QoS-Based Service-Time Scheduling in the IoT-Edge Cloud

Abstract

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

“…As a result, wireless technology innovation is critical to keep pace with the ever-increasing demands for spectrum efficiency, energy consumption, mobility, flawless coverage, and the wide range of QoS and QoE needs. The network resources must be coordinated end-to-end and cross-layer to meet the needs and expectations of individual users [43]. A 6G mobile network is anticipated to provide QoS, QoE, and quality of physical experience (QoPE), as well as reliability and security [35,44].…”

Local Scheduling in KubeEdge-Based Edge Computing Environment

“…EdgeGossip is implemented on the Gossip algorithm [ 22 ], and its effectiveness was demonstrated using real-time deep-learning workloads. Mutichiro et al [ 23 ] proposed StaSA, which can satisfy the quality of service (QoS) requirements of users as an edge application. The STaSA scheduler improves cluster resource utilization and QoS in edge–cloud clusters in terms of service time by automatically assigning requests to different processing nodes and scheduling execution according to real-time constraints.…”

A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

“…On the other hand, the IoT paradigm demands new metrics for performance evaluation since the devices and systems implemented are generally different and more complex than other traditional or more studied technologies [12]. Numerous works in the related literature present IoT monitoring platforms [13][14][15].…”