Cheetah: A fast unsupervised learning technique to provision next generation network services

Recently, attributed graphs have been extensively employed in modeling, studying and analyzing complex interactions in real world systems. A myriad of techniques have been proposed to partition these graphs into clusters that exhibit small entropy with respect to both compositional attributes and the structural properties of the graph. In cloud network infrastructures, they play an important role to understand end users, compute nodes and their interactions. One of the main challenges in today's large scale cloud infrastructures is to categorize these compute nodes into clusters that share similar attributes. Existing unsupervised machine learning techniques such as k-Means and DBSCAN, are inadequate to partition large scale computer network infrastructures due to their non suitability for such contexts and their algorithmic complexities that prevent them from being scalable to such sizes in a reasonable time. In this paper, we first formulate the problem of partitioning attributed graphs in the context of cloud infrastructures as a Quadratic Assignment Problem to solve small to medium scale instances and show its NP-Hardness. We then propose Cheetah a fast and scalable multi-objective topology-aware unsupervised machine learning technique that is tailored to effectively partition large scale cloud network infrastructures. Yet, in terms of complexity, Cheetah is linear as it leverages Breadth First Search algorithm. Experimental results demonstrate its ability to quickly construct good-quality clusters (≈ 1.63 seconds) given 1000 nodes compared to K-Means (≈ 2.78 seconds) and DBSCAN (≈ 24.76 seconds), respectively, and reveal its suitability for large scale infrastructures making it an appealing solution to be integrated into orchestration systems.