HybridScaler: Handling Bursting Workload for Multi-tier Web Applications in Cloud

Cloud elasticity allows users to dynamically allocate resources for their applications to adapt with the fluctuant demand. But allocating right amount of resources at right time to handle the bursting workload is still challenging. Most practical auto scaling approaches allocate resources in horizontal or vertical manner while the horizontal scaling usually causes considerable overhead and extra cost for short-term bursting workload. Vertical scaling is lightweight and timely but lacks scalability and capacity guarantee in public cloud. In order to find an accurate and cost-effective auto scaling method, we propose a hybrid auto scaling solution called HybridScaler which combines long-term predictive horizontal scaling and timely reactive vertical scaling properly. Specially, our method is based on a resource-pressure model which can provide suitable amount of resources matching the changing workload. We implement our prototype in an OpenStack private cloud. We evaluate the effectiveness and efficiency of HybridScaler by comparing with mainstream auto scaling methods. HybridScaler decreases 1639% average response time and 34-50% SLO violation rate than both static threshold-based scheme and prediction-based scheme. Meanwhile, it uses less instance-hours than static threshold-based scaling method and keeps CPU utilization almost between 60% and 70% which can avoid significant resource waste and SLO violation in the other methods.