A Makespan-optimized Task-Level Scheduling Strategy for Cloud Workflow Systems

Zhang, Rui; Shi, Wenyu

doi:10.1109/ainit54228.2021.00145

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Zhang et. al [38] implemented a novel makespan model to schedule cloud workflow in task-layer. The authors explored a VM's time shared characteristic and used Ant Colony Optimisation (ACO) to obtain the optimal makespan.…”

Section: Related Workmentioning

confidence: 99%

“…Goal Research field Method Guo et al [9] Minimise makespan Cloud Fuzzy Rimal and Maier [23] Minimise makespan, cost, and tardiness Cloud -Zhou et al [24] Maximise completion ratio and minimise bandwidth consumption Broadcast EDS Zaourar et al [25] Minimise makespan and energy consumption Manufacturing PSO Manasrah and Ali [26] Minimise makespan, cost and balancing load Cloud GA & PSO Rodriguez and Buyya [27] Minimise makespan, cost, deadline, and VM Cloud -Anwar and Deng [28] Minimise makespan and cost Cloud BoTs & MIP Sun et al [29] Minimise makespan and resource utilisation rate E-Stream EFT & max-min fairness Ghafouri et al [30] Minimise makespan and cost Cloud back-tracking Xie et al [31] Minimise makespan and cost Cloud & Edge PSO Eldesokey et al [32] Minimise makespan, execution time and cost Cloud PSO & SSO Attiya et al [33] Minimise makespan Cloud & IoT MRFO & SSO Kumar et al [34] Minimise makespan Cloud B&B Al-Maytami et al [35] Minimise makespan and cost Cloud DAG Gade et al [36] Minimise makespan Cloud NALCA Tarafdar et al [37] Minimise makespan and energy consumption Cloud Greedy & ACO Zhang et al [38] Minimise makespan and execution time Cloud ACO Xia et al [39] Minimise makespan Cloud TOPSIS & MT Tian et al [40] Minimise which go through a segment of «tasks» uncoupled and connected by «communication channels», and then, the data is delivered to one or more data sink. Sources and sinks of data can be applications, databases, sensors, etc.…”

Section: Refmentioning

confidence: 99%

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A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

Freire

Frantz

Basto‐Fernandes

et al. 2023

Preprint

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Contemporaneous environments involve a large number of information which is received from many interconnected devices and integrated by many platforms. The Internet of Things and Cloud computing offer services to deal with such big data, so that enterprises can play in their business core by outsourcing the development, the configuration, and the maintenance of those services and their computational resources. Many enterprises use integration Platform as a Service to maintain their applications and data working in a synchronous way. However, current task scheduling algorithms of integration platforms are facing some hardships to tackle with high volumes of data. In this article, we propose a scheduling algorithm based in the round-robin heuristic through multiple task queue, which presents better performance than the traditional First-in-first-out heuristic used by current platforms. We experimented our algorithm in the execution of an integration process, and we validated the results with statistical techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

Freire

Frantz

Basto‐Fernandes

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have widely studied workflow scheduling problems, and obtaining an optimal scheduling solution is an NP‐hard problem due to the dependencies between tasks 4,5 . Most existing research focuses on optimizing VM's rental cost or workflow's makespan 6,7 . However, some essential issues were ignored in these studies.…”

Section: Introductionmentioning

confidence: 99%

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 multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling

Lu,

Zhu,

Huang

et al. 2024

Future Generation Computer Systems

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A Makespan-optimized Task-Level Scheduling Strategy for Cloud Workflow Systems

Cited by 3 publications

References 20 publications

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

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

A multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling

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