Research on Optimal Scheduling in Cloud Computing

Aiming at the problem of unbalanced load and slow convergence speed of task scheduling based on ant colony algorithm, an improved task scheduling optimization algorithm is proposed. The pheromone update rules of ant colony algorithm are optimized by giving weight to speed up the solution speed. The comprehensive performance of the algorithm is optimized by dynamically updating the volatilization coefficient, and in the updating process of local pheromone. The load weight coefficient of virtual machine is introduced to ensure the load balance of virtual machine. The experimental results show that the task scheduling strategy of the improved algorithm can not only ensure the reasonable allocation of tasks, but also improve the convergence speed and shorten the total execution time.

A scheduling and control integration optimization method for regenerative braking energy utilization

The utilization of regenerative braking energy is of great significance to the energy saving of subway. Therefore, this paper proposes an optimization method for scheduling and control integration, which not only adjusts the timetable but also optimizes the speed curves of trains. When there is a train braking, this method will try to find a train that accelerates to absorb the regenerative energy generated by the braking train. Firstly, this paper establishes the timetable energy saving optimization model, based on which the speed curves will be optimized. Furthermore, we design a scheduling optimization algorithm based on genetic algorithm, and optimizes speed curves of trains by binary search method to obtain a good solution. Finally, simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the integrated scheduling and control optimization method can reduce energy consumption by 15.18%. In the random disturbance simulations, the proposed method shows good robustness, which makes it possible to apply this method to the real subway operations.

Multi-path Allocation Scheduling Optimization Algorithm for Network Data Traffic Based on SDN Architecture

The explosive growth of network data traffic puts new demands on traffic scheduling. In this paper, the scheduling algorithm based on the software-defined network (SDN) architecture is studied. Firstly, the SDN architecture was introduced, then an SDN-based adaptive multi-path load balancing algorithm was proposed and finally the algorithm was simulated on the Mininet simulation platform to compare the performance of the proposed algorithm and traditional equal-cost multi-path routing algorithm by using Ryu as the controller. It was found that the proposed algorithm had greater throughput, higher bandwidth utilization and shorter transmission time in allocation scheduling of network data traffic, which could effectively reduce network congestion and ensure the reliability of the network. This study verifies the effectiveness of the proposed method in allocation scheduling of network data traffic and provides some theoretical bases for the further application of the SDN architecture-based allocation scheduling algorithm.