Towards an Autoscaling Service for Real-Time Online Interactive Applications on Clouds

We develop a novel autoscaling service (autoscaler) to provide elasticity for Real-Time Online Interactive Applications (ROIA) running on clouds for thousands of concurrent users. High-performance ROIA include real-time 3D product configurators, multiplayer online gaming, digital twins for the industry 4.0 market, and e-learning. Using our autos caler for ROIA on clouds facilitates meeting high demands on Quality of Experience (QoE) and the economic utilization of cloud resources. Compared to existing autoscaling solutions (e.g., in Kubernetes), our autoscaler is based not on the classical metrics (CPU/GPU load, memory usage, etc.), but rather on the session slots which limit the number of concurrent sessions for a service instance. We design a novel auto scaling algorithm using linear regression of the session slots usage, and we mathematically analyze and experimentally evaluate autoscaler's dynamic reaction to changing workload while avoiding overswinging (creating more service instances than needed). We also report our preliminary experimental results.