In the virtual machine migration process, one of the most problems is how to select the candidate migrating virtual machine list from the abnormal physical hosts in cloud data center. Therefore, a Greedy Algorithm Optimization based Virtual Machine Selection algorithm (GAO-VMS) was proposed. In GAO-VMS, the virtual machines with the optimal objective functions would be selected to perform the migration and the candidate migration virtual machine list was formed subsequently. There are three kinds of greedy modes in GAO-VMS: Maximum Power Reduction Policy (MPR), minimum migration Time and Power Tradeoff policy (TPT) and Violated million instructions per second-Virtual Machines policy (VVM). GAO-VMS was evaluated on CloudSim simulator. Simulation results show that compared to the common virtual machine migration strategy, GAO-VMS reduces the energy consumption of cloud data center by 30% - 35%, and reduces the number of virtual machine migrations by 40% - 45% with 5% increment of the Service Level Agreement (SLA) violation rate and the joint index of SLA violation and energy. The proposed GAO-VMS strategy can be used for enterprises to construct green cloud computing center.

Internet of Things (IoT) communication system has the characteristics of small active user number and short data frame, while the pilot and user identification code required by channel estimation and multi-user detection will greatly reduce the communication efficiency and response speed of IoT system. To solve these problems, a blind channel estimation and multi-user detection algorithm based on Non-Orthogonal Multiple Access (NOMA) was proposed. Firstly, the spread spectrum matrix in Code Division Multiple Access (CDMA) system was used to allocate the carrier to each user, and the constellation rotation problem caused by blind estimation was solved by differential coding. Secondly, according to the sparsity of carriers allocated to users, the Bernoulli-Gaussian (B-G) distribution was introduced as a prior distribution, and the hidden Markov characteristic between the variables was used to perform the factor decomposition and modeling, and the multi-user identification was carried out based on sparse features of user data. Finally, the above model was deduced by message passing algorithm to solve multi-user interference caused by NOMA, and the joint channel estimation and detection receiver algorithm for IoT environment was obtained. The simulation results show that, compared with Block Sparse Single Measurement Vector (BS-SMV) algorithm and Block Sparse Adaptive Space Pursuit (BSASP) algorithm, the proposed algorithm can achieve a performance gain of about 1 dB without increasing the complexity.