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, Osaka, Japan, 2022, ISBN: 9781450392570.
Abstract | Links | BibTeX | Tags: Applications, Data Management, distributed storage
@inproceedings{patil2022kua:,
title = {Kua: A Distributed Object Store over Named Data Networking},
author = {Varun Patil and Hemil Desai and Lixia Zhang},
url = {https://doi.org/10.1145/3517212.3558083},
doi = {10.1145/3517212.3558083},
isbn = {9781450392570},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 9th ACM Conference on Information-Centric Networking},
pages = {56–66},
publisher = {Association for Computing Machinery},
address = {Osaka, Japan},
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, Data Management, distributed storage},
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.
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.
Presley, Justin; Wang, Xi; Brandel, Tym; Ai, Xusheng; Podder, Proyash; Yu, Tianyuan; Patil, Varun; Zhang, Lixia; Afanasyev, Alex; Feltus, F. Alex; Shannigrahi, Susmit
Hydra – A Federated Data Repository over NDN Miscellaneous
2022.
Abstract | Links | BibTeX | Tags: Applications, Data Management, distributed storage
@misc{presley_hydra_2022,
title = {Hydra – A Federated Data Repository over NDN},
author = {Justin Presley and Xi Wang and Tym Brandel and Xusheng Ai and Proyash Podder and Tianyuan Yu and Varun Patil and Lixia Zhang and Alex Afanasyev and F. Alex Feltus and Susmit Shannigrahi},
url = {https://arxiv.org/abs/2211.00919v1},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
journal = {arXiv.org},
abstract = {Today's big data science communities manage their data publication and replication at the application layer. These communities utilize myriad mechanisms to publish, discover, and retrieve datasets - the result is an ecosystem of either centralized, or otherwise a collection of ad-hoc data repositories. Publishing datasets to centralized repositories can be process-intensive, and those repositories do not accept all datasets. The ad-hoc repositories are difficult to find and utilize due to differences in data names, metadata standards, and access methods. To address the problem of scientific data publication and storage, we have designed Hydra, a secure, distributed, and decentralized data repository made of a loose federation of storage servers (nodes) provided by user communities. Hydra runs over Named Data Networking (NDN) and utilizes the State Vector Sync (SVS) protocol that lets individual nodes maintain a "global view" of the system. Hydra provides a scalable and resilient data retrieval service, with data distribution scalability achieved via NDN's built-in data anycast and in-network caching and resiliency against individual server failures through automated failure detection and maintaining a specific degree of replication. Hydra utilizes "Favor", a locally calculated numerical value to decide which nodes will replicate a file. Finally, Hydra utilizes data-centric security for data publication and node authentication. Hydra uses a Network Operation Center (NOC) to bootstrap trust in Hydra nodes and data publishers. The NOC distributes user and node certificates and performs the proof-of-possession challenges. This technical report serves as the reference for Hydra. It outlines the design decisions, the rationale behind them, the functional modules, and the protocol specifications.},
keywords = {Applications, Data Management, distributed storage},
pubstate = {published},
tppubtype = {misc}
}
Today's big data science communities manage their data publication and replication at the application layer. These communities utilize myriad mechanisms to publish, discover, and retrieve datasets - the result is an ecosystem of either centralized, or otherwise a collection of ad-hoc data repositories. Publishing datasets to centralized repositories can be process-intensive, and those repositories do not accept all datasets. The ad-hoc repositories are difficult to find and utilize due to differences in data names, metadata standards, and access methods. To address the problem of scientific data publication and storage, we have designed Hydra, a secure, distributed, and decentralized data repository made of a loose federation of storage servers (nodes) provided by user communities. Hydra runs over Named Data Networking (NDN) and utilizes the State Vector Sync (SVS) protocol that lets individual nodes maintain a "global view" of the system. Hydra provides a scalable and resilient data retrieval service, with data distribution scalability achieved via NDN's built-in data anycast and in-network caching and resiliency against individual server failures through automated failure detection and maintaining a specific degree of replication. Hydra utilizes "Favor", a locally calculated numerical value to decide which nodes will replicate a file. Finally, Hydra utilizes data-centric security for data publication and node authentication. Hydra uses a Network Operation Center (NOC) to bootstrap trust in Hydra nodes and data publishers. The NOC distributes user and node certificates and performs the proof-of-possession challenges. This technical report serves as the reference for Hydra. It outlines the design decisions, the rationale behind them, the functional modules, and the protocol specifications.