Resource Management in Fog/Edge Computing: A Survey

Abstract

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

22 pages

References in corpus (10)

• Decentralized Computation Offloading Game For Mobile Cloud Computing
• Big Data Caching for Networking: Moving from Cloud to Edge
• Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs
• Foggy: A Platform for Workload Orchestration in a Fog Computing Environment
• Feasibility of Fog Computing
• Cloud Benchmarking for Performance
• Edge-as-a-Service: Towards Distributed Cloud Architectures
• DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool
• Cloud Benchmarking For Maximising Performance of Scientific Applications
• ENGINE:Cost Effective Offloading in Mobile Edge Computing with Fog-Cloud Cooperation

Cited by in corpus (20)

• Edge AI: A Taxonomy, Systematic Review and Future Directions
• Modern Computing: Vision and Challenges
• 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
• Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
• Application-Aware Resource Allocation and Data Management for MEC-assisted IoT Service Providers
• A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions
• Multilayer Resource-aware Partitioning for Fog Application Placement
• IntentContinuum: Using LLMs to Support Intent-Based Computing Across the Compute Continuum
• Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning
• Measurement-Driven Design and Runtime Optimization in Edge Computing: Methodology and Tools
• Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization
• Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing
• DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
• Hardware-Level QoS Enforcement Features: Technologies, Use Cases, and Research Challenges
• Genetic-based optimization in Fog Computing: current trends and research opportunities
• Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices
• Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT device
• EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge-Cloud Environments
• On the Road from Edge Computing to the Edge Mesh