Selecting and utilizing proper virtual machines in a virtualized cloud platform to achieve high availability, throughput, reliability, as well as low cost and makespan is very important. The importance lies in the adaptive resource provisioning to satisfy variant of workloads. An Adaptive Accessing Aware Algorithm (A5) is proposed in this paper to deal with this conflicting objective optimization problem. The main strategy of A5 is selecting adaptive upper/lower bound of service capacity to decide the time for scheduling redundant virtual machines and a Pareto-front-based multi-objective optimization method to decide the number of scheduling virtual machines. We carried out experiments in simulation, which show that A5 can achieve much higher performance improvements in four different workload testing environments, compared with other three commonly used methods. © 2011 World Scientific Publishing Company.