Performance Interference-Aware Vertical Elasticity for Cloud-Hosted Latency-Sensitive Applications

Elastic auto-scaling in cloud platforms has primarily used horizontal scaling by assigning application instances to distributed resources. Owing to rapid advances in hardware, cloud providers are now seeking vertical elasticity before attempting horizontal scaling to provide elastic auto-scaling for applications. Vertical elasticity solutions must, however, be cognizant of performance interference that stems from multi-tenant collocated applications since interference significantly impacts application quality-of-service (QoS) properties, such as latency. The problem becomes more pronounced for latency-sensitive applications that demand strict QoS properties. Further exacerbating the problem are variations in workloads, which make it hard to determine the right kinds of timely resource adaptations for latency-sensitive applications. To address these challenges and overcome limitations in existing offline approaches, we present an online, data-driven approach which utilizes Gaussian Processes-based machine learning techniques to build runtime predictive models of the performance of the system under different levels of interference. The predictive online models are then used in dynamically adapting to the workload variability by vertically auto-scaling co-located applications such that performance interference is minimized and QoS properties of latency-sensitive applications are met.