Multiple DAGs Dynamic Workflow Scheduling Based on the Primary Backup Algorithm in Cloud Computing System

Wang, Yu; Jia, Changyun; Xu, Yang

doi:10.1109/bwcca.2014.62

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…It constructs workflow model or process model by analyzing business process. The representation of the theoretical model of workflow generally adopts description language [45], object model, rule-based method, graph or net based method, such as directed graph [46], conditional directed graph, Petri net [47] etc.. According to the application fields of workflow, it is generally divided into Business WorkFlow (BWF) and Scientific WorkFlow (SWF) [48,49].…”

Section: Workflow Theory Of Rs Computingmentioning

confidence: 99%

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Liu

Dang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the rapid development of high-resolution earth observation system, the data processing, algorithm design and system development of Remote Sensing Spatial-Temporal Big Data (RS-STBD) have gradually become the bottleneck problems in the application and development of earth observation system. The research on the model, algorithm and system of RS-STBD processing involves complex scientific problems, technical bottlenecks and inconstant requirements of engineering applications. This paper summarizes the data type and processing theory model of RS-STBD, the high performance algorithm design based on cloud service and intelligent computing, and the architecture design and engineering development methods of the complex remote sensing application system. Furthermore, the existing problems in the current research are analyzed, and the related solutions are given. Finally, the future development trend of scientific exploration, technical research and application development of RS-STBD is prospected.

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Section: Workflow Theory Of Rs Computingmentioning

confidence: 99%

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Liu

Dang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…It ranks all ready tasks in a job-ready pool and decides which task should be scheduled first. Wang et al (2014) presented a multiple directed acyclic graphs (DAGs) dynamic workflow scheduling algorithm and considered the physical link communication competition in the process of task scheduling. Regarding to the recent survey of workflow scheduling, Wu et al (2015) focused on the workflow scheduling in cloud environment and made a comprehensive survey of this problem in a problem–solution manner.…”

Section: Related Workmentioning

confidence: 99%

A look-ahead algorithm for online multiple workflow scheduling problem in heterogeneous systems

Chen

et al. 2017

Concurrent Engineering

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A look-ahead algorithm is proposed to solve the online multiple workflow scheduling problem with two constraints in heterogeneous system. In this problem, workflows come to the system online when they are released, and each workflow is composed of multiple tasks which can be executed on heterogeneous processors, according to their types. Considered two constrains including the non-preemptive processor and the task order in a workflow, the proposed algorithm utilizes the information contained in the list of the submitted workflows and optimizes the scheduling of current task according to subsequent tasks. It can keep the subsequent tasks from waiting for a long time due to the occupation of the limited resource by the current task. The simulation results show that the proposed look-ahead algorithm outperforms four classical online scheduling algorithms, and the algorithm can get better performance when the look-ahead value L = 1 than L ≥ 2.

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“…But multiple workflows are allocated immediately as per their arrival, and results are finally combined in the dynamic environment. Other multiple workflow allocation models use the level attribute to assign priorities, and managing precedence constraints to optimize one or more QoS parameters has also been proposed in the literature [19][20][21][22][23]. In this case, workflows are grouped according to the depth level, followed by mapping onto resources by exploiting the parallel executable tasks at the same depth level within the batch of DAGs.…”

Section: Introductionmentioning

confidence: 99%

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

et al. 2022

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Cloud Service Providers are speedily becoming the target platform for scientific workflow computations due to the massive possible and flexible pay-as-you-go pricing model. Workflow allocation problem in cloud systems is considered NP-hard. A heterogeneous IaaS cloud could be fully effective if the allocation method provides an efficient mapping between virtual machines (VMs) and workflow applications demanding execution. First, we model multiple workflow allocation problem in the cloud environment. Then, we propose a levelized multiple workflow allocation strategy with task merging (LMWS-TM) to optimize turnaround time for multiple workflow applications in the Infrastructure as a Service (IaaS) cloud environment to achieve better performance. The task merging scheme is incorporated into workflows after partitioning and prior to allocation to reduce inter-task communication share and the total number of depth levels for improving the overall completion time. Moreover, it considers the inter-task communication and inter-machine distance for estimating communication cost share among tasks on the schedule generated. Furthermore, the scheme is capable enough to use simple and flexible level attributes to tackle precedence constraints. Afterward, we conducted an experimental study to evaluate LMWS-TM by comparative performance analysis with its peers, namely SLBBS, DLS, and HEFT, on quality of service (QoS) parameters, namely, turnaround time, system utilization, flow time, and response time. The study reveals the superior performance of LMWS-TM among its considered peers in almost all the cases for almost all considered parameters under investigation. Finally, we performed statistical testing to test the significance level using SPSS 20, confirming the hypothesis drawn in the experimental study.

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Multiple DAGs Dynamic Workflow Scheduling Based on the Primary Backup Algorithm in Cloud Computing System

Cited by 4 publications

References 4 publications

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

A look-ahead algorithm for online multiple workflow scheduling problem in heterogeneous systems

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

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