Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research