Service clustering for autonomic clouds using random forest

Rafael Brundo Uriarte, Sotirios Tsaftaris, Francesco Tiezzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Managing and optimising cloud services is one of the main challenges faced by industry and academia. A possible solution is resorting to self-management, as fostered by autonomic computing. However, the abstraction layer provided by cloud computing obfuscates several details of the provided services, which, in turn, hinders the effectiveness of autonomic managers. Data-driven approaches, particularly those relying on service clustering based on machine learning techniques, can assist the autonomic management and support decisions concerning, for example, the scheduling and deployment of services. One aspect that complicates this approach is that the information provided by the monitoring contains both continuous (e.g. CPU load) and categorical (e.g. VM instance type) data. Current approaches treat this problem in a heuristic fashion. This paper, instead, proposes an approach, which uses all kinds of data and learns in a data-driven fashion the similarities and resource usage patterns among the services. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. Using datasets from real-world clouds, we demonstrate the superiority of our solution with respect to others and validate the accuracy of the on-line prediction. Moreover, to show the applicability of our approach, we devise a service scheduler that uses the notion of similarity among services and evaluate it in a cloud test-bed.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages515-524
Number of pages10
ISBN (Print)9781479980062
DOIs
Publication statusPublished - 7 Jul 2015
Event15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 - Shenzhen, China
Duration: 4 May 20157 May 2015

Conference

Conference15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Country/TerritoryChina
CityShenzhen
Period4/05/157/05/15

Keywords

  • Autonomic computing
  • Cloud computing
  • Machine learning
  • Random forest
  • Similarity learning

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