SLO-Power: SLO and Power-aware Elastic Scaling for Web Services
Mehmet Savasci, Abel Souza, Wu Li, David Irwin, Ahmed Ali-Eldin, Prashant J. Shenoy · 2024
Managing the performance of online web services in cloud data centers while optimizing resource allocation and power consumption is a multifaceted challenge. Often, resource and power management techniques, such as elastic scaling and power capping, are handled independently, leading to conflicts and sub-optimal power-performance trade-offs. To tackle this issue, we introduce SLO-Power, a system that coordinates the resource and power scaling techniques to achieve power savings while adhering to service level objectives (SLOs), such as tail latency constraints. Our approach employs a combination of analytic queuing models and feedback-driven techniques to jointly allocate resources and power to cloud applications in an SLO and power-aware manner. We implement a prototype of our system and evaluate it using realistic workloads to demonstrate its ability to harmonize elastic and power scaling, enabling enhanced resource utilization and reduced power consumption while ensuring the application performance. Our findings indicate that SLO-Power achieves exceptional power and resource efficiency, approaching near-optimal power-efficiency levels at 90%, all while preventing SLO violations. Furthermore, compared to state-of-the-art solutions, SLO-Power demonstrates lower P95 latency, accompanied by a 12% reduction in resource usage.