Publication | Open Access
Fog Orchestration for Internet of Things Services
330
Citations
12
References
2017
Year
Cluster ComputingEngineeringFog Computing SecuritySmart CityComplex Iot ServicesComputational ComplexityIot SystemFog ComputingSystems EngineeringInternet Of ThingsComputer EngineeringMobile ComputingComputer ScienceIot ArchitectureService OrchestrationFog NetworksFog OrchestrationEdge ComputingCloud ComputingIndustrial Informatics
Large‑scale IoT services such as healthcare, smart cities, and marine monitoring rely on fog computing, yet dynamic variations, heterogeneity, and computational complexity make orchestration challenging. The article reviews core issues, challenges, and future research directions in fog‑enabled orchestration for IoT services. It outlines a conceptual framework for addressing these challenges and identifies gaps in current approaches. Early experiments demonstrate that a distributed genetic algorithm can feasibly orchestrate IoT services in fog environments.
Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.
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