一种云环境下基于混合型BBO的任务调度算法

任务调度在云计算中占有重要地位，是影响云计算性能的关键因素，被证明是NP问题。启发式算法是解决该问题的最有效方法之一，针对近年来出现的一种新型启发式算法--BBO算法展开研究，由于BBO算法在求解过程中收敛速度较慢，因此结合粒子群算法提出了一种新型算法的任务调度算法--HMBBO，并结合Cloudsim云仿真平台，进行了以Makespan为目标函数的比对实验。实验结果表明，与几种经典的启发式算法相比，HMBBO算法具有寻优能力强、收敛速度快、求解质量高的特点，为解决云计算环境中任务调度问题提供了一种新思路。

A hybrid biogeography-based optimizationalgorithm for task scheduling in cloud computing

Task scheduling plays a critical role in cloud computing and is a key factor affecting the performance of cloud computing. It has been proved to be an NP problem. Heuristic algorithm is one of the most effective methods to solve this problem. This paper focuses on the Biogeography-Based Optimization (BBO) algorithm, which serves in recent years as a new heuristic algorithm. Because the BBO algorithm converges slowly in the solution process, by combining Particle Swarm Optimization (PSO) algorithm, we propose a novel task scheduling algorithm, named Hybrid Migrating Biogeography-Based Optimization (HMBBO). A comparison experiment using Makespan as the objective function is performed on the Cloudsim cloud simulation platform. The experiment results show that, compared with several classical heuristic algorithms, HMBBO has the advantages of strong optimization ability, fast convergence speed and high-quality solution, and provides a new way to solving the task scheduling problem in cloud computing environment.