Efficient Resource Allocated For Time-Sensitive IoT Applications In Cloud And Fog Environment

Abstract

In recent years, Internet of Things (IoT) has attracted the attention of both academia and commercial organizations. Nowadays, IoT provides great benefits to society that lead to significant improvements of the quality of our daily life. Examples include: smart city, smart homes, autonomous driving cars or airplanes ,and health monitoring systems. Cloud computing provides to IoT systems a series of services such as data computing, processing or storage, analysis and securing. It is estimated that by the year 2025, approximately trillion IoT devices will be used. As a result, a huge amount of data is going to be generated. In addition, in order to efficiently and accurately work, there are situations where IoT applications (such as Self Driving, Health Monitoring, etc.) require quick responses. In this context, the traditional Cloud computing systems will have difficulties in handling and providing services. To balance this scenario, and in order to reach this objective and to overcome the disadvantages of Cloud computing, a new computing model called Fog computing has been proposed. In this work, we first introduce a comparison between these two paradigms Fog computing and Cloud computing . We consider task scheduling for IoT applications in a Cloud - Fog computing environments, where the Fog computing does not eliminate the Cloud computing, it is complementary to it . For efficiently executing IoT applications. We are proposing an efficient architecture and algorithm for resource allocation in Fog computing environment. Our algorithm overcomes some problem on the requirement of service level flexibility and availability of resources whose major Target is to improve the Quality of Service (QoS) of IoT applications .In our work, we use CloudAnalyst, CloudSim and iFogSimsimulation toolkit to simulate our systems. The numerical findings show that our system achieves better performance and work more efficient than Cloud computing. It also reduced the response time, processing time , networks usage in Cloud and cost of execution.