Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost
This addresses capacity provisioning in data centers or cloud systems, but appears incremental as it extends known online optimization frameworks to non-convex settings with memory.
The paper tackles the problem of minimizing job flow time by dynamically adjusting active servers while accounting for switching costs and memory-dependent non-convex objectives, deriving competitive algorithms for both worst-case and stochastic inputs.
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time. Each job can be processed by at most one fixed speed server at any time. Compared to the usual online convex optimization (OCO) problem with switching cost, the objective function considered is non-convex and more importantly, at each time, it depends on all past decisions and not just the present one. Both worst-case and stochastic inputs are considered; for both cases, competitive algorithms are derived.