Dynamic offloading for energy-aware scheduling in a mobile cloud

Lu, Junwen; Hao, Yongsheng; Wu, Kesou; Chen, Yuming; Wang, Qin

doi:10.1016/j.jksuci.2022.03.029

Cited by 14 publications

(9 citation statements)

References 46 publications

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“…Due to the tradeoff of the parameters in the offloading process, the QoS-related offloading methods include network bandwidth, deadline, and power consumption [10]. Various offloading methods like Artificial intelligence-based applications, Directed Acyclic Graphs (DAG) scheduling, Game theory, Lyapunov optimization, Markov Decision Process, and deep learning methods [11,12] have been applied in various areas. X. Wei et al [6] proposed HACAS, which balances the system's load in both cases when the load is high, and the load is low, with maximum profit and minimum energy consumption.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

Task Scheduling on Cloudlet in Mobile Cloud Computing with Load Balancing

Ahlawat

Sangwan

2022

IJEER

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The recent growth in the use of mobile devices has contributed to increased computing and storage requirements. Cloud computing has been used over the past decade to cater to computational and storage needs over the internet. However, the use of various mobile applications like Augmented Reality (AR), M2M Communications, V2X Communications, and the Internet of Things (IoT) led to the emergence of mobile cloud computing (MCC). All data from mobile devices is offloaded and computed on the cloud, removing all limitations incorporated with mobile devices. However, delays induced by the location of data centers led to the birth of edge computing technologies. In this paper, we discuss one of the edge computing technologies, i.e., cloudlet. Cloudlet brings the cloud close to the end-user leading to reduced delay and response time. An algorithm is proposed for scheduling tasks on cloudlet by considering VM's load. Simulation results indicate that the proposed algorithm provides 12% and 29% improvement over EMACS and QRR while balancing the load.

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Section: ░ 2 Related Workmentioning

confidence: 99%

Task Scheduling on Cloudlet in Mobile Cloud Computing with Load Balancing

Ahlawat

Sangwan

2022

IJEER

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“…In recent years, with the rapid development of deep learning, the application field of graph neural networks is more and more extensive [1,2,3]. Based on the work of graph convolutional networks, graph convolution is applied in the field of traffic flow prediction [35,36,40]. The new graph convolution model (GCNM) is proposed to solve the gait phase classification problem from the non-Euclidean domain based on a graphing mechanism for exoskeletons [38].…”

Section: Introductionmentioning

confidence: 99%

Oversampling-based node imbalance classification method in graph

Jia

2023

Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023)

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Graph neural networks have been widely used in many fields. Most studies are used to solve the node classification task, but most of the time the distribution of the data is unbalanced, which affects the classification accuracy of the model. In this paper, we balance the data distribution in the graph by generating new samples in the embedded space and introducing random variables to control the spatial distance between the new samples and the target samples. We also propose a framework to solve the unbalanced node classification task in the graph, and the experimental results show that the method is effective.

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“…However, the high density of SC leads to a significant increase in the network's energy consumption. Energy efficiency is a major concern for current and future mobile networks [10][11][12]. The energy-optimized utilization is a key factor for efficient resource allocation planning and has a major impact on the user experience [13].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

2022

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This research was supported by the Ukrainian project No. 0120U100674 ("Development of the novel decentralized mobile network based on blockchainarchitecture and artificial intelligence for 5G/6G development in Ukraine"); in part by the Ukrainian project No. 0120U102201 ("Development the methods and unified software-hardware means for the deployment of the energy-efficient intent-based multi-purpose information and communication networks"),

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Dynamic offloading for energy-aware scheduling in a mobile cloud

Cited by 14 publications

References 46 publications

Task Scheduling on Cloudlet in Mobile Cloud Computing with Load Balancing

Task Scheduling on Cloudlet in Mobile Cloud Computing with Load Balancing

Oversampling-based node imbalance classification method in graph

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

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