Scaling of Cloud Applications Using Machine Learning - VMware Technical Journal

Today's Internet applications are required to be highly scalable and available in the face of rapidly changing, unpredictable workloads. Multi-tier architecture is commonly used to build Internet applications, with different tiers providing load balancing, application logic, and persistence. The advent of cloud computing has given rise to rapid horizontal scaling of applications hosted in virtual machines (VMs) in each of the tiers. Currently, this scaling is done by monitoring system-level metrics (e.g., CPU utilization) and determining whether to scale out or in based on a threshold. These threshold-based algorithms, however, do not capture the complex interaction among multiple tiers, and determining the right set of thresholds for multiple resources to achieve a particular service level objective (SLO) is difficult. In this paper, we present vScale, a horizontal scaling system that can automatically scale the number of VMs in a tier to meet end-to-end application SLOs.