Genetic-based optimization in fog computing: Current trends and research opportunities

Fog computing is a new computational paradigm that emerged from the need to reduce network usage and la-tency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The het-erogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a compre-hensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a taxonomy of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, work-flow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

Automated summary

Fog computing, as a new computational paradigm, has emerged to reduce network usage and latency in the IoT. This paper provides a comprehensive review of recent research works on genetic-based fog resource optimization. The authors propose a taxonomy for optimizing fog infrastructures and classify 70 papers accordingly. They evaluate the papers based on their genetic optimization design and outline the benefits and limitations of each work. The study concludes that more research efforts are needed to address current challenges and improve the design and deployment of genetic algorithms in fog domains.