Scalable and automatic virtual machines placement based on behavioral similarities

The success of the cloud computing paradigm is leading to a significant growth in size and complexity of cloud data centers. This growth exacerbates the scalability issues of the Virtual Machines (VMs) placement problem, that assigns VMs to the physical nodes of the infrastructure. This task can be modeled as a multi-dimensional bin-packing problem, with the goal to minimize the number of physical servers (for economic and environmental reasons), while ensuring that each VM can access the resources required in the next future. Unfortunately, the naïve bin packing problem applied to a real data center is not solvable in a reasonable time because the high number of VMs and of physical nodes makes the problem computationally unmanageable. Existing solutions improve scalability at the expense of solution quality, resulting in higher costs and heavier environmental footprint. The Class-Based placement technique (CBP) is a novel approach that exploits existing solutions to automatically group VMs showing similar behaviour. The Class-Based technique solves a placement problem that considers only some representative VMs for each class, and that can be replicated as a building block to solve the global VMs placement problem. Using real traces, we analyse our proposal performance, comparing different alternatives to automatically determine the number of building blocks. Furthermore, we compare our proposal against the existing alternatives and evaluate the results for different workload compositions. We demonstrate that the CBP proposal outperforms existing solutions in terms of scalability and VM placement quality