LAUSR

Data center virtualization is a flexible and efficient way to enable multiple users to share the common resources of a physical data center (DC). For efficient sharing, virtual data center (VDC) embedding is a vital problem that should be carefully addressed. However, existing studies on VDC embedding mostly assume that the capacity of each VDC is fixed, but do not consider the time-varying feature of resource demands. Considering the fact that the resource demands of most enterprise IT services exhibit the time-varying feature, resource allocation based on the fixed capacity assumption would cause a great inefficiency. To overcome this inefficiency, we propose a new VDC consolidation scheme that takes into account the time-varying feature of resource demands when embedding VDCs. We first develop a resource demand prediction model for each VDC using the Long Short-Term Memory (LSTM) neural network, which is used to predict the real-time resource demands of VDCs at different future moments. Based on the predicted resource demands, we then embed VDCs whose peaks and valleys of resource demands stagger each other onto common physical servers and links, such that the required physical resources can be minimized under the condition that all the resource demands of different VDCs are satisfied at all the different moments. An integer linear programming (ILP) model and a resource demand correlation-based heuristic algorithm are also developed for the proposed scheme. Simulation results show that the proposed consolidation scheme can significantly improve resource utilization in a DC. It can save up to 25 percent of physical servers and 29 percent of physical links used for accommodating the same requests as compared to a scheme assigning resources based on the fixed capacity assumption.