TY - GEN
T1 - Cost-effective data feeds to blockchains via workload-adaptive data replication
AU - Li, Kai
AU - Yuan, Zhehu
AU - Tang, Yuzhe
AU - Xu, Cheng
AU - Chen, Jiaqi
AU - Xu, Jianliang
N1 - Funding Information:
The authors appreciate anonymous reviewers and the shepherd of this paper, Alysson Bessani. This work was supported by the National Science Foundation under Grant CNS1815814. Jianliang Xu and Cheng Xu were partially supported by Hong Kong RGC Projects 12200819 and 12201018.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications. Given the data-intensive nature of real-life feeds (e.g., high-frequency price updates) and the high cost of using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads infinancial applications, this work aims at designing a dynamic, workload-aware approach for Gas cost optimization. This design space is understudied in existing blockchain research which has so far focused on static data placement. This work presents GRuB, a cost-effective data feed that dynamically replicates data between the blockchain and offchain cloud storage. GRuB monitors the current workload and makes data-replication decisions in a workload-adaptive fashion. Online algorithms are proposed to bound the worst-case cost in Gas. GRuB's decision-making components run on the untrusted cloud off-chain for lower Gas, and employs a security protocol to authenticate the data transferred between the blockchain and cloud. We built a GRuB prototype on Ethereum and supported reafinancial applications. Using the workloads reconstructed from Ethereum transaction history, we evaluate GRuB's cost and show a Gas saving by 10% ~ 74%, in comparison with the static baselines.
AB - Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications. Given the data-intensive nature of real-life feeds (e.g., high-frequency price updates) and the high cost of using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads infinancial applications, this work aims at designing a dynamic, workload-aware approach for Gas cost optimization. This design space is understudied in existing blockchain research which has so far focused on static data placement. This work presents GRuB, a cost-effective data feed that dynamically replicates data between the blockchain and offchain cloud storage. GRuB monitors the current workload and makes data-replication decisions in a workload-adaptive fashion. Online algorithms are proposed to bound the worst-case cost in Gas. GRuB's decision-making components run on the untrusted cloud off-chain for lower Gas, and employs a security protocol to authenticate the data transferred between the blockchain and cloud. We built a GRuB prototype on Ethereum and supported reafinancial applications. Using the workloads reconstructed from Ethereum transaction history, we evaluate GRuB's cost and show a Gas saving by 10% ~ 74%, in comparison with the static baselines.
KW - Authenticated data structures
KW - Blockchains
KW - Data feeds
KW - Data replication
KW - DeFi
KW - Workload awareness
UR - http://www.scopus.com/inward/record.url?scp=85098492557&partnerID=8YFLogxK
U2 - 10.1145/3423211.3425696
DO - 10.1145/3423211.3425696
M3 - Conference proceeding
AN - SCOPUS:85098492557
T3 - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
SP - 371
EP - 385
BT - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
PB - Association for Computing Machinery (ACM)
T2 - 21st International Middleware Conference, Middleware 2020
Y2 - 7 December 2020 through 11 December 2020
ER -