An Improved Harris Hawks Optimization Algorithm for Microservice Composition and Collaborative Optimization
Yuyao Xu, Lianglun Cheng, Tao Wang, Mingzhe Ni · 2024
As mobile and Internet of Things (IoT) applications become more complex, they increasingly rely on distributed microservices architectures where multiple microservices need to work together in different computing environments to provide a quality user experience. In this situation, effectively selecting and combining these microservices to maximize application performance and quality of service (QoS) for edge servers scheduling microservice examples with combining and synergetic collaboration strategies has become a notable challenge. In this paper, we address this challenge by proposing a novel mixed integer programming model that introduces a bernoulli gaussian wandering harris hawk algorithm (BGHHO) incorporating similar integer coding by analyzing the distributional properties of the solution space. The algorithm utilizes similar integer encoding techniques and Bernoulli chaotic sequences to improve the search mechanism, improving the response speed, solution accuracy, and convergence speed for complex microservice combination problems. The experimental results show that compared with the traditional Harris Hawk algorithm (HHO), the BGHHO algorithm is more efficient in handling these problems, providing an effective means to optimize the combination and collaboration of distributed microservices, which is of great value for improving the performance of applications under microservice architecture.