DEFT: Dynamic Fault-Tolerant Elastic scheduling for tasks with uncertain runtime in cloud

Yan, Hui; Zhu, Xiaomin; Chen, Huangke; Guo, Hui; Zhou, Wen; Bao, Weidong

doi:10.1016/j.ins.2018.10.020

Cited by 38 publications

(22 citation statements)

References 21 publications

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“…IoT applications cause serious fault issues. Some researches work on FTS and resource management problems in distributed computing [11–14]. When a large number of tasks fail, a distributed system crashes.…”

Section: Related Workmentioning

confidence: 99%

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Sharif

Nickray²,

Shahidinejad

2020

IET Communications

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Section: Related Workmentioning

confidence: 99%

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Sharif

Nickray²,

Shahidinejad

2020

IET Communications

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“…It is vital to improve the resource utilization in cloud computing environment. Hui Yan et al 38 proposed a fault tolerant algorithm to accomplish resource utilization. The data center comprises multiple hosts, each having its own VM.…”

Section: Dynamic Fault-tolerant Elastic Schedulingmentioning

confidence: 99%

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Qureshi

Fayaz

et al. 2020

International Journal of Distributed Sensor Networks

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An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gaps.

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“…Luo et al [15] develop a real-time fault-tolerant algorithm RDFTAHS to schedule preemptive periodic tasks on heterogeneous distributed systems to boost system reliability. Yan et al [16] focus on the fault tolerance when task runtime is uncertain. They propose an efficient fault-tolerant scheduling algorithm DEFT for real-time tasks in the cloud, aiming to achieve both fault tolerance and resource utilization efficiency, whilst not taking hosts' communication time into account.…”

Section: B Related Workmentioning

confidence: 99%

A Novel and Adaptive Transient Fault-Tolerant Algorithm Considering Timing Constraint on Heterogeneous Systems

2020

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Due to high performance and low power consumption, heterogeneous processors are widely used in many real-time systems. In these systems, if tasks are not completed before deadline, it will cause disastrous consequences, and thus it is important to provide fault-tolerance. This paper proposes a novel, adaptive and transient fault-tolerant scheduling algorithm to solve the fault-tolerant problem in heterogeneous real-time systems, aiming to improve system reliability within a given deadline. Since task replication is efficient in improving system reliability, the proposed algorithm supports multiple replicas for each primary task and allows the primary tasks and their replicas to be scheduled on the same processor to increase reliability and lower latency. Also, the algorithm can dynamically adjust the number of replicas for each task to accommodate the deadline and ensure higher reliability. Simulated results show that the proposed algorithm can achieve higher reliability in comparison with existing and related fault-tolerant algorithms. To be specific, the proposed algorithm can obtain the reliability of 89.37% whereas the two existing algorithms DB-FTSA and FTSA obtain the reliability of 47.05% and 84.75% for the benchmark of sixty tasks, respectively, to be detailed in Fig. 4 in experiment.

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DEFT: Dynamic Fault-Tolerant Elastic scheduling for tasks with uncertain runtime in cloud

Cited by 38 publications

References 21 publications

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

A Novel and Adaptive Transient Fault-Tolerant Algorithm Considering Timing Constraint on Heterogeneous Systems

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