Heterogeneity Model for Wireless Mobile Cloud Computing &amp; its Future Challenges

Aslam, Bakhtawar; Abid, Rabia; Rizwan, Muhammad; Ahmad, Fahad; Sattar, Mian Usman

doi:10.1109/icecce47252.2019.8940681

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…CC is a business model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with little management effort or interaction from service providers [8]. Through virtualization, CC can support a huge customer base with varying computation needs on the same physical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…before makin choice. Among all the non-functional indicators of cloud services, reliability is argua the most important and should thus be the primary factor when selecting services [5-CC is a business model that enables convenient, on-demand network access shared pool of configurable computing resources (e.g., networks, servers, storage, ap cations, and services) that can be rapidly provisioned and released with little managem effort or interaction from service providers [8]. Through virtualization, CC can suppo huge customer base with varying computation needs on the same physical infrastruct Contrary to prior paradigms, such as clusters and grid computing, CC is service-orien it offers virtualized resources on demand as quantifiable and billable utilities [9,10].…”

Section: Introductionmentioning

confidence: 99%

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InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

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Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The studies found that large tasks can be broken down into smaller tasks and task precedence requirements. The fine granularity of the task level offloading can achieve both time and energy saving [ 14 ].…”

Section: Introductionmentioning

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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Heterogeneity Model for Wireless Mobile Cloud Computing & its Future Challenges

Cited by 2 publications

References 7 publications

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

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

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