Browsing by Author "Paidiparthy, Manoj Prabhakar"

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    EdgeFn: A Lightweight Customizable Data Store for Serverless Edge Computing
    Paidiparthy, Manoj Prabhakar (Virginia Tech, 2023-06-01)
    Serverless Edge Computing is an extension of the serverless computing paradigm that enables the deployment and execution of modular software functions on resource-constrained edge devices. However, it poses several challenges due to the edge network's dynamic nature and serverless applications' latency constraints. In this work, we introduce EdgeFn, a lightweight distributed data store for the serverless edge computing system. While serverless comput- ing platforms simplify the development and automated management of software functions, running serverless applications reliably on resource-constrained edge devices poses multiple challenges. These challenges include a lack of flexibility, minimum control over management policies, high data shipping, and cold start latencies. EdgeFn addresses these challenges by providing distributed data storage for serverless applications and allows users to define custom policies that affect the life cycle of serverless functions and their objects. First, we study the challenges of existing serverless systems to adapt to the edge environment. Sec- ond, we propose a distributed data store on top of a Distributed Hash Table (DHT) based Peer-to-Peer (P2P) Overlay, which achieves data locality by co-locating the function and its data. Third, we implement programmable callbacks for storage operations which users can leverage to define custom policies for their applications. We also define some use cases that can be built using the callbacks. Finally, we evaluate EdgeFn scalability and performance using industry-generated trace workload and real-world edge applications.
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    Team 2 for End Users
    Paidiparthy, Manoj Prabhakar; Ramanujan, Ramaraja; Teegalapally, Akshita; Muralikrishnan, Madhuvanti; Balar, Romil Khimraj; Juvekar, Shaunak; Murali, Vivek (Virginia Tech, 2023-01-11)
    A huge collection of Electronic Theses and Dissertations (ETDs) has valuable information. However, accessing the information from these documents has proven to be challenging as the process is mostly manual. We propose to build a unique Information Retrieval System that will support searching, ranking, browsing, and recommendations for a large collection of ETDs. The system indexes the digital objects related to the ETD, like documents, chapters, etc. The user can then query the indexed objects through a carefully designed web interface. The web interface provides users with utilities to sort, filter, and query specific fields. We have incorporated machine learning models to support semantic search. To enhance user engagement, we provide the user with a list of recommended documents based on the user's actions and topics of interest. A total of 57,130 documents and 21,537 chapters were indexed. The system was tested by the Fall 2022 CS 5604 class, which had 28 members, and was found to fulfill most of the goals set out at the beginning of the semester.
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    Toward an Edge-Friendly Distributed Object Store for Serverless Functions
    Chen, Xin; Paidiparthy, Manoj Prabhakar; Hu, Liting (ACM, 2024-09-04)
    Serverless computing is changing the way in which we structure and deploy computations in Internet-scale edge systems. This paper presents Capybara, a new scalable and programmable distributed object store for storing and sharing serverless function data objects (state) on edge infrastructures. The key innovations here are (1) achieving scalability and avoiding the significant DRAM cost through a consistent DHT-based P2P architecture; and (2) providing a programmable handler abstraction to customize state management policies (e.g., data caching policies, container “keepalive” times, access control methods, and data replication policies). We implement Capybara prototype on the Pastry DHT, deploy it on 150 Amazon EC2 nodes, and evaluate it by building several use cases to conduct real-world experiments, demonstrating its significant gains in data locality, state management customization, and scalability compared to the state-of-the-art.