Control-Based Load-Balancing Techniques: Analysis and Performance Evaluation via a Randomized Optimization Approach

Research output: Contribution to journalArticle


Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas - copies of the application with the same functionality - for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer.

Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing.

To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load-balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF) - believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability.


External organisations
  • Umeå University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Engineering and Technology


  • load-balancing, randomized optimization, cloud control
Original languageEnglish
Pages (from-to)24-34
Number of pages11
JournalControl Engineering Practice
Issue numberJuly
Early online date2016 Apr
Publication statusPublished - 2016 Jul
Publication categoryResearch

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