Abstract
Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.
Highlights
In recent years, the Information Technology (IT) sector has developed at a massive rate, in which more than 50 billion Internet of Things (IoT) devices will be connected to the internet in the coming years [1,2,3,4]
This work trades utilization for reduced overall service time; it could lead to wastage in Conclusion and future work This paper has presented and evaluated a novel task offloading approach for latency-sensitivity IoT applications in the Edge-Cloud systems
The obtained results show that the scheduling algorithms of offloading tasks not considering application characteristics and system behavior could lead to service time degradation for latency-sensitive applications
Summary
The Information Technology (IT) sector has developed at a massive rate, in which more than 50 billion Internet of Things (IoT) devices will be connected to the internet in the coming years [1,2,3,4]. Their approach ranks the resources at the edge with their capabilities and assigns tasks to a suitable server based on the task’s requirements (e.g., CPU, RAM and Bandwidth) This method focused on improving the performance of application service time, but without explicitly considering application latency-sensitivity. Tasks scheduling approach for minimum latency In the Edge-Cloud environment, IoT devices produce a stream of incoming offloading tasks that differ in terms of their computation and network demand. This would require an efficient task scheduling technique that considers these differences in order to enhance the overall service performance and minimize the delay in the processing of offloaded tasks. The following is a brief description of the process of fuzzy logic system
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