PhD Thesis Defense: Mehmet Savasci, SLO-Aware Power Management for Elastic Cloud Applications
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Speaker: Mehmet Savasci
The enormous power consumption of cloud data centers poses severe financial and environmental concerns. Server consolidation, server throttling, and power capping emerge as several of the numerous approaches proposed to manage the power consumption of data centers. While these methods notably increase data center power efficiency by reducing the power consumption of data center servers, they also negatively impact the performance SLOs of hosted applications. Thus, data center operators must grasp and navigate the tradeoffs between power and performance. The motivation behind this thesis is to design techniques for managing power-performance tradeoffs in cloud data centers. To such an end, this thesis investigates the connections between the power usage of cloud data center servers and the performance SLOs of applications hosted on the servers, designs models that capture these connections, and develops controllers aimed at controlling the power usage of data center servers while considering application performance SLO targets.
This thesis presents the following three key contributions. First, I design techniques to automate the process of power-performance controller generation for latency-sensitive web applications, ensuring that the generated controllers control power allocation while meeting application performance SLOs and offer improved performance compared to state-of-the-art techniques. I achieve this by designing DDPC, an autonomous data-driven controller generation system for power-performance management. Second, I introduce SLO-Power, a framework for effectively coordinating elastic resource provisioning techniques and power capping methods under performance constraints. SLO-Power shows improvements over the standalone state-of-the-art elastic resource provisioning and power capping. Finally, I design PADS, a hardware-agnostic power capping technique that integrates horizontal and vertical scaling of CPU resources—termed diagonal scaling—with the power-performance models of applications to keep the total power consumption of servers under power cap while respecting application SLOs. I show that PADS outperforms the state-of-the-art power capping solution in the literature regarding dealing with power cap violations and keeping application performance under control.
Advisor: Prashant Shenoy