Publications from conferences, workshops, and journals are listed below. Please also see the NDN Technical Reports and Technical Presentations.
2022
Patil, Varun; Desai, Hemil; Zhang, Lixia
Kua: a distributed object store over named data networking Proceedings Article
In: Proceedings of the 9th ACM Conference on Information-Centric Networking, pp. 56–66, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 978-1-4503-9257-0.
Abstract | Links | BibTeX | Tags: Applications, distributed storage, object store
@inproceedings{patil_kua_2022,
title = {Kua: a distributed object store over named data networking},
author = {Varun Patil and Hemil Desai and Lixia Zhang},
url = {https://dl.acm.org/doi/10.1145/3517212.3558083},
doi = {10.1145/3517212.3558083},
isbn = {978-1-4503-9257-0},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
booktitle = {Proceedings of the 9th ACM Conference on Information-Centric Networking},
pages = {56–66},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {ICN '22},
abstract = {Applications such as machine learning training systems or log collection generate and consume large amounts of data. Object storage systems provide a simple abstraction to store and access such large datasets. These datasets are typically larger than the capacities of individual storage servers, and require fault tolerance through replication. In this paper, we present Kua, a distributed object storage system built over Named Data Networking (NDN). The data-centric nature of NDN helps Kua maintain a simple design while catering to requirements of storing large objects, providing fault tolerance, low latency and strong consistency guarantees, along with data-centric security. Our prototype Kua implementation provides easy-to-use primitives to let applications store and access data securely, and our initial evaluation suggests that Kua can leverage NDN's capabilities of multicast data delivery and in-network caching to achieve higher efficiency than existing object storage systems.},
keywords = {Applications, distributed storage, object store},
pubstate = {published},
tppubtype = {inproceedings}
}
Applications such as machine learning training systems or log collection generate and consume large amounts of data. Object storage systems provide a simple abstraction to store and access such large datasets. These datasets are typically larger than the capacities of individual storage servers, and require fault tolerance through replication. In this paper, we present Kua, a distributed object storage system built over Named Data Networking (NDN). The data-centric nature of NDN helps Kua maintain a simple design while catering to requirements of storing large objects, providing fault tolerance, low latency and strong consistency guarantees, along with data-centric security. Our prototype Kua implementation provides easy-to-use primitives to let applications store and access data securely, and our initial evaluation suggests that Kua can leverage NDN's capabilities of multicast data delivery and in-network caching to achieve higher efficiency than existing object storage systems.