How to Place Your Apps in the Fog -- State of the Art and Open Challenges

Abstract

Fog computing aims at extending the Cloud towards the IoT so to achieve improved QoS and to empower latency-sensitive and bandwidth-hungry applications. The Fog calls for novel models and algorithms to distribute multi-service applications in such a way that data processing occurs wherever it is best-placed, based on both functional and non-functional requirements. This survey reviews the existing methodologies to solve the application placement problem in the Fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and Fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimised metrics. Finally, it identifies some open challenges in application placement in the Fog.

References in corpus (4)

• Online Placement of Multi-Component Applications in Edge Computing Environments
• A lightweight decentralized service placement policy for performance optimization in fog computing
• A Survey of Energy Efficiency in SDN Software Based Methods and Optimization Models
• Optimization policy for file replica placement in fog domains

Cited by in corpus (13)

• Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
• A Decade of Research in Fog computing: Relevance, Challenges, and Future Directions
• Secure Cloud-Edge Deployments, with Trust
• Optimizing Response Time in SDN-Edge Environments for Time-Strict IoT Applications
• Probabilistic QoS-aware Placement of VNF chains at the Edge
• Komet: A Serverless Platform for Low-Earth Orbit Edge Services
• Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
• Declarative Application Management in the Fog. A bacteria-inspired decentralised approach
• Genetic-based fog colony optimization hybridized with hierarchical clustering and its influence in the placement of fog services
• Continuous Reasoning for Managing Next-Gen Distributed Applications
• Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization
• Analysis of wireless network access logs for a hierarchical characterization of user mobility
• Combining Declarative and Linear Programming for Application Management in the Cloud-Edge Continuum