5's NIC hardware model and device driver to enable the support for userspace device drivers to run the DPDK framework. We also implement a network load generator hardware model in gem5 to generate various traffic patterns and perform per-packet timestamp and latency measurements without introducing packet loss. Our experimental results show that DPDK's simulated network bandwidth scales with the number of cores and NIC ports. As two use cases, we analyze the sensitivity of (1) network performance to several microarchitectural parameters, and (2) direct cache access (DCA) technology to DPDK burst size.

cs.AR↗

arXiv2023

Investigations on convergence behaviour of Physics Informed Neural Networks across spectral ranges and derivative orders

An important inference from Neural Tangent Kernel (NTK) theory is the existence of spectral bias (SB), that is, low frequency components of the target function of a fully connected Artificial Neural Network (ANN) being learnt significantly faster than the higher frequencies during training. This is established for Mean Square Error (MSE) loss functions with very low learning rate parameters. Physics Informed Neural Networks (PINNs) are designed to learn the solutions of differential equations (DE) of arbitrary orders; in PINNs the loss functions are obtained as the residues of the conservative form of the DEs and represent the degree of dissatisfaction of the equations. So there has been an open question whether (a) PINNs also exhibit SB and (b) if so, how does this bias vary across the orders of the DEs. In this work, a series of numerical experiments are conducted on simple sinusoidal functions of varying frequencies, compositions and equation orders to investigate these issues. It is firmly established that under normalized conditions, PINNs do exhibit strong spectral bias, and this increases with the order of the differential equation.

cs.LG↗

arXiv2022

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($Δ$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.

eess.IV↗

arXiv2021

S-BEV: Semantic Birds-Eye View Representation for Weather and Lighting Invariant 3-DoF Localization

We describe a light-weight, weather and lighting invariant, Semantic Bird's Eye View (S-BEV) signature for vision-based vehicle re-localization. A topological map of S-BEV signatures is created during the first traversal of the route, which are used for coarse localization in subsequent route traversal. A fine-grained localizer is then trained to output the global 3-DoF pose of the vehicle using its S-BEV and its coarse localization. We conduct experiments on vKITTI2 virtual dataset and show the potential of the S-BEV to be robust to weather and lighting. We also demonstrate results with 2 vehicles on a 22 km long highway route in the Ford AV dataset.

cs.CV↗

arXiv2020

Enhanced Normalized Mutual Information for Localization in Noisy Environments

Fine localization is a crucial task for autonomous vehicles. Although many algorithms have been explored in the literature for this specific task, the goal of getting accurate results from commodity sensors remains a challenge. As autonomous vehicles make the transition from expensive prototypes to production items, the need for inexpensive, yet reliable solutions is increasing rapidly. This article considers scenarios where images are captured with inexpensive cameras and localization takes place using pre-loaded fine maps of local roads as side information. The techniques proposed herein extend schemes based on normalized mutual information by leveraging the likelihood of shades rather than exact sensor readings for localization in noisy environments. This algorithmic enhancement, rooted in statistical signal processing, offers substantial gains in performance. Numerical simulations are used to highlight the benefits of the proposed techniques in representative application scenarios. Analysis of a Ford image set is performed to validate the core findings of this work.

eess.SP↗

arXiv2020

Aerial Imagery based LIDAR Localization for Autonomous Vehicles

This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments. Traditional localization techniques using LIDAR reflectivity rely on high definition reflectivity maps generated from a mapping vehicle. The cost and effort required to maintain such prior maps are generally very high because it requires a fleet of expensive mapping vehicles. In this work we propose a localization technique where the vehicle localizes using aerial/satellite imagery, eradicating the need to develop and maintain complex high-definition maps. The proposed technique has been tested on a real world dataset collected from a test track in Ann Arbor, Michigan. This research concludes that aerial imagery based maps provides real-time localization performance similar to state-of-the-art LIDAR based maps for autonomous vehicles in urban environments at reduced costs.

cs.RO↗