Virtual machine placement is one of the core problems of resource scheduling in cloud data center. It has an important impact on the performance, resource utilization and energy consumption of data center. In order to optimize the data center energy consumption, improve resource utilization and ensure Quality of Service (QoS), a fuzzy membership degree based virtual machine placement algorithm was proposed. Firstly, combined the overload probability of physical hosts with the fitness placement relationship between virtual machines and physical hosts, a new distance measurement method was proposed. Then, according to the fuzzy membership function, the fitness fuzzy membership matrix between virtual machines and physical hosts was calculated. Finally, with the mechanism of energy awareness, the local search was performed in the fuzzy membership matrix to obtain the optimal placement scheme of the migration virtual machines. Simulation results show that the proposed algorithm can reduce the energy consumption of cloud data center, improve resource utilization and ensure QoS.

Virtual Machine Placement (VMP) is the core of virtual machine consolidation and is a multi-objective optimization problem with multiple resource constraints. Efficient VMP algorithm can significantly reduce energy consumption, improve resource utilization, and guarantee Quality of Service (QoS). Concerning the problems of high energy consumption and low resource utilization in data center, a Discrete Bat Algorithm-based Virtual Machine Placement (DBA-VMP) algorithm was proposed. Firstly, an optimization model with multi-object constraints was established for VMP, with minimum energy consumption and maximum resource utilization as optimization objectives. Then, the pheromone feedback mechanism was introduced in the bat algorithm by emulating the pheromone sharing mechanism of artificial ant colonies in the foraging process, and the bat algorithm was improved and discretized. Finally, the improved discrete bat algorithm was used to solve the Pareto optimal solutions of the model. The experimental results show that compared with other multi-objective optimization algorithms for VMP, the proposed algorithm can effectively reduce energy consumption and improve resource utilization, and achieves an optimal balance between reducing energy consumption and improving resource utilization under the premise of guaranteeing QoS.

Aiming at the shortcomings of Artificial Bee Colony (ABC) algorithm, which is not easy to jump out of the local optimal value, a Multiple Interactive Artificial Bee Colony (MIABC) algorithm was proposed. The proposed algorithm was based on the basic ABC algorithm, involved the random neighborhood search strategy and the cross-dimensional search strategy, and improved the treatment when bees exceed the limit, so the search way of the algorithm became various, the algorithm itself had stronger bound and it's hard to trap in the local optimal value. Meanwhile, the convergence analysis and performance test were carried out. The simulation result based on five kinds of classic benchmark functions and experimental results for time complexity show that comparing with the standard ABC algorithm and basic Particle Swarm Optimization (PSO), this proposed method has faster convergence speed which is increased by about 30% and 65% at 1E-2 accuracy and better search precision, besides, it has significant advantages in solving high dimensional problems.

Real networks are of large delay and dynamics. According to the IMC (Internal Model Control) and improved control theory model with TCP/AQM, an active queue management algorithm suitable for the large delay network environment was designed to cope with the large delays. For the dynamics of the networks, the impact that the change of network parameter brings to the algorithm was analyzed to correct the algorithm parameter online. The adaptive active queue management algorithm suitable for the large delay network was acquired. The reliability of the algorithm has been verified by NS2 simulation.