Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing

Adhikary, Tamal; Das, Amit Kumar; Razzaque, Md. Abdur; Almogren, Ahmad; Alrubaian, Majed; Hassan, Mohammad Mehedi

doi:10.1007/s11036-015-0657-5

Cited by 35 publications

(14 citation statements)

References 18 publications

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“…Adhikary, et al have proposed a task scheduling plan satisfying QoS and reliability factor in vehicular cloud computing using the MapReduce method. Simulation results proved the ability of the proposed plan in reducing tasks execution time.…”

Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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“…The offloading algorithms were modelled utilizing MVC model but neither the vehicular mobility network nor the communication network environment was considered. Authors in [8], proposed a reliable task-scheduling model in Vehicular Cloud Computing environment to minimize execution time and satisfy job deadlines by defining a MILP optimization issue. But the proposed model is based on map-reduce which is implied for data-intensive applications.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the performance, we consider that the client nodes generate 2 to 8 jobs per minute. These jobs are partitioned into 10 to 20 tasks each [8]. Thus we vary the number of tasks from 20 to 160 tasks for different experiments.…”

Section: Energy Aware Task Offloading In Vehicular Cloudsmentioning

confidence: 99%

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Scalable and Energy Efficient Task Offloading Schemes for Vehicular Cloud Computing

Pasha¹,

Khan²

2018

IJCNC

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Smart vehicles of today on road are equipped with advanced computational units, multiple communication technologies, intelligent sensing platforms, and human-computer interaction devices which utilize Vehicular Edge Networks to support services offered by the remote cloud. This being named as Opportunistic Vehicular Edge Computing recently, has the possibility to supplement the services provided by the Edge gadgets. Many Vehicular Edge Computing architectures have been proposed as of late which support task offloading. One among the premier difficulties in these networks is efficiently utilizing the resources available at the vehicular nodes. The present work uses APEATOVC, a conveyed and versatile protocol for economical, efficient and effective task offloading in these networks which address the adaptability of vehicular clouds. The results obtained by extensive simulations are presented to assess and contrast its performance with existing protocols.

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“…Some advanced technologies, such as tensors, cloud computing, and some intellectual frameworks, are used to analyze big data [3][4][5][6][7][8]. Big data processing, however, leads to problems, as much semi-structured and unstructured information exists.…”

Section: Introductionmentioning

confidence: 99%

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

2019

Self Cite

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Recently, the healthcare industry has caught the attention of researchers due to a need to develop a smart and interactive system for effective and efficient treatment facilities. The healthcare system consists of massive biological data (unstructured or semi-structured) which needs to be analyzed and processed for early disease detection. In this paper, we have designed a piece of healthcare technology which can deal with a patient’s past and present medical data including symptoms of a disease, emotional data, and genetic data. We have designed a probabilistic data acquisition scheme to analyze the medical data. This model contains a data warehouse with a two-way interaction between high-performance computing and cloud synchronization. Finally, we present a prediction scheme that is performed in the cloud server to predict disease in a patient. To complete this task, we used Random Forest, Support Vector Machine (SVM), C5.0, Naive Bayes, and Artificial Neural Networks for prediction analysis, and made a comparison between these algorithms.

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Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing

Cited by 35 publications

References 18 publications

Task scheduling mechanisms in cloud computing: A systematic review

Task scheduling mechanisms in cloud computing: A systematic review

Scalable and Energy Efficient Task Offloading Schemes for Vehicular Cloud Computing

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

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