Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions

Abstract

The Fog computing paradigm utilises distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of fast-evolving IoT applications. Due to the fine-grained modularity of the microservices and their independently deployable and scalable nature, MSA exhibits great potential in harnessing Fog and Cloud resources, thus giving rise to novel paradigms like Osmotic computing. The loosely coupled nature of the microservices, aided by the container orchestrators and service mesh technologies, enables the dynamic composition of distributed and scalable microservices to achieve diverse performance requirements of the IoT applications using distributed Fog resources. To this end, efficient placement of microservice plays a vital role, and scalable placement algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. Thus, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications placement within Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the placement problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.

41 pages, 11 figures, submitted to ACM Computing Surveys

References in corpus (7)

• YAFS: A simulator for IoT scenarios in fog computing
• A lightweight decentralized service placement policy for performance optimization in fog computing
• Availability-aware Service Placement Policy in Fog Computing Based on Graph Partitions
• Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures
• Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
• A Cost-Effective Workload Allocation Strategy for Cloud-Native Edge Services
• Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Cited by in corpus (5)

• Computing: Looking Back and Moving Forward
• Energy Metrics for Edge Microservice Request Placement Strategies
• TraDE: Network and Traffic-aware Adaptive Scheduling for Microservices Under Dynamics
• Achieving Observability on Fog Computing with the use of open-source tools
• Application Placement with Constraint Relaxation