(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Keat, Low Choon; Ang, Tan Fong; Chong, Chun Yong; Tew, Yiqi

doi:10.4018/ijwsr.299017

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…In [ 20 ], the authors suggested a method for placing incoming modules onto Fog devices that takes into account Quality of Experience (QoE) and energy efficiency. This involves using Fuzzy logic-based approaches and a multi-constraint single objective optimization technique for QoE-aware application mapping.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

Alasmari,

Alwakeel,

Alohali

2023

Sensors

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The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing’s limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies.

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Section: Related Workmentioning

confidence: 99%

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

Alasmari,

Alwakeel,

Alohali

2023

Sensors

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“…Many research studies have tried to address the placement problem from various perspectives. Some have considered minimising the overall application latency [12,[15][16][17][18] while placing the application modules on fog nodes, whereas some research has considered the energy consumption [19][20][21] of the fog nodes while placing the application modules. Despite the efforts in the previous research, there's a need for more robust solutions that take into account both latency, energy consumption, and the completion time of the applications.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Meta-Heuristic Algorithm for Application Module Placement in Iot-Fog-Cloud Computing Environment

Murali¹,

Yakubu²

2023

Preprint

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“…Partial offloading efficiently utilizes both the local and MEC resources. With good splitting of a task between local processing and MEC offloading, partial offloading can efficiently minimize the task execution time, and it is a promising technique for the IoT [ 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Partial Offloading with Relay and Adaptive Bandwidth Allocation for the MEC-Assisted IoT

Imtiaz

Tang

2022

Sensors

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The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function.

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(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Cited by 5 publications

References 21 publications

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

A Hybrid Meta-Heuristic Algorithm for Application Module Placement in Iot-Fog-Cloud Computing Environment

Multi-Task Partial Offloading with Relay and Adaptive Bandwidth Allocation for the MEC-Assisted IoT

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