In order to reduce cost and improve efficiency, many data centers adopt virtualization solutions. The advent of virtualization allows multiple virtual machines hosted on a single physical server. However, this poses new challenges for resource management. Web workloads which are dominant in data centers are known to vary dynamically with time. In order to meet application's service level agreement (SLA), how to allocate resources for virtual machines has become an important challenge in virtualized server environments, especially when dealing with fluctuating workloads and complex server applications. User experience is an important manifestation of SLA and attracts more attention. In this paper, the SLA is defined by server-side response time. Traditional resource allocation based on resource utilization has some drawbacks. We argue that dynamic resource allocation directly based on real-time user experience is more reasonable and also has practical significance. To address the problem, we propose a system architecture that combines response time measurements and analysis of user experience for resource allocation. An optimization model is introduced to dynamically allocate the resources among virtual machines. When resources are insufficient, we provide service differentiation and firstly guarantee resource requirements of applications that have higher priorities. We evaluate our proposal using TPC-W and Webbench. The experimental results show that our system can judiciously allocate system resources. The system helps stabilize applications' user experience. It can reduce the mean deviation of user experience from desired targets.

High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.

The spectrum handoff problem for cognitive radio systems is considered in this paper. The secondary users (SUs) can only opportunistically access the spectrum holes, i.e. the frequency channels unoccupied by the primary users (PUs). As long as a PU appears, SUs have to vacate the channel to avoid interference to PUs and switch to another available channel. In this paper, a prediction-based spectrum handoff scheme is proposed to reduce the negative effect (both the interference to PUs and the service block of SUs) during the switching time. In the proposed scheme, a hidden Markov model is used to predict the occupancy of a frequency channel. By estimating the state of the model in the next time instant, we can predict whether the frequency channel will be occupied by PUs or not. As a cross-layer design, the spectrum sensing performance parameters false alarm probability and missing detection probability are taken into account to enhance accuracy of the channel occupancy prediction. The proposed scheme will react on the spectrum sensing algorithm parameters while the spectrum handoff performance is significantly affected by them. The interference to the PUs could be reduced obviously by adapting the proposed spectrum handoff scheme, associated with a potential increase of switch delay of SUs. It will also be helpful for SUs to save broadband scan time and prefer an appropriate objective channel so as to avoid service block. Numerical results demonstrate the above performance improvement by using this prediction-based scheme.