Arash Deldari - Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.
Ghasem Akbarian - Department of Computer Engineering, Imamreza International University, Mashhad, Iran.
Mostafa Sabzekar - Department of Computer Engineering, Birjand University of Technology, Birjand, Iran.

: In the recent years, the Internet of things (IoT) applications have affected many aspects of human life due to the continuous innovation in hardware, software, and communication technologies along with the growth of the connected devices. The enormous amounts of data generated by these devices must be stored and then processed for analysis and decision-making. One promising solution for this purpose is cloud computing. However, offloading tasks to the cloud imposes costs that must be reduced by intelligent techniques and optimization algorithms. Fortunately, considering cloud computing instances with dynamic pricing referred to as spot instances can significantly reduce the processing costs. Although these instances offer a considerable cost reduction compared to on-demand instances, they can be evicted by the cloud providers and require special scheduling techniques. In this paper, we propose a dynamic scheduling method for IoT task offloading on Amazon EC۲ spot instances. The proposed method considers both the predicted execution time of the task and the specified deadline that can be mapped on spot instances. The empirical results denote that the proposed method leads to a considerable reduction in the execution costs, while it simultaneously increases the number of successful tasks executed before the deadline and decreases task turnaround time.

Index terms, Cloud Computing, The Internet of Things, Spot instances, Task offloading