IEEE Access (2020-01-01)

Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

  • Tariq Daradkeh,
  • Anjali Agarwal,
  • Marzia Zaman,
  • Nishith Goel

DOI
https://doi.org/10.1109/ACCESS.2020.3042716
Journal volume & issue
Vol. 8
pp. 219430 – 219446

Abstract

Read online

Cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand. This requires fast, and accurate resource scaling methods at minimum cost (e.g. pay as you go) that match with workload demands. Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes. These parameters are not labeled for cloud management system while data center logs are being captured. Building an advance elastic model is a critical task, which defines multiple classes under these two categories i.e. for workload and for provisioning. A dynamic method is therefore required to define (during configuration time window) the workload classes and resource provisioning classes. Unsupervised learning model such as K-Means has many challenges such as time complexity, selection of optimum number of clusters (representing the classes), and determining centroid values of the clusters. All clustering methods depend on minimizing mean square error between center of population in same class member. These methods are often enhanced using guidelines to find out the centroids, but they suffer from K-Means limitations. For the application of clustering cloud log traces, most of the reported work use K-Means clustering to label workload types. However, there is no work reported that label data center scaling classes. In this work, a novel method is proposed to analyze the characteristics of both workloads and datacenter configurations using clustering method, and is based on random variable model transformation (kernel density estimator) guide. This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters. In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods.

Keywords