Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

TLDR

Cloud providers must balance customer performance with profit, yet existing resource‑allocation studies rarely address both energy cost minimization and revenue maximization in virtualized data centers. This work proposes a profit‑optimizing strategy for virtualized cloud data centers that aligns with service‑level agreements between providers and customers. The approach models external and internal request arrival rates for virtual machines, derives a probabilistic model for non‑steady states, implements a smart controller for fine‑grained provisioning, and solves the resulting profit‑maximization problem with a hybrid simulated‑annealing/particle‑swarm metaheuristic. The algorithm delivers differentiated service quality with higher overall performance and lower energy consumption, as confirmed by trace‑driven simulations.

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.