A framework for extending resources of embedded systems using the Cloud

Modern embedded systems generate a large volume of data that require a highly scalable and available framework that enables computing, storage, and data analysis. Many cloud providers offer unlimited storage as well as a flexible processing infrastructure, allowing for executing a large number of tasks with high performance. This paper presents a framework for extending the local resources of embedded systems, which are very limited in terms of computing, memory, and storage, using cloud computing. This framework provides scalability and high availability for embedded systems. Moreover, a scheduling algorithm is implemented to minimize the execution time and computation cost of using the cloud, and it is according to a logic which directly depends on the criticality and computation requirements of the tasks. The framework has been implemented using Windows Azure services. We evaluated the framework in term of performance. The results indicate that the framework performance has improved as well as the throughput efficiency increases by 55% after integrating the local resources, which include the embedded system, to the cloud.