Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

As the application of desktop virtualization systems (DVSs) continues to gain momentums, the security issue of DVSs becomes increasingly critical and is extensively studied. Unfortunately, the majority of current researches on DVSs only focuses on the virtual machines (VMs) on the servers, and overlooks to a large extent the security issue of the clients. In addition, traditional security techniques are not completely suitable for the DVSs' particularly thin client environment. Towards finding a solution to these problems, we propose a novel behavioral anomaly detection method for DVS clients by creating and using process portraits. Based on the correlations between users, virtualized desktop processes (VDPs), and VMs in DVSs, this proposed method describes the process behaviors of clients by the CPU utilization rates of VMs located on the server, constructs process portraits for VDPs by hidden Markov models and by considering the user profiles, and detects anomalies of VDPs by contrasting VDPs' behaviors against the constructed process portraits. Our experimental results show that the proposed method is effective and successful.