Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

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 off-chain cloud storage. GRuB monitors the current workload and makes data-replication decisions in a workload-adaptive fashion. Online algorithms are proposed to bound the worstcase 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 realfinancial applications. Using the workloads reconstructed from Ethereum transaction history, we evaluate GRuB's cost and show a Gas saving by 10% similar to 74%, in comparison with the static baselines.