A novel approach for IoT tasks offloading in edge-cloud environments

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.

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment

“…By considering the lowest VM utilization of any edge server in the network, the OWB approach preferred to offload the task to these servers. The fuzzy-based competitor [70] utilized four crisp input variables (i.e., task length, network demand, delay sensitivity, and VM utilization) and one FLS process. In this approach, the task could be offloaded to one of the following three server types: a local edge server, a neighboring edge server, or a cloud server.…”

Performance Evaluation of Publish-Subscribe Systems in IoT Using Energy-Efficient and Context-Aware Secure Messages

“…First, the topic must identify the data source to the cloud platform process and store it. Cloud platforms, such as Azure [1] and AWS [2] present guidelines regarding the topic construction. However, these guidelines do not focus on the energy efficiency of the topic nor provide unique identification of devices.…”

“…Besides, JSON is one of the main structures used when there is no standard to follow [35,36]. Although, the JSON structure alone requires at least 7 bytes at the payload: {"":""} -without considering the sensor [1] https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-mqttsupport [2] https://docs.aws.amazon.com/whitepapers/latest/designingmqtt-topics-aws-iot-core/designing-mqtt-topics-aws-iotcore.html type used. About IoT sensors, the various types of sensors require efforts to identify them.…”

“…The number of Internet of Things (IoT) devices connected to the Internet continually increases as well as the data produced by these devices [1,2,3]. The IoT devices provide the services of sensing, monitoring, and automation of activities [4].…”

Applications of geospatial big data in the Internet of Things

“…To allow this analysis to be efficiently performed in domains with increasing flows of data, such as smart cities and transportation services, there is increasing application of cloud computing to enhance the data processing and analysis by providing on-demand use of a massive set of computing resources (Sarkar, Chatterjee, & Misra, 2018;Wan, Du, Zhao, & Yang, 2021). To strengthen the potential of cloud computing and enhance the robustness, scalability, and QoS of the applications, more recently, the edge and fog computing paradigms have been increasingly applied in IoT applications (Almutairi & Aldossary, 2021;Mondragón-Ruiz, Tenorio-Trigoso, Castillo-Cara, Caminero, & Carrión, 2021).…”