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

• URL: http://arxiv.org/abs/2102.01348v1
• Date: Tue, 2 Feb 2021 06:47:58 GMT
• Title: QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning
• Abstract summary: This paper presents a runtime-aware controller using horizontal scaling (node allocation) and vertical scaling (resource allocation within nodes) A horizontal scaling determines the number of active nodes based on workload demands and the required scalable according to a set of rules. Then, it is coupled with vertical scaling using transfer Q-learning, which tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS) When combined, these methods allow to reduce the exploration time and violations when compared to model-free Q-learning.
• Score: 8.123268089072523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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