An adaptive model-free resource and power management approach for multi-tier cloud environments

With the development of cloud environments serving as a unified infrastructure, the resource management and energy consumption issues become more important in the operations of such systems. In this paper, we investigate adaptive model-free approaches for resource allocation and energy management under time-varying workloads and heterogeneous multi-tier applications. Specifically, we make use of measurable metrics, including throughput, rejection amount, queuing state, and so on, to design resource adjustment schemes and to make control decisions adaptively. The ultimate objective is to guarantee the summarized revenue of the resource provider while saving energy and operational costs. To validate the effectiveness, performance evaluation experiments are performed in a simulated environment, with realistic workloads considered. Results show that with the combination of long-term adaptation and short-term adaptation, the fluctuation of unpredictable workloads can be captured, and thus the total revenue can be preserved while balancing the power consumption as needed. Furthermore, the proposed approach can achieve better effect and efficiency than the model-based approaches in dealing with real-world workloads.

► We propose an adaptive model-free approach for managing resource and power in cloud.
► Measurable information is exploited for making adjustment decisions.
► Resource can be controlled without assuming any known distributions in advance.
► Short-term and long-term adaptations are incorporated with admission control.
► Our approach outperforms model-based approaches in both effect and efficiency.