Component-Centric Mobile Cloud Architecture Performance Evaluation: an Analytical Approach for Unified Models and Component Compatibility with Next Generation Evolving Technologies

Mohiuddin, Khalid; Islam, Asharul; Islam, Mohammad Aminul; Khaleel, Mohammad Abdul; Shahwar, Samreen; Khan, Sajid Ali; Yasmin, Sadaf; Hussain, Rashid

doi:10.1007/s11036-022-01933-7

Cited by 5 publications

(5 citation statements)

References 41 publications

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“…Noticeably, 5G network getting popularity in many countries and has enabled an efficient platform (10–100 Gbps) for decentralized computing (Meng et al, 2020). The provision of 5G encourages emerging architectures developers to design effective mobile cloud architectures (Mohiuddin, Islam, et al, 2022). Such architectures shall leverage cloud-based services (Taleb et al, 2017) and enable an effective execution environment while executing resource-intensive (Islam et al, 2020) applications (e.g., augmented reality and virtual reality) for enhancing m-learning performance.…”

Section: Methodology and Research Designmentioning

confidence: 99%

“…The architecture resources are significantly distributed in its layers. Usually, these layers are classified into physical (tangible resources model) and application layers (application execution framework) (Mohiuddin, Islam, et al, 2022). Additionally, a network layer is determined to facilitate communication between users and across the architecture.…”

Section: The Novel Architecture Designmentioning

confidence: 99%

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Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

Mohiuddin,

Fatima,

Khan

et al. 2023

SAGE Open

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This article aims to design a novel edge-centric hierarchical cloud architecture to optimize mobile learning (m-learning) performance during learners executing computation-intensive learning applications. This research adopts the potential of distributed computing paradigms, that is, mobile edge, for improving the effectiveness of m-learning performance in higher education. Edge computing enables computing at the network’s edge and effectively avoids latencies while processing learners’ computational requests. The envisioned architecture was designed on the ETSI MEC ISG protocols and deployed on the university mobile cloud infrastructure. Additionally, a use case was designed, focusing the edge computing’s latency-avoiding ability, and executing it in a real-time environment involving sixteen students from an academic course. The execution results validated the architecture’s contribution, such as tasks executed in the local server, optimized learner privacy, reduced latencies, instant access, lowered bandwidth consumption, and continued tasks’ execution despite the failure of smart nodes. The result influences user acceptance and attracts designers to extend the architecture base focusing on machine learning algorithms (for learning analytics) and blockchain (to prevent malicious attacks) to improve the effectiveness of learning management system performance.

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Section: Methodology and Research Designmentioning

confidence: 99%

Section: The Novel Architecture Designmentioning

confidence: 99%

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

Mohiuddin,

Fatima,

Khan

et al. 2023

SAGE Open

Self Cite

View full text Add to dashboard Cite

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“…Further, these applications are tailored-made, challenging to customize, and incapable of handling resource-demanding tasks. For instance, such applications' frameworks [21] are incapable of executing resource-intensive applications because the mobile devices are resource-constraints [7]. Additionally, resource-demanding applications, e.g., online video discussion, require rich cloud resources.…”

Section: M-learning Applications and Lmsmentioning

confidence: 99%

“…To execute a cloud-based m-learning application, it must deploy on a physical layer MCA. For designing a unified m-learning MCA, the following two policies should consider [21]:…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Mohiuddin

Miladi

Khan

et al. 2022

Wireless Communications and Mobile Computing

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Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.

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Network-assisted processing of advanced IoT applications: challenges and proof-of-concept application

et al. 2023

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Recent advances in the area of the Internet of Things shows that devices are usually resource-constrained. To enable advanced applications on these devices, it is necessary to enhance their performance by leveraging external computing resources available in the network. This work presents a study of computational platforms to increase the performance of these devices based on the Mobile Cloud Computing (MCC) paradigm. The main contribution of this paper is to research the advantages and possibilities of architectures with multiple offloading options. To this end, a review of architectures that use a combination of the computing layers in the available infrastructure to perform this paradigm and outsource processing load is presented. In addition, a proof-of-concept application is introduced to demonstrate its realization along all the network layers. The results of the simulations confirm the high flexibility to offload numerous tasks using different layers and the ability to overcome unfavorable scenarios.

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Component-Centric Mobile Cloud Architecture Performance Evaluation: an Analytical Approach for Unified Models and Component Compatibility with Next Generation Evolving Technologies

Cited by 5 publications

References 41 publications

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Network-assisted processing of advanced IoT applications: challenges and proof-of-concept application

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