QoS-aware placement of microservices-based IoT applications in Fog computing environments

Abstract

The Fog computing paradigm, offering cloud-like services at the edge of the network, has become a feasible model to support computing and storage capabilities required by latency-sensitive and bandwidth-hungry Internet of Things (IoT) applications. As fog devices are distributed, heterogeneous and resource-constrained, efficient application scheduling mechanisms are required to harvest the full potential of such computing environments. Due to the rapid evolution in IoT ecosystems and also to better suit fog environment characteristics, IoT application development has moved from the monolithic architecture towards the microservices architecture that enhances scalability, maintainability and extensibility of the applications. This architecture improves the granularity of service decomposition, thus providing scope for improvement in QoS-aware placement policies. Existing application placement policies lack proper utilisation of these features of microservices architecture, thus failing to produce efficient placements. In this paper, we harvest the characteristics of microservice architecture to propose a scalable QoS-aware application scheduling policy for batch placement of microservices-based IoT applications within fog environments. Our proposed policy, QoS-aware Multi-objective Set-based Particle Swarm Optimisation (QMPSO), aims at maximising the satisfaction of multiple QoS parameters (makespan, budget and throughput) while focusing on the utilisation of limited fog resources. Besides, QMPSO adapts and improves the Set-based Comprehensive Learning Particle Swarm Optimisation (S-CLPSO) algorithm to achieve better convergence in the fog application placement problem. We evaluate our policy in a simulated fog environment. The results show that compared to the state-of-the-art solutions, our placement algorithm significantly improves QoS in terms of makespan satisfaction (up to 35% improvement) and budget satisfaction (up to 70% improvement) and ensures optimum usage of computing and network resources, thus providing a robust approach for QoS-aware placement of microservices-based heterogeneous applications within fog environments.