@article{Huang2026, 
author = {Zhaowu Huang and Fang Dong and Wenlong Ruan and Xiaolin Guo and Jinghui Zhang},
title = {Adaptive Microservice Deployment for Data Partition-Based Applications in MEC},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {5},
pages = {2485-2503},
keywords = {Mobile Edge Computing (MEC), Directed Acyclic Graph (DAG), microservice deployment},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010255},
doi = {10.26599/TST.2024.9010255},
abstract = {Recently, Mobile Edge Computing (MEC) has used lightweight container-based microservices to provide resources for artificial intelligence applications, which will be decomposed into multiple dependent components, forming a Directed Acyclic Graph (DAG). In MEC, users will partition the input of the computation-intensive tasks into multiple sub-tasks for parallel execution acceleration. To satisfy concurrency, app vendors must deploy multiple container replicas for a microservice. Due to the limited capacity of edge servers, containers need to be deployed into geographically distributed and heterogeneous edge servers, resulting in significant inter-edge server traffic. To this end, we propose an adaptive scheme to guide microservice deployment for data partition-based applications in the MEC. We model the multi-replica microservice deployment problem as an integer programming problem to minimize operation costs. We propose a Deterministic Local Search-based Microservice Deployment algorithm (DLSMD), that chooses a superior neighborhood solution iteratively to solve it. We also formulate a more general problem considering both computing and communication time to minimize the total completion time and devise a Heuristic Microservice Deployment (HMD) algorithm to solve it. Extensive simulation results show that DLSMD and HMD outperform other benchmarks, achieving up to  4× and  3.12× speedups in terms of the inter-server traffic and total completion time reduction, respectively.}
}