Mist-Edge-Cloud (MEC) Computing: An Integrated Computing Architecture

Hensh, Falguni; Gupta, Mayank; Nene, Manisha J.

doi:10.1109/icesc51422.2021.9532929

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…In a similar manner, the total processing time (Ttotal) is determined by considering the same factors as the total energy cost. It can be calculated using the following equation: Ttotal = Ttr + Tc + Ta (6) where Ttr represents the packet transmission time, Tc denotes the packet classification time, and Ta represents the packet allocation time.…”

Section: Discussionmentioning

confidence: 99%

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Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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Section: Discussionmentioning

confidence: 99%

“…Hensh et al, 2021 [ 6 ] studied and analysed the impact of extending the computing process through the Cloud, Edge, and Mist layers to balance the workload among them and engage all layers to be a part of the computing process. The authors divided the priority of tasks into four types to select a resource.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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“…By leveraging MEC, users can experience faster response times, improved security, 11 reduced communication costs, and a more responsive user experience 8,12,13 . This technology is particularly relevant for emerging applications such as augmented and virtual reality, 14–16 autonomous vehicles, and smart cities, where real‐time processing of large volumes of data is critical 17,18 …”

Section: Introductionmentioning

confidence: 99%

“…is particularly relevant for emerging applications such as augmented and virtual reality, [14][15][16] autonomous vehicles, and smart cities, where real-time processing of large volumes of data is critical. 17,18 However, effective resource allocation is crucial to fully leverage the capabilities of MEC and overcome limitations.…”

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confidence: 99%

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

Khani,

Sadr,

Jamali

2023

Concurrency and Computation

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SummaryNetwork architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC.

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An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

et al. 2022

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In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

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Mist-Edge-Cloud (MEC) Computing: An Integrated Computing Architecture

Cited by 8 publications

References 35 publications

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

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