Fault Tolerance Impact on Near Field Communication for Data Storage of Mobile Commerce Technology in Cloud Computing

Ahmad, Nesar; Herawan, Tutut; Rahman, Mohd Tarmizi Abd; Abdullah, Zailani; Mustafa, Balsam A.; Fakharaldien, Mohammed Adam Ibrahim

doi:10.1007/978-981-13-1799-6_51

Cited by 1 publication

(1 citation statement)

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“…Based on the task offloading schemes, specific groups of jobs are scheduled based on the criteria. Noraziah et al [ 20 ] implemented a fault tolerance technique in which cloud providers and cloud users/customers share their responsibilities in response to fault tolerance techniques to efficiently utilize resources and energy efficiency. As in [ 21 , 22 ], different application faults can be repaired and detected at the user level, however, hardware and virtual machine faults can be identified and repaired at the cloud level.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

Ali

Iqbal

Jamil

et al. 2021

Sensors

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Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

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Section: Related Workmentioning

confidence: 99%