Tetris: Proactive Container Scheduling for Long-Term Load Balancing in Shared Clusters
Fei Xu, Xiyue Shen, Shuo-Hao Lin, Li Chen, Zhi Zhou, Fen Xiao, Fangming Liu · IEEE Transactions on Services Computing · 2024
Long-running containerized workloads (e.g., machine learning), which typically showtime-varyingpatterns, are increasingly prevailing in shared production clusters. To improve workload performance, current schedulers mainly focus on optimizingshort-termbenefits of cluster load balancing orinitial container placementon servers. However, this would inevitably bring manyinvalid migrations(i.e., containers are migrated back and forth among servers over a short time window), leading to significant service level objective (SLO) violations. This paper introducesTetris, amodel predictive control(MPC)-based container scheduling strategy to proactively migrate long-running workloads for cluster load balancing. Specifically, we first build a discrete-time dynamic model forlong-termoptimization of container scheduling. To solve such an optimization problem,Tetristhen employs two main components: (1) a container resource predictor, which leverages time-series analysis approaches to accurately predict the container resource consumption; (2) an MPC-based container scheduler that jointly optimizes the cluster load balancing and container migration costover a certain sliding time window. We implement and open source a prototype ofTetrisbased on K8s. Extensive prototype experiments and trace-driven simulations demonstrate thatTetriscan improve the cluster load balancing degree by up to 77.8% without incurring any SLO violations, compared to the state-of-the-art container scheduling strategies.