List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.

An Improved Genetic Algorithm on Hybrid Information Scheduling

Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA.