Efficient Autoscaling of Cloud Applications using MPC: A Complementarity Constraints Formulation

Abstract

Cloud applications based on microservices architecture require autoscaling mechanisms that can react to time-varying workloads while accounting for provisioning delays and discrete resource capacities. Model predictive control (MPC) approaches based on queueing-network models provide a principled way to handle these transient effects, but incorporating saturation phenomena, discrete capacity levels, and actuation logic typically leads to mixed logical dynamical (MLD) models and mixed-integer optimization problems that can be challenging to solve in real-time. This paper proposes an MPC formulation for autoscaling of cloud applications modeled as fluid queueing networks, where the hybrid dynamics induced by service saturation are represented using stage-wise complementarity constraints. By exploiting the piecewise-affine structure of the queueing dynamics, the resulting prediction model admits a linear time-invariant state update with a stable transition matrix, while nonsmooth effects are captured locally through complementarity constraints, thus enabling a faster solution than standard MLD-based formulations. The proposed approach is evaluated in simulation on autoscaling scenarios of increasing difficulty.