You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

PEAS: A Performance Evaluation Framework for Auto-Scaling Strategies in Cloud Applications

Fulltext:


Authors:

Alessandro Papadopoulos, Ahmed Ali-Eldin , Karl-Erik Årzén , Johan Tordsson , Erik Elmroth

Publication Type:

Journal article

Venue:

ACM Trans. Model. Perform. Eval. Comput. Syst.

Publisher:

ACM

DOI:

10.1145/2930659


Abstract

Numerous auto-scaling strategies have been proposed in the past few years for improving various Quality of Service (QoS) indicators of cloud applications, for example, response time and throughput, by adapting the amount of resources assigned to the application to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved through experiments under specific conditions and seldom includes extensive testing to account for uncertainties in the workloads and unexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in general conditions. In this article, we present a Performance Evaluation framework for Auto-Scaling (PEAS) strategies in the presence of uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenario theory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six different auto-scaling strategies have been selected from the literature for extensive test evaluation and compared using the proposed framework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’ servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics, highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of the algorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show that based on the evaluation criteria, a controller can be shown to be better than other controllers.

Bibtex

@article{Papadopoulos4479,
author = {Alessandro Papadopoulos and Ahmed Ali-Eldin and Karl-Erik {\AA}rz{\'e}n and Johan Tordsson and Erik Elmroth},
title = {PEAS: A Performance Evaluation Framework for Auto-Scaling Strategies in Cloud Applications},
volume = {3},
number = {2},
pages = {1--31},
month = {August},
year = {2016},
journal = {ACM Trans. Model. Perform. Eval. Comput. Syst.},
publisher = {ACM},
url = {http://www.es.mdu.se/publications/4479-}
}