Identification of Resource Utilisation Patterns in Data Centers Using Tensor Decomposition

The worldwide workload of the public cloud infrastructure has been increasing steadily for the past many years and the latest statistics indicate that it will remain so for the coming years. At the same time, the energy consumption of the cloud infrastructure is considerably high. In this respect, the efficient utilisation of computing resources is of profound importance. In contemporary data centres tens of thousands of virtual machines execute simultaneously. Considering the number and heterogeneity of the virtual machines, balancing the demand for and the supply of resources is one of the challenges facing Cloud and Edge Computing. Often, infrastructure providers over-provision resources to ensure that service level agreements are respected. This, however, is not sustainable and its long-term impact on the environment cannot be overlooked. In this paper, we propose the use of tensor decomposition to analyse the resource utilisation metrics of a large number of hosted virtual machines and to identify complementary and contentious features which can be vital for efficient resource utilisation.