Journal
PROCEEDINGS OF THE 2020 21ST INTERNATIONAL MIDDLEWARE CONFERENCE (MIDDLEWARE '20)
Volume -, Issue -, Pages 371-385Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3423211.3425696
Keywords
Blockchains; data feeds; authenticated data structures; data replication; workload awareness; DeFi
Funding
- National Science Foundation [CNS1815814]
- Hong Kong RGC Projects [12200819, 12201018]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available