IoMT-Cloud Task Scheduling Using AI

Hussain, Adedoyin A.; Al‐Turjman, Fadi

doi:10.32604/cmes.2023.022783

Cited by 1 publication

(1 citation statement)

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“…Sensors, interfaces, and other devices for cloud computing are also discussed in [17]. Sequential programs, mobile computing offloading, and GUI-based design are also consid-ered for task scheduling in mobile cloud computing [18]. In addition, some strategies are implemented for task scheduling to save the battery power of mobile devices using mobile cloud computing [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Hassan,

Al-Awady,

Ali

et al. 2024

Sensors

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Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

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Section: Literature Reviewmentioning

confidence: 99%