A Model-Based System to Automate Cloud Resource Allocation and Optimization

  • Yu Sun
  • Jules White
  • Sean Eade
Conference paper

DOI: 10.1007/978-3-319-11653-2_2

Volume 8767 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Sun Y., White J., Eade S. (2014) A Model-Based System to Automate Cloud Resource Allocation and Optimization. In: Dingel J., Schulte W., Ramos I., Abrahão S., Insfran E. (eds) Model-Driven Engineering Languages and Systems. MODELS 2014. Lecture Notes in Computer Science, vol 8767. Springer, Cham

Abstract

Cloud computing offers a flexible approach to elastically allocate computing resources for web applications without significant upfront hardware acquisition costs. Although a diverse collection of cloud resources is available, choosing the most optimized and cost-effective set of cloud resources to meet the QoS requirements is not a straightforward task. Manual load testing, monitoring of resource utilization, followed by bottleneck analysis is time consuming and complex due to limitations of the abstractions of load testing tools, challenges characterizing resource utilization, significant manual test orchestration effort, and complexity of selecting resource configurations to test. This paper introduces a model-based approach to simplify, optimize, and automate cloud resource allocation decisions to meet QoS goals for web applications. Given a high-level application description and QoS requirements, the model-based approach automatically tests the application under a variety of load and resources to derive the most cost-effective resource configuration to meet the QoS goals.

Keywords

Cloud Computing Resource Allocation Resource Optimization Model-Based System Domain-Specific Language 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yu Sun
    • 1
  • Jules White
    • 1
  • Sean Eade
    • 2
  1. 1.Vanderbilt UniversityNashvilleUSA
  2. 2.Siemens IndustryWendellUSA