Machine learning

Crossover

Load balancing (electrical power)

Artificial intelligence

Geometry

Scheduling (production processes)

Mathematics

Genetic algorithm

Mathematical optimization

CloudSim

Operating system

Computer science

Cloud computing

Algorithm

Distributed computing

Routing (electronic design automation)

Job shop scheduling

Algorithm design

Embedded system

Estimation of distribution algorithm

Grid

IEEE Access

As one of the hot issues in cloud computing, task scheduling is an important way to meet user needs and achieve multiple goals. With the increasing number of cloud users and growing demand for cloud computing, how to reduce the task completion time and improve the system load balancing ability have attracted increasing interest from academia and industry in recent years. To meet the two aforementioned goals, this paper develops an EDA-GA hybrid scheduling algorithm based on EDA (estimation of distribution algorithm) and GA (genetic algorithm). First, the probability model and sampling method of EDA are used to generate a certain scale of feasible solutions. Second, the crossover and mutation operations of GA are used to expand the search range of solutions. Finally, the optimal scheduling strategy for assigning tasks to virtual machines is realized. This algorithm has advantages of fast convergence speed and strong search ability. The algorithm proposed in this paper is compared with EDA and GA via the CloudSim simulation experiment platform. The experimental results show that the EDA-GA hybrid algorithm can effectively reduce the task completion time and improve the load balancing ability.

An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing