The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others. Recently, the Internet of Things (IoT) is one of the major revolutions in information and communication technology (ICT). The IoT and its associated technologies, such as machine-to-machine (M2M) technology, extend the Internet connectivity beyond traditional smart devices like smartphones, tablets to a diverse range of devices, and everyday things (e.g. objects, machines, vehicles, buildings) to perform a variety of services and applications (e.g. health care, medicine treatment, traffic control, energy management, vehicular networking). These connected devices are generating an unprecedented amount of data, which needs to be stored, processed, and analyzed for deriving valuable insights as well as properly accessed by end users and/ or client applications. Together with it, the quantity and the scale of services and applications are increasing
The authors in REF , proposed a cost makespan aware scheduling heuristic which is a cost scheduling algorithm.
A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing
International Journal of Distributed Sensor Networks
Computer Science