article_krebs.bib

@article{KrMoKo2013-SciCo-MetricsAndTechniquesForPerformanceIsolation,
  author = {Rouven Krebs and Christof Momm and Samuel Kounev},
  journal = {Elsevier Science of Computer Programming Journal (SciCo)},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrMoKo2013-SciCo-MetricsAndTechniquesForPerformanceIsolation.pdf},
  publisher = {Elsevier B.V.},
  title = {{Metrics and Techniques for Quantifying Performance Isolation in Cloud Environments}},
  volume = {90, Part B},
  number = {},
  pages = {116 - 134},
  year = {2014},
  note = {Special Issue on Component-Based Software Engineering and Software Architecture},
  issn = {0167-6423},
  doi = {http://dx.doi.org/10.1016/j.scico.2013.08.003},
  url = {http://www.sciencedirect.com/science/article/pii/S0167642313001962},
  abstract = {The cloud computing paradigm enables the provision of cost efficient IT-services by leveraging economies of scale and sharing data center resources efficiently among multiple independent applications and customers. However, the sharing of resources leads to possible interference between users and performance problems are one of the major obstacles for potential cloud customers. Consequently, it is one of the primary goals of cloud service providers to have different customers and their hosted applications isolated as much as possible in terms of the performance they observe. To make different offerings, comparable with regards to their performance isolation capabilities, a representative metric is needed to quantify the level of performance isolation in cloud environments. Such a metric should allow to measure externally by running benchmarks from the outside treating the cloud as a black box. In this article, we propose three different types of novel metrics for quantifying the performance isolation of cloud-based systems. We consider four new approaches to achieve performance isolation in Software-as-a-Service (SaaS) offerings and evaluate them based on the proposed metrics as part of a simulation-based case study. To demonstrate the effectiveness and practical applicability of the proposed metrics for quantifying the performance isolation in various scenarios, we present a second case study evaluating performance isolation of the hypervisor Xen.}
}