New papers!

IEEE CLOUD 2024

We are excited to announce that two papers have been accepted at the 2024 IEEE CLOUD in Shenzhen, China. ๐ŸŽ‰ย 

Big congratulations to our team for their outstanding work on
"Predictive Placement of Geo-distributed Blockchain Nodes for Performance Guarantee" ๐ŸŒ and
"Harmonia: Accurate Federated Learning with All-Inclusive Dataset." ๐Ÿค–

Blockchain-as-a-service (BaaS) in cloud datacenters is gaining widespread attention due to its high performance and privacy. However, existing BaaS solutions lack a method for deciding the proper placement of blockchain nodes across virtual machines in worldwide datacenters to achieve desired performance. Our motivating experiments show that transaction processing performance (TPS) varies ~31.6% depending on the placements. To provide an automatic placement solution for BaaS, we propose Cyan that predicts the TPS for blockchain node placements. Our evaluations on Google Cloud Platform demonstrate that Cyan improves the TPS guarantee ~2.39ร— compared to existing techniques.

Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by โˆผ57.7%. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by โˆผ1.7ร— over existing schemes.

ย  * The authors contributed equally to each study.