QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning

TL;DR

This paper introduces a transfer Q-learning based runtime control method for distributed many-core servers that dynamically adjusts resource allocation to minimize power consumption while maintaining QoS guarantees.

Contribution

It presents a novel transfer Q-learning approach combined with horizontal and vertical scaling for energy-efficient QoS management in distributed servers.

Findings

Reduces power consumption compared to model-free Q-learning.

Minimizes QoS violations under dynamic workloads.

Achieves scalable and portable runtime control for heterogeneous server clusters.

Abstract

Web servers scaled across distributed systems necessitate complex runtime controls for providing quality of service (QoS) guarantees as well as minimizing the energy costs under dynamic workloads. This paper presents a QoS-aware runtime controller using horizontal scaling (node allocation) and vertical scaling (resource allocation within nodes) methods synergistically to provide adaptation to workloads while minimizing the power consumption under QoS constraint (i.e., response time). A horizontal scaling determines the number of active nodes based on workload demands and the required QoS according to a set of rules. Then, it is coupled with vertical scaling using transfer Q-learning, which further tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS). It transfers Q-values within minimally explored states reducing exploration requirements. In addition, the approach exploits a scalable architecture of the many-core server allowing to reuse available knowledge from fully or partially explored nodes. When combined, these methods allow to reduce the exploration time and QoS violations when compared to model-free Q-learning. The technique balances design-time and runtime costs to maximize the portability and operational optimality demonstrated through persistent power reductions with minimal QoS violations under different workload scenarios on heterogeneous multi-processing nodes of a server cluster.