arXiv subjects

Harshit Shah

Publications and source records attributed to Harshit Shah.

4 recordsLinked to original sources

arXiv2026

A Resource-centric Analysis and Optimization of NoSQL Workloads using Distressed Resource Volume Metric

Large-scale managed cloud databases leverage sophisticated load Packing and Migration (PAM) algorithms, which provide the efficiencies necessary for running these services at scale on cloud resources. Research into optimizing the resources and reliability of cloud databases at massive scales is limited by a lack of public NoSQL workloads. We address this in the context of Cosmos DB, Microsoft's flagship cloud-hosted NoSQL database. We first propose open-source NoSQL workloads from real Cosmos DB clusters, and analyze these traces to derive a novel reliability metric, Distressed Resource Volume (DRV), which captures the quality of service experienced by the end user. We then develop an open-source policy simulation framework, LoadStar, powered by a non-parametric statistical model of estimating the QoS of real traffic patterns. These form a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads. We then define a resource optimization problem for placing Cosmos DB replicas onto VM nodes, develop the Luna model for forecasting future load distributions, and the Orbit PAM algorithm that uses these forecasts to trigger and rebalance stressed replicas, to reduce tail-errors. Our experiments, validated using LoadStar for these workloads, demonstrate Orbit's benefits over the existing Cosmos DB policy and a worst-fit optimized baseline, with higher load delivered at lower error rates and up to $35\%$ reduction in resources. These have been deployed in production, with potential savings of $\$100M$s/yr while improving service reliability for millions of customers.

cs.DC↗

arXiv2023

Data-Free Model Extraction Attacks in the Context of Object Detection

A significant number of machine learning models are vulnerable to model extraction attacks, which focus on stealing the models by using specially curated queries against the target model. This task is well accomplished by using part of the training data or a surrogate dataset to train a new model that mimics a target model in a white-box environment. In pragmatic situations, however, the target models are trained on private datasets that are inaccessible to the adversary. The data-free model extraction technique replaces this problem when it comes to using queries artificially curated by a generator similar to that used in Generative Adversarial Nets. We propose for the first time, to the best of our knowledge, an adversary black box attack extending to a regression problem for predicting bounding box coordinates in object detection. As part of our study, we found that defining a loss function and using a novel generator setup is one of the key aspects in extracting the target model. We find that the proposed model extraction method achieves significant results by using reasonable queries. The discovery of this object detection vulnerability will support future prospects for securing such models.

cs.CR↗

arXiv2023

TIRCAM2 Camera Interface on the Side port of the 3.6 meter Devasthal Optical Telescope

The TIFR Near Infrared Imaging Camera-II (TIRCAM2) is being used at the 3.6 m Devasthal Optical Telescope (DOT) operated by Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital, Uttarakhand, India. Earlier, the TIRCAM2 was used at the main port of the DOT on time shared basis. It has now been installed at the side port of the telescope. Side port installation allows near simultaneous observations with the main port instrument as well as longer operating periods. Thus, the TIRCAM2 serves the astronomical community for a variety of observations ranging from lunar occultations, transient events and normal scheduled observations.

astro-ph.IM↗

arXiv2022

TANSPEC: TIFR-ARIES Near Infrared Spectrometer

We present the design and performance of the TANSPEC, a medium-resolution $0.55-2.5~\mu$m cryogenic spectrometer and imager, now in operation at the 3.6-m Devasthal Optical Telescope (DOT), Nainital, India. The TANSPEC provides three modes of operation which include, photometry with broad- and narrow-band filters, spectroscopy with short slits of 20$^{\prime \prime}$ length and different widths (from 0.5$^{\prime \prime}$ to 4.0$^{\prime \prime}$) in cross-dispersed mode at a resolving power R of $\sim$2750, and spectroscopy with long slits of 60$^{\prime \prime}$ length and different widths (from 0.5$^{\prime \prime}$ to 4.0$^{\prime \prime}$) in prism mode at a resolving power R of $\sim$100-350. TANSPEC's imager mode provides a field of view of 60$^{\prime \prime} \times 60^{\prime \prime}$ with a plate scale of 0.245$^{\prime \prime}$/pixel on the 3.6-m DOT. The TANSPEC was successfully commissioned during April-May 2019 and the subsequent characterization and astronomical observations are presented here. The TANSPEC has been made available to the worldwide astronomical community for science observations from October 2020.

astro-ph.IM↗