Cost-optimized Internet of Things application deployment in edge computing environment

Abstract

With the increasing popularity of cloud native and DevOps, container technology has become widely used in combination with microservices. However, deploying container-based microservices in distributed edge-cloud infrastructure requires complex selection strategies to ensure high-quality service for users. Existing container orchestration tools lack flexibility in selecting the best deployment location based on user cost budgets and are insufficient in providing personalized deployment solutions. This paper proposes a genetic algorithm-based Internet of Things (IoT) application deployment and selection strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints, which is an NP-hard problem. The genetic algorithm is introduced to solve this problem effectively and improve deployment efficiency. Comparative results show that the proposed algorithm outperforms four baseline algorithms, including time-greedy, cost-greedy, random, and PSO, using real datasets and some synthetic datasets. The proposed algorithm provides personalized deployment solutions for edge-cloud infrastructure.