Aiming at the issue of low efficiency existing in resource scheduling of cloud computing, a new resource scheduling algorithm of cloud computing based on Quality of Service (QoS) was proposed. Firstly, the quality function and convergence factor were used in Ant Colony Optimization (ACO) algorithm to ensure the efficiency of pheromone updating and the feedback factor was set to improve the selection of probability. Secondly, the local search efficiency of Shuffled Frog Leading Algorithm (SFLA) was improved by setting crossover factor and mutation factor in the SFLA. Finally, the local search and global search of the SFLA were introduced for updating in each iteration of ACO algorithm, which improved the efficiency of algorithm. The simulation experimental results of cloud computing show that, compared with the basic ACO algorithm, SFLA, Improved Particle Swarm Optimization (IPSO) algorithm and Improved Artificial Bee Colony algorithm (IABC), the proposed algorithm has advantages in four indexes of QoS:the least completion time, the lowest cost of consumption, the highest satisfaction and the lowest abnormal value. The proposed algorithm can be effectively used in resource scheduling of cloud computing.

In multicast routing comunication of smart grid, concerning the long communication delay of multicast tree when transmitting control messages to high-power load device, which caused by only considering delay constraint without considering the demand of smart grid, a new multicast tree construction method that considered load and comunication delay at the same time was proposed, namely multicast routing algorithm based on Demand Response (DR) capability constraint. Firstly, a complete graph satisfying the constraint was generated according to the grid network topology. Then, a lower-cost multicast tree was constructed by using the Prim algorithm. Finally, the multicast tree was restored to the original network. The simulation results show that the proposed algorithm can effectively reduce the demand response delay of high-power load devices, and can significantly reduce the power frequency deviation compared with the multicast routing algorithm only considering delay constraint. This algorithm can actually improve the real-time demand response in the smart grid and stabilize the grid frequency.