Energy aware virtual network embedding with dynamic demands: Online and offline

In Network as a Service model in cloud computing, how to efficiently embed virtual networks with both node and link demands into a shared physical network, namely virtual network embedding, has attracted significant attention. Most of prior studies on this problem have the following two limitations: (i) they assumed that the virtual network demands are constants, which does not hold in real-world network since such demands may vary a lot over time; (ii) their primary goal was to achieve more revenues for the physical network, with no consideration of the energy cost, which has become a more and more critical issue. In this paper, we bridge the gaps and study the energy aware virtual network embedding problem with dynamic demands. Specifically, we first model the dynamics of virtual network demands as a combination of a Gaussian distribution and a daily diurnal pattern. We then design two efficient heuristic algorithms by leveraging the dynamic characteristic of virtual network demands to minimize the energy consumption while keeping a high revenue for the physical network. One algorithm processes the virtual network requests one by one while the other one processes them group by group. We implemented these two algorithms in C++ and performed side-by-side comparisons with the prior algorithm. Extensive simulations show that our algorithms significantly reduce the energy cost by up to 25% over the state-of-the-art algorithm, while maintaining near the same revenue.