# Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling
###### Abstract  
As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naive or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.  
fog computing; IoT; IIoT; cloud computing; resource management; scheduling +
Footnote †: 2021: 17 November 2021  
edge servers should cooperate with the central cloud center and other nearby edge servers. When unconsidered scheduling and migration algorithms trigger migrations, tuples or related data can be lost.  
To mitigate the reliability issues of cloud tasks in IoT and IIoT computing environments, in this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. Furthermore, we use information about the distance between a user and nearby edge servers in order to optimize task scheduling and migration decisions.  
Unlike the previous work related to IoT and IIoT cloud task scheduling [15; 16], we take users' mobility speed into account so that our scalable fog computing orchestration can select an optimal destination edge server for the migration. A performance evaluation based on realistic mobility data (simulation of urban mobility--SUMO [17; 18]) shows that our proposed fog computing orchestration mechanism for a reliable cloud task scheduling algorithm outperforms previous work in terms of the reliability of cloud tasks while introducing affordable data traffic on the network. It also shows that our algorithm scales well in terms of the number of IoT and IIoT users.  
The contributions of our study can be summarized as follows.  
* We discuss the IoT and IIoT system architectures to show their characteristics and their limitations in supporting cloud-based mobile applications, which will help both architects of IoT and IIoT systems and cloud data center administrators.
* We implement two scalable fog computing orchestration algorithms: one is for scheduling and assigning cloud tasks to appropriate edge servers and containers with considerations of signal strength, and the other one is for cloud task migrations that improve the overall reliability of mobile applications by checking and calculating migratability.
* We validate our proposed algorithms by comparing various performance metrics and incorporating mobility pattern data obtained from SUMO.  
The rest of the paper can be summarized as follows. Section 2 discusses our research background and related work. Section 3 proposes our scalable fog computing orchestration algorithm in IoT and IIoT computing environments. Section 4 provides comparative performance evaluation results to show the efficiency and scalability of the proposed mechanism. Finally, Section 5 concludes the paper.  
Figure 1: IoT and IIoT computing architecture with an edge cloud.