A steady increase in Internet-of-Things (IoT) applications needing large-scale computation and long-term storage has lead to an overreliance on cloud computing. The resulting network congestion in the cloud, coupled with the distance of cloud data centers from IoT, contributes to unreliable end-to-end response delay. Fog computing has been introduced as an alternative to cloud, providing low-latency service by bringing processing and storage resources to the network edge. In this survey, we sequentially present the phases required in the implementation and realization of practical fog computing systems: 1) design and dimensioning of a fog infrastructure; 2) fog resource provisioning for IoT application use and IoT resource allocation to fog; 3) installation of fog frameworks for fog resource management; and 4) evaluation of fog infrastructure through simulation and emulation. Our focus is on determining the implementation aspects required to build a practical large-scale fog computing infrastructure to support the general IoT landscape.

Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey

/pdf/ifogsim2-an-extended-ifogsim-simulator-for-mobility-6hpzen2d.pdf

iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments

Context-aware scheduling in Fog computing: A survey, taxonomy, challenges and future directions

Technological evolution of mobile devices, such as smart phones, laptops, wearable and other handheld devices have come up with the emergence of different user applications in learning, social networking, entertainment, and community computing domains. Many of such applications are fully or partially offloaded to the nearby server capable with high computing and storage resources. The delivery of task offloading results to the users is a challenge in those networks where the frequency of user mobility is high, leading to increased latency, higher energy consumption and inefficient resource utilization. In this paper, we survey the existing studies which optimize the task offloading in edge networks with mobility management. We formulate taxonomy of the research domain for classification of research works. We compare the listed state-of-the-art research works based on the components identified from taxonomy. Moreover, we debate future research directions for mobility, security, and scalability aware MEC offloading.

Mobility-aware computational offloading in mobile edge networks: a survey

LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing

MobFogSim: Simulation of mobility and migration for fog computing

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MobFogSim: Simulation of mobility and migration for fog computing