Moohyun Song

Hanyang University

Seoul, South Korea

I am a Ph.D. student in Artificial Intelligence at Hanyang University, where I am a member of the Distributed Data Processing Systems Lab (DDPS Lab). I received my B.S.E. in Computer Science from Kookmin University.

My research interests include Distributed Systems, Cloud Computing, and AI Platform.

recent publications

Ding-Dong Ditch: Peeking Into Spot Instance Availability

Kyumin Kim, Moohyun Song, Taeyoon Kim, and Kyungyong Lee

In 2026 IEEE 19th International Conference on Cloud Computing (CLOUD), 2026

To appear

Bib

@inproceedings{kim2026ddd,
  author = {Kim, Kyumin and Song, Moohyun and Kim, Taeyoon and Lee, Kyungyong},
  title = {Ding-Dong Ditch: Peeking Into Spot Instance Availability},
  year = {2026},
  booktitle = {2026 IEEE 19th International Conference on Cloud Computing (CLOUD)},
  series = {IEEE CLOUD 2026},
  note = {To appear},
  keywords = {international},
}

Orchestrating WASM-based MCP Tool Runtimes for AI Agents across Edge-Cloud Continuum

Moohyun Song, Hayoung Kim, Kyoohyun Lee, Jae Gi Son, and Kyungyong Lee

In Proceedings of the 26th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 2026

To appear

Bib

@inproceedings{song2026edgeagent,
  author = {Song, Moohyun and Kim, Hayoung and Lee, Kyoohyun and Son, Jae Gi and Lee, Kyungyong},
  title = {Orchestrating WASM-based MCP Tool Runtimes for AI Agents across Edge-Cloud Continuum},
  year = {2026},
  booktitle = {Proceedings of the 26th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing},
  series = {CCGrid '26},
  note = {To appear},
  keywords = {international},
}

HybridServe: Adaptive WebAssembly-Container Runtime Selection for Edge Serverless Computing

Seokhyeon Kang^*, Moohyun Song^*, Taeyoon Kim^*, Soohyuk Lee, Jaeseob Han, Hyeokman Kim, and Kyungyong Lee

In Proceedings of the 11th International Workshop on Serverless Computing, 2025

Abs DOI Bib

Edge serverless environments with limited CPU, memory, and heterogeneous hardware demand efficient execution under resource constraints. A key challenge in serverless computing is cold start, where a function instance is created and initialized from scratch instead of reusing an already initialized (warm) instance, which is particularly severe in edge environments. Container-based platforms exhibit cold start latencies exceeding hundreds of milliseconds with tens of megabyte images, while WebAssembly (WASM) provides less than ten millisecond initialization and compact binaries, but slower execution. We comprehensively analyze multiple WASM runtimes (Wasmtime, Wasmer, WasmEdge) and their compiler backends, revealing that WASM reduces cold start latency by up to 88.1% and image size by up to 99.17%, while exhibiting 36.15% slower execution and 9.26% higher power consumption for compute-intensive workloads. Based on these complementary characteristics, we propose HybridServe, a dynamic runtime selection framework that leverages WASM during container cold starts while preparing containers in the background. Evaluation on Azure Function Trace demonstrates 43.11% average response time reduction versus WASM-only and 91.9% cold start latency reduction versus container-only deployments, effectively mitigating the performance-isolation trade-off in edge serverless computing.
@inproceedings{kang2025hybridserve, author = {Kang, Seokhyeon and Song, Moohyun and Kim, Taeyoon and Lee, Soohyuk and Han, Jaeseob and Kim, Hyeokman and Lee, Kyungyong}, title = {HybridServe: Adaptive WebAssembly-Container Runtime Selection for Edge Serverless Computing}, year = {2025}, isbn = {9798400723025}, publisher = {Association for Computing Machinery}, url = {https://doi.org/10.1145/3774899.3775011}, doi = {10.1145/3774899.3775011}, booktitle = {Proceedings of the 11th International Workshop on Serverless Computing}, pages = {1--6}, numpages = {6}, series = {WoSC11 '25}, keywords = {international}, }