Abstract
A key factor of win-win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.
Note to Practitioners-Resource allocation plays an important role in constructing scalable and green VCDC. This work presents a novel and fundamental framework to achieve dynamic fine-grained resource allocation. It develops a dynamic fine-grained resource allocation model with non-steady states according to the external and internal workload of different resource-intensive applications in a VCDC. In order to meet the SLA requirements of Gold and Silver services for various applications while maximizing profit, this work proposes a dynamic hybrid optimization algorithm by combing particle swarm optimization and simulated annealing. The experimental results show that the proposed method has a great potential to maximize the VCDC provider's profit. The proposed framework can aid the design and optimization of industrial cloud data centers and practitioners' understanding of SLA aspects of various applications.