Lee, Chao YuChao YuLeeHong, Ding YongDing YongHongPANGFENG LIUWu, Jan JanJan JanWu2024-04-302024-04-302023-01-01979835033071715219097https://scholars.lib.ntu.edu.tw/handle/123456789/642188In recent years, container technology has gained significant attention in the software industry, with many businesses opting for its elasticity, cost-effectiveness, and ease of implementation. The most common way to ensure a seamless user experience is to keep a substantial number of containers active throughout the day, which causes resource over-provision. Conversely, closing the container right after handling the requests can reduce memory consumption but generate a cold start whenever the request arrives. Cold start occurrence and resource usage is a trade-off and presents a significant challenge on the container platform.To address this challenge, we observe that serving consecutive requests with the same container can notably decrease the number of cold starts. We propose TAC, a Temporal Adjacency Function Clustering algorithm, to meet the challenge. TAC selects the functions with time adjacency requests into a cluster from the historical data. TAC packs functions serving time adjacency requests into a cluster to reduce cold starts and enable efficient resource utilization. The experiment result shows that TAC reduces 8% cold start occurrences and 53% memory usage with the real-world traces compared to the state-of-the-art methods, e.g., Defuse [1] and Hybrid histogram policy [2].cold-start | container platform | Function-as-a-Service | serverlessconference paper10.1109/ICPADS60453.2023.003612-s2.0-85190235607https://api.elsevier.com/content/abstract/scopus_id/85190235607