Control-Based Load-Balancing Techniques: Analysis and Performance Evaluation via a Randomized Optimization Approach
2016 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 52, 24-34 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Elsevier , 2016. Vol. 52, 24-34 p.
Load-balancing, Randomized optimization, Cloud control
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:mdh:diva-33787DOI: 10.1016/j.conengprac.2016.03.020OAI: oai:DiVA.org:mdh-33787DiVA: diva2:1048548