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 - Publisher Copyright:
© 2020 Association for Computing Machinery.
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
UR - http://www.scopus.com/inward/citedby.url?scp=85098492557&partnerID=8YFLogxK
U2 - 10.1145/3423211.3425696
DO - 10.1145/3423211.3425696
M3 - Conference contribution
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, Inc
T2 - 21st International Middleware Conference, Middleware 2020
Y2 - 7 December 2020 through 11 December 2020
ER -