Impact of network delays on Hyperledger Fabric
March 21, 2019 ยท Declared Dead ยท ๐ Conference on Computer Communications Workshops
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Thanh Son Lam Nguyen, Guillaume Jourjon, Maria Potop-Butucaru, Kim Thai
arXiv ID
1903.08856
Category
cs.PF: Performance
Cross-listed
cs.CR,
cs.NI
Citations
55
Venue
Conference on Computer Communications Workshops
Last Checked
1 month ago
Abstract
Blockchain has become one of the most attractive technologies for applications, with a large range of deployments such as production, economy, or banking. Under the hood, Blockchain technology is a type of distributed database that supports untrusted parties. In this paper we focus Hyperledger Fabric, the first blockchain in the market tailored for a private environment, allowing businesses to create a permissioned network. Hyperledger Fabric implements a PBFT consensus in order to maintain a non forking blockchain at the application level. We deployed this framework over an area network between France and Germany in order to evaluate its performance when potentially large network delays are observed. Overall we found that when network delay increases significantly (i.e. up to 3.5 seconds at network layer between two clouds), we observed that the blocks added to our blockchain had up to 134 seconds offset after 100 th block from one cloud to another. Thus by delaying block propagation, we demonstrated that Hyperledger Fabric does not provide sufficient consistency guaranties to be deployed in critical environments. Our work, is the fist to evidence the negative impact of network delays on a PBFT-based blockchain.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Performance
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
A General Formula for the Stationary Distribution of the Age of Information and Its Application to Single-Server Queues
R.I.P.
๐ป
Ghosted
AI Benchmark: All About Deep Learning on Smartphones in 2019
R.I.P.
๐ป
Ghosted
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
R.I.P.
๐ป
Ghosted
Online normalizer calculation for softmax
R.I.P.
๐ป
Ghosted
CLTune: A Generic Auto-Tuner for OpenCL Kernels
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted