Continuous QoS-aware adaptation of Cloud-IoT application placements

Abstract

Cloud-Internet of Things computing paradigms call for novel and efficient method ologies to decide where to place application services in continuity with Continuous Integration/Continuous Deployment pipelines and infrastructure monitoring. In this article, we present Continuous Adaptation (CA), a new DevOps practice for (1) detect ing runtime changes in the application requirements or the nfrastructure that, due to their change in resource consumption or their effects on the Quality of Service (QoS), can affect the validity and dependability of the current application placement, and for (2) locally intervening on them by suggesting new placements that ensure all (func tional and non-functional) application requirements are met. We assess a prototype of CA, ConDADO, and analyze its performance over a motivating use case. Con DADO adapts the application placement to environmental changes through the use of continuous reasoning, reducing the size of the problem to be solved to optimize its performance. The evaluation shows that ConDADO is able to obtain nearly optimal QoS up to 4.2× faster than alternative techniques, also minimizing the cost of service migration.