Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds

Ahmad, Zulfiqar; Jehangiri, Ali Imran; Iftikhar, Mehreen; Umer, Arif Iqbal; Afzal, Ibrar

doi:10.1134/s0361768819080097

Cited by 11 publications

(12 citation statements)

References 37 publications

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“… We executed the scientific workflows Montage [43] and CyberShake. We compared the simulation results with the existing four state-of-the-art strategies i.e., QFWMS [44], EDS-DC [6], CFD [45], and BDCWS [46]. The remaining part of the article is organized by providing related work in section II, system design and model in section III, component of the proposed model in section IV, experiments, results and discussion in section V and conclusion in section VI.…”

Section: Figure 2: Characteristics Of Five Realistic Scientific Workf...mentioning

confidence: 99%

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Fault Tolerant and Data Oriented Scientific Workflows Management and Scheduling System in Cloud Computing

et al. 2022

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Cloud computing is a virtualized, scalable, ubiquitous, and distributed computing paradigm that provides resources and services dynamically in a subscription based environment. Cloud computing provides services through Cloud Service Providers (CSPs). Cloud computing is mainly used for delivering solutions to a large number of business and scientific applications. Large-scale scientific applications are evaluated through cloud computing in the form of scientific workflows. Scientific workflows are dataintensive applications, and a single scientific workflow may be comprised of thousands of tasks. Deadline constraints, task failures, budget constraints, improper organization and management of tasks can cause inconvenience in executing scientific workflows. Therefore, we proposed a fault-tolerant and data-oriented scientific workflow management and scheduling system (FD-SWMS) in cloud computing. The proposed strategy applies a multi-criteria-based approach to schedule and manage the tasks of scientific workflows. The proposed strategy considers the special characteristics of tasks in scientific workflows, i.e., the scientific workflow tasks are executed simultaneously in parallel, in pipelined, aggregated to form a single task, and distributed to create multiple tasks. The proposed strategy schedules the tasks based on the data-intensiveness, provides a fault tolerant technique through a cluster-based approach, and makes it energy efficient through a load sharing mechanism. In order to find the effectiveness of the proposed strategy, the simulations are carried out on WorkflowSim for Montage and CyberShake workflows. The proposed FD-SWMS strategy performs better as compared with the existing state-of-the-art strategies. The proposed strategy on average reduced execution time by 25%, 17%, 22%, and 16%, minimized the execution cost by 24%, 17%, 21%, and 16%, and decreased the energy consumption by 21%, 17%, 20%, and 16%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for Montage scientific workflow. Similarly, the proposed strategy on average reduced execution time by 48%, 17%, 25%, and 42%, minimized the execution cost by 45%, 11%, 16%, and 38%, and decreased the energy consumption by 27%, 25%, 32%, and 20%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for CyberShake scientific workflow.

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Section: Figure 2: Characteristics Of Five Realistic Scientific Workf...mentioning

confidence: 99%

“…An "Enhanced Data-oriented Scheduling strategy with Dynamic clustering fault-tolerant technique" (EDS-DC) is presented in [6]. The authors implemented a Data-oriented scheduling in EDS-DC.…”

Section: Related Workmentioning

confidence: 99%

Fault Tolerant and Data Oriented Scientific Workflows Management and Scheduling System in Cloud Computing

et al. 2022

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“…Execution cost is the cost of total MIPS (million instructions per second) of hosts with respect to the time frame [12,41]. The execution cost is measured in dollars and it has been calculated with the help of (2).…”

Section: Execution Costmentioning

confidence: 99%

“…Execution time is the total time taken by the task for processing and it is measured in seconds [41,42]. The execution time is calculated by using (5).…”

Section: Execution Timementioning

confidence: 99%

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

2021

KSII TIIS

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Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through welldefined components and steps. A two way fault-tolerant mechanism i.e., task-based faulttolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

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“…In [41], a data-aware scheduling strategy referred to as "Enhanced Data-oriented Scheduling strategy with Dynamic clustering fault-tolerant technique" (EDS-DC) was given. The strategy was particularly developed for applications related to scientific work.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

Ahmad

Jehangiri

Ala’anzy

et al. 2021

Sensors

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Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive tasks and also have some special characteristics. These characteristics include the tasks of scientific workflows that are executed in terms of integration, disintegration, pipeline, and parallelism, and thus require special attention to task management and data-oriented resource scheduling and management. The tasks executed during pipeline are considered as bottleneck executions, the failure of which result in the wholly futile execution, which requires a fault-tolerant-aware execution. The tasks executed during parallelism require similar instances of cloud resources, and thus, cluster-based execution may upgrade the system performance in terms of make-span and execution cost. Therefore, this research work presents a cluster-based, fault-tolerant and data-intensive (CFD) scheduling for scientific applications in cloud environments. The CFD strategy addresses the data intensiveness of tasks of scientific workflows with cluster-based, fault-tolerant mechanisms. The Montage scientific workflow is considered as a simulation and the results of the CFD strategy were compared with three well-known heuristic scheduling policies: (a) MCT, (b) Max-min, and (c) Min-min. The simulation results showed that the CFD strategy reduced the make-span by 14.28%, 20.37%, and 11.77%, respectively, as compared with the existing three policies. Similarly, the CFD reduces the execution cost by 1.27%, 5.3%, and 2.21%, respectively, as compared with the existing three policies. In case of the CFD strategy, the SLA is not violated with regard to time and cost constraints, whereas it is violated by the existing policies numerous times.

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Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds

Cited by 11 publications

References 37 publications

Fault Tolerant and Data Oriented Scientific Workflows Management and Scheduling System in Cloud Computing

Fault Tolerant and Data Oriented Scientific Workflows Management and Scheduling System in Cloud Computing

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

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