A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. (November 2017)

• Record Type: Journal Article
• Authors: Pham, Xuan-Qui; Man, Nguyen Doan; Tri, Nguyen Dao Tan; Thai, Ngo Quang; Huh, Eui-Nam
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 11(2017)
• Publication Date: 2017-11
• Subjects: Internet of Things -- cloud computing -- fog computing -- task scheduling -- deadline constraint; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717742073
• Languages: English
• ISSNs: 1550-1329

Abstract

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.