Sushant Kumar Gupta
Sushant Kumar Gupta is a software engineer at Google, working on the development of advertising infrastructure. He has a strong foundation in distributed systems and system performance optimization, complemented by extensive practical experience in networking, blockchain technologies, and cloud computing. He is also an active researcher and published author in the field of computer systems.
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Fast ACS: Low-Latency File-Based Ordered Message Delivery at Scale
Anil Raghunath Iyer
Neel Bagora
Chang Yu
Olivier Pomerleau
Vivek Kumar
Prunthaban Kanthakumar
Usenix Annual Technical Conference (2025)
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Low-latency message delivery is crucial for real-time systems. Data originating from a producer must be delivered to consumers, potentially distributed in clusters across metropolitan and continental boundaries. With the growing scale of computing, there can be several thousand consumers of the data. Such systems require a robust messaging system capable of transmitting messages containing data across clusters and efficiently delivering them to consumers. The system must offer guarantees like ordering and at-least-once delivery while avoiding overload on consumers, allowing them to consume messages at their own pace.
This paper presents the design of Fast ACS (an abbreviation for Ads Copy Service), a file-based ordered message delivery system that leverages a combination of two-sided (inter-cluster) and one-sided (intra-cluster) communication primitives—namely, Remote Procedure Call and Remote Direct Memory Access, respectively—to deliver messages. The system has been successfully deployed to dozens of production clusters and scales to accommodate several thousand consumers within each cluster, which amounts to Tbps-scale intra-cluster consumer traffic at peak. Notably, Fast ACS delivers messages to consumers across the globe within a few seconds or even sub-seconds (p99) based on the message volume and consumer scale, at a low resource cost.
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Statistical Analysis of Cardiovascular Diseases Dataset of BRFSS
Ashank Anshuman
Aakarshit Uppal
Indrajit Mukherjee
Open Access Library Journal, 11 (2024)
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Cardiovascular Diseases (CVDs) remain a leading cause of death in the United States. These diseases, including coronary heart disease, heart attack, and stroke, pose significant health risks. Accurate prediction of CVD probability can aid in prevention and management. To address this challenge, we analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning 1995-2017. We developed innovative methods to handle missing data and normalize values. Deep learning models were employed to predict risk factors and, subsequently, the likelihood of CVDs. Our models were implemented using TensorFlow and trained on a high-performance computing server. The models accurately predicted risk factors with over 90% accuracy, enabling targeted interventions. We successfully predicted CVD probability with greater than 95% accuracy, providing valuable insights for healthcare providers. An online portal was developed to forecast CVD trends over the next 31 years, facilitating proactive planning and resource allocation.
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Distributed Tracing for InterPlanetary File System
Marshall David Miller
Rachel Han
Haorui Guo
2024 International Symposium on Parallel Computing and Distributed Systems (PCDS), IEEE, pp. 1-5
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The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS.
In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment.
We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.
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On-Chain Crypto-Secured Credential Verification On Permissioned Blockchain
2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS), IEEE, pp. 1-6
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Verifying credentials, such as educational degrees, professional licenses, and permits, is a crucial yet challenging task for organizations globally. Traditional verification methods often rely on third-party vendors, introducing vulnerabilities like bias, security breaches, and privacy risks. While blockchain technology offers a promising solution for credential management, existing approaches often store sensitive credential data off-chain in centralized databases or InterPlanetary File System (IPFS), leaving them susceptible to data breaches and loss.
This paper presents a novel, privacy-preserving credential verification system built on a permissioned blockchain network. This system, implemented using the Hyperledger Fabric framework, offers several key advantages over traditional methods, including enhanced security and improved privacy. By leveraging cryptographic techniques, the system ensures the robust and privacypreserving storage of credentials directly on the blockchain. This eliminates the reliance on vulnerable off-chain storage and mitigates associated risks. Furthermore, our analysis of a common credential dataset demonstrates the practical feasibility and cost-effectiveness of our solution, suggesting its widespread adoption. By addressing the limitations of both traditional and existing blockchain-based approaches, our system provides a robust, secure, and efficient solution for credential management in diverse sectors.
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