Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

Abstract

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm -Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.

Efficient Workflow Scheduling in Edge Cloud-Enabled Space-Air-Ground-Integrated Information Systems

“…Traditional optimization scheduling algorithms in edge cloud environments strive to optimize objective functions, seeking optimal or near-optimal solutions under various constraints. These include convex optimization methods (Wang et al, 2014), mixed-integer nonlinear programming (Zhang et al, 2021), game-theoretic approaches (Guo & Liu, 2018), and heuristic methods (Soltani et al, 2017), such as Monte Carlo tree search (Yu et al, 2020), and swarm intelligence algorithms (Chen et al, 2023;. Represented by heuristic algorithms, these optimization methods are easy to implement, have low computational complexity, and can quickly find near-optimal solutions; hence they are widely used.…”