Scheduling Optimization of real-time IOT system based on RNN

Ubiquitous computation, which promoted by Rapid development of Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living, Internet of Things has been identified as one of Network Infrastructure in the next generation of application domains. An architecture based on cloud computing at the center, which contribute to highly flexibility and scalablity, is an extensively used scheme to construct IOT applications. With growing number of intelligent terminals and third part application accessing on the platform, the Qos problem caused by the large-scale concurrent access rise to the surface. To address this question, a self-adaption Multi-level Feedback Queue Scheduling policy, used to reduce mean turnaround time and complexity of scheduling, based on Recurrent Neural Network (RNN) is presented in this paper. Feature parameters of queues and tasks are used as input of network, the calculated parameters are exported to optimize queue parameters continuously. This research implement a prototype of this scheme. According To demonstrate the efficiency, this thesis give performance results from our prototype and other scheduling policy.