A Lightweight Dynamic Storage Algorithm With Adaptive Encoding for Energy Internet-Reference-Cited by-同舟云学术

A Lightweight Dynamic Storage Algorithm With Adaptive Encoding for Energy Internet

Published:2023-09 Issue:5 Volume:16 Page:3115-3128
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Deng Song¹^ORCID,Zhai Yujia²^ORCID,Wu Di³^ORCID,Yue Dong²^ORCID,Fu Xiong⁴^ORCID,He Yi⁵^ORCID

Affiliation:

1. Institute of Advanced Technology, Nanjing University of Posts & Telecommunications, Nanjing, China

2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

3. College of Computer and Information Science, Southwest University, Chongqing, China

4. School of Computer, Nanjing University of Posts & Telecommunications, Nanjing, China

5. Old Dominion University, Norfolk, VA, USA

Funder

National Natural Science Foundation of China

Postgraduate Research & Practice Innovation Program of Jiangsu Province

BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/4629386/10274114/10089192.pdf?arnumber=10089192

Reference38 articles.

1. Interactive analytical processing in big data systems

2. Scalable performance of the panasas parallel file system;welch;Proc USENIX Conf File Storage Technol,2008

3. A Secure Decentralized Erasure Code for Distributed Networked Storage

4. Pergamum: Replacing tape with energy efficient, reliable, disk-based archival storage;storer;Proc 6th USENIX Conf File Storage Technol,2008

5. Pegasus: Tolerating skewed workloads in distributed storage with In-Network coherence directories;li;Proc 14th USENIX Symp Operating Syst Des Implementation,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis;Entropy;2023-07-14