A Cost-Effective Time-Constrained Multi-workflow Scheduling Strategy in Fog Computing

Ding, Ruimiao; Li, Xuejun; Liu, Xiao; Xu, Jia

doi:10.1007/978-3-030-17642-6_17

Cited by 27 publications

(16 citation statements)

References 22 publications

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“…Ding et al [47] proposed a cost-effective scheduling strategy for multi-workflow with time constraints in Fog computing. This scheduling strategy uses multi-layer resources of Fog and Cloud computing called (CTSF).…”

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

“…After detailing some of the works in Cost Efficiency-based Scheduling category, we observed that authors in [47,48] have proposed scheduling methods and strategy for multi-workflow standard applications. While the author in [49] applied these methods on workflow standard applications.…”

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

“…The aim is to reduce the cost of using resources. The execution time is taken in consideration by reducing the makespan [47,49] or respecting the deadlines for real-time applications [48]. For the cost calculation, both works [48,49] considered the type of resources and to which layers of the paradigm belong (Fog or Cloud), however, the work in [47] and the work of previous sections such as [22,36,37] did not optimize the cost based on the type of resources.…”

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

“…The work in Ding et al [47] is based on a hybrid type optimization algorithm (PSO and Min-Min) to improve the quality of the result. Stavrinides et al [48], Pham et al [49] incorporated priority algorithms in their approaches to respond efficiently to the deadline constraint.…”

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

Belmahdi¹,

Mechta²,

Harous³

2021

ISI

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The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the algorithm used in the scheduling, the QoS optimized by the proposed approach and the type of applications in order to show what is suitable for critical IoT (CIOT), massive IoT (MIOT) and Industry IoT (IIOT). Finally, we present a comparison of the different simulation tools used to evaluate these approaches to guide fog computing developers/researchers which tool is suitable and most flexible for simulating the application under consideration.

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Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

Section: Cost Efficiency Based Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

Belmahdi¹,

Mechta²,

Harous³

2021

ISI

View full text Add to dashboard Cite

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“…Due to these issues, task scheduling in FC had witnessed substantial attention of the researchers. For instance, many researchers proposed task scheduling methods considering various parameters such as execution time, 8‐11 response time, 12‐17 deadline, 18‐23 cost, 14,15,22,24‐29 energy, 12,30‐38 security, 20,23,26,39,40 load balancing, 9,18,31,40 resource reallocation, 21 and optimal task‐resource pairing 24,39,41,42 . Although, a lot of research has already been carried out in task scheduling, but still there is a scope for further improvement.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Kaur

Kumar

2021

Concurrency and Computation

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The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real‐time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta‐analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.

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Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

Fan

Zhang

2022

Concurrency and Computation

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Summary The elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.

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A Cost-Effective Time-Constrained Multi-workflow Scheduling Strategy in Fog Computing

Cited by 27 publications

References 22 publications

A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

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