Paper Info
Title
Aca-Sds: Adaptive Crypto Acceleration For Secure Data Storage In Big Data
Abstract
In the era of big data, the demand for secure data storage is rapidly increasing. To accelerate the complex encryption computation, both specific instructions and hardware accelerators are adopted in a large number of scenarios. However, the hardware accelerators are not so effective especially for small volume data due to the induced invocation costs, while the AES-NI (Intel (R) advanced encryption standard new instructions) is not so energy efficiency for big data. To satisfy the diversity performance/energy requirements for intensive data encryptions, a collaborative solution is proposed in this paper. We proposed a feasible hardware-software co-design methodology based on the stack file system eCryptfs, with quick assist technology, which is named adaptive crypto acceleration for secure data storage (ACA-SDS). ACA-SDS is able to choose the optimal encryption solution dynamically according to file operation modes and request characters. Adjustable parameters, such as alpha, beta, and M are provided in our scheme to provide a better adaptability and tradeoff choices for encryption computation. Our evaluation shows that ACA-SDS can get 15%-25% performance improvement for big-data blocks compared with only software or hardware accelerations. Furthermore, our methodology provides a wide range of practical design concepts for the further research in this field.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2862425
IEEE ACCESS
Keywords
Field
DocType
Hardware-software co-design, eCryptfs, encryption, data-intensive, QAT
File system,Computer data storage,Efficient energy use,Computer science,Encryption,Software,Concurrent computing,Big data,Embedded system,Performance improvement,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Chunhua Xiao108.45
Pengda Li200.68
Lei Zhang33411.51
Weichen Liu441137.34
Neil W. Bergmann545470.23