New papers!

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

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

• Junseok Lee*, Yeonho Yoo*, Chuck Yoo, Gyeongsik Yang
  "Predictive Placement of Geo-distributed Blockchain Nodes for Performance Guarantee" 🌐
  2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

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 ~2.39× compared to existing techniques.

• Wonmi Choi*, Juyoung Ahn*, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang
  "Harmonia: Accurate Federated Learning with All-Inclusive Dataset" 🤖
  2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

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 ∼57.7%. 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 ∼1.7× over existing schemes.

  * The authors contributed equally to each study.