Online Control of Cloud and Edge Resources Using Inaccurate Predictions

We study cloud resource control in the global-local distributed cloud infrastructure. We firstly model and formulate the problem while capturing the multiple challenges such as the inter-dependency between resources and the uncertainty in the inputs. We then propose a novel online algorithm which, via the regularization technique, decouples the original problem into a series of subproblems for individual time slots and solves both the subproblems and the original problem over every prediction time window to jointly make resource allocation decisions. Compared against the offline optimum with accurate inputs, our approach maintains a provable parameterized worst-case performance gap with only inaccurate inputs under certain conditions. Finally, we conduct evaluations with large-scale, real-world data traces and show that our solution outperforms existing methods and works efficiently with near-optimal cost in practice.