FBRC: Optimization of task scheduling in Fog-based Region and Cloud

© 2017 IEEE. Fog computing preserves benefits of cloud computing and is strategically positioned to address effectively many local and performance issues because its resources and specific services are virtualized and located at the edge of the customer premises. Resource management is a critical issue affecting system performance significantly. Due to the complex distribution and high mobility of fog devices, computation resources still experience high latencies in fog's large coverage area. This paper considers a Fog-based Region and Cloud (FBRC) in which requests are locally handled not just by a region but multiple regions when additional resources are needed. An efficient task scheduling mechanism is thus essential to minimize the completion time of tasks and improve user experiences. To this end, two issues are investigated in the paper: 1) designing a fog-based region architecture to provide nearby computing resources; 2) investigating efficient scheduling algorithms to distribute tasks among regions and remote clouds. To deal with the complexity of scheduling tasks, a heuristic-based algorithm is proposed based on our formulation and validated by extensive simulations

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

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