Task scheduling with fault-tolerance in real-time heterogeneous systems

Liu, Jing; Wei, Mengxue; Hu, Wei; Xu, Xin; Ouyang, Aijia

doi:10.1016/j.sysarc.2018.08.007

Cited by 30 publications

(22 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the PB model, two copies of a task, namely, the primary copy and the backup copy, are allocated to the two different nodes. There is also an acceptance test in the model, which is adopted to check the correctness of allocations [12].…”

Section: Related Workmentioning

confidence: 99%

“…The backup copy of a task can have two alternative schemes, one is the passive backup-copy scheme [12], the other is the active backup-copy scheme [28]. For the passive backup-copy scheme, the real-time tasks should have enough laxity to restart their backup copies [29], and the backup copy of a task is allowed to execute only when a fault occurs in the primary task [30], [31].…”

Section: Related Workmentioning

confidence: 99%

“…Al-Omari et al [32] focused on a PB overloading technique that allowed the primary copy of a task to overlap with the backup copy of another task to ensure the high schedulability. There is an important assumption in these studies that the laxity of a task must be at least twice as large as its computation time so that the passive backup-copy scheme can be adopted [12]. However, this assumption is not realistic in practice, i.e., for the case of the heavily loaded real-time systems.…”

Section: Related Workmentioning

confidence: 99%

“…One of the effective ways to improve the service reliability is to design the fault tolerance based scheduling method [12]. The core of the fault-tolerant scheduling algorithm is to introduce redundancy to ensure that tasks can be completed smoothly even when permanent or transient system failures occur [13].…”

Section: Introductionmentioning

confidence: 99%

“…(1) In view of the heterogeneous, distributed resources and the ever-changing context in the edge-cloud, we propose a novel QoS-aware scheduling model with fault-tolerance in the edge-cloud, which extends the traditional primarybackup (PB) fault-tolerant model [12] to improve the reliability of services of the edge-cloud.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Sun

Fan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Sun

Fan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Kirti,

Maurya,

Yadav

2024

Concurrency and Computation

View full text Add to dashboard Cite

Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

show abstract

Fault-Tolerant Scheme of Cloud Task Allocation Based on Deep Reinforcement Learning

Tang

Dong

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Task scheduling with fault-tolerance in real-time heterogeneous systems

Cited by 30 publications

References 19 publications

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

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

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Fault-Tolerant Scheme of Cloud Task Allocation Based on Deep Reinforcement Learning

Contact Info

Product

Resources

About