Online Cloud Resource Provisioning under Cost Budget for QoS Maximization

Cloud computing is becoming one of the ubiquitous computing paradigms for enterprises and organizations in recent years. Due to the volatility of system states such as cloud resource price and workload demand, it is challenging to provision cloud resources efficiently. This paper studies online cloud resource provisioning problems under cost budget where no accurate or distributional future information is available. We develop an algorithmic framework and design online algorithms based on the framework. We prove the competitive ratio of the proposed algorithms. We further show the proposed algorithms have better performance than a prominent existing algorithm named CR-Pursuit. While prior works on the problem require the objective functions to be concave, the proposed algorithms work for non-convex and non-concave objective functions. We conduct real-world trace-driven simulations. Results highlight the proposed algorithms outperform baselines significantly over a wide range of settings.