QoS-Aware Task Placement With Fault-Tolerance in the Edge-Cloud

Abstract

The geographically dispersed resources and ever-changing context incur unique heterogeneity, potential fragility, and vulnerability of an edge-cloud system. Thus, the reliability guarantee of services in the edge-cloud is critical. This paper firstly proposes a QoS-aware scheduling model with fault-tolerance in the edge-cloud, which extends the traditional primary-backup (PB) fault-tolerant model to improve the service reliability in the edge-cloud with the time constraints of tasks being satisfied. Then, a QoS-aware fault-tolerant scheduling algorithm including primary copy placement, backup copy placement and an adjustment mechanism is proposed to improve the QoS levels of tasks in the edge-cloud. The primary copy placement is to guarantee the earlier execution of the primary copy of a task to better satisfy the time requirements of tasks. The backup copy placement is to ensure the later execution of the backup copy of a task, reducing the overlapping of the two copies of a task, realizing the improvement of the resource utilization in the edge-cloud under the condition of redundancy and deadline requirements of tasks. The adjustment mechanism is triggered to rearrange the task copies of a computing node of the edge-cloud after the deallocation of a backup copy on the node, to better assist the goal-achievement of the primary and backup copy scheduling. Finally, through extensive simulation experiments with the real world taxi traces, the performance difference between the proposed method and the other four methods are evaluated. Results show that the proposed method generally outperforms the other methods in terms of guarantee ratio, average QoS level, and reliability cost. INDEX TERMS Edge-cloud, QoS, fault-tolerance, time constraint, primary-backup.

FASDQ: Fault-Tolerant Adaptive Scheduling with Dynamic QoS-Awareness in Edge Containers for Delay-Sensitive Tasks

“…This experiment applied FESTAL to the edge side for comparative experiments to verify the effectiveness of FASDQ in terms of latency and resource utilization. Fault-tolerance based QoS-aware scheduling algorithm (FTBQA)[ 19 ]: adjusts the QoS level of tasks and has an adjustment mechanism, which is invoked when the backup copy of a task in a compute node is deleted after the corresponding primary copy succeeds. Non-Resource Adjustment (NONRAD): a variant of FASDQ that does not use the resource-adaptive adjustment mechanism.…”

“…Fault-tolerance based QoS-aware scheduling algorithm (FTBQA)[ 19 ]: adjusts the QoS level of tasks and has an adjustment mechanism, which is invoked when the backup copy of a task in a compute node is deleted after the corresponding primary copy succeeds.…”

“…The research work in [ 18 ] provided fault tolerance for the Internet of Things (IoT) by modeling the failure behavior of the Internet grid storage area network (SAN) to provide fault tolerance through redundancy to reduce system downtime. In [ 19 ], the PB model was extended to the edge cloud, and a fault-tolerant based QoS-aware scheduling algorithm (FTBQA) was proposed to improve the reliability of edge cloud application services. DeCoRAM middleware [ 20 ] tolerates processor failures by considering the execution time and failover order of primary and backup copies in order to meet the soft real-time requirements of each task.…”

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

“…The method, however, targets erasure coding, and it is focused on Cloud resources rather than ENs. More similar to our work, [37] focuses on optimization of ENs resource allocation, considering as parameters QoS and reliability, without considering energy. In [18], the authors focused on multi-objective offline provisioning, focusing on energy and cost efficiency rather than on reliability.…”

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

“…This data placement strategy was developed with due considerations of contextual dimensions that focused on reducing the delay during the process of satisfying the user tasks requirements. Furthermore, Sun et al 26 contributed a fault-tolerant QoS-sensitive scheduling scheme for improving service reliability in edge computing by satisfying the time constraints. Lin et al proposed a resource allocation scheme based on the differential game and feedback control.…”