QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach

Chen, Ying; Zhao, Jie; Wu, Yuan; Huang, Jiwei

doi:10.1109/tmc.2022.3223119

Cited by 41 publications

(8 citation statements)

References 44 publications

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“…It is strongly advised that, for future expansion, various PD datasets be used and that a neural network be used in the cloud, followed by an evaluation of the methodologies. Moreover, in this regard, some recent and novel research on edgecomputing task management has been indicated, which facilitates real-time processing [8] and [4].…”

Section: Discussionmentioning

confidence: 99%

“…The weight of a feature indicates how significant it is for the following groups of methods. This shows that any particular approach it may be altered dependent (4)…”

Section: Linear Regressionmentioning

confidence: 99%

“…Numerous industries now profit from cutting-edge technology by disseminating technology to the populace. A recent study demonstrates the engagement of several researchers in wireless communication [1][2][3][4][5] to improve an existing system by addressing pertinent difficulties. Some areas of research, such as AI, have played a crucial part in the evolution of intelligence over these years to develop different projects in areas such as image recognization [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

et al. 2023

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Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson’s disease to assess the methods’ performance and identify the essential features that may be used to predict the total Unified Parkinson’s disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm’s estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS.

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Section: Discussionmentioning

confidence: 99%

“…The weight of a feature indicates how significant it is for the following groups of methods. This shows that any particular approach it may be altered dependent (4)…”

Section: Linear Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

et al. 2023

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“…Wu et al [23] are driven by edge computing for target detection and image enhancement. Chen et al [24] have focused on the unloading problem in edge cloud systems and proposed the idea of game-based decentralized task offloading (GDTO) to obtain offloading strategy and analyze the upper bound for the convergence time.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Intelligent Task Scheduling for Large-Scale IoT Systems

Jin

Han

Chen

et al. 2023

Wireless Communications and Mobile Computing

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The new-generation of Internet of Things (NG-IoT) brings a wide range of challenging problems. At the same time, cloud computing technology is an important foundation for the development of the IoT. In this article, we focus on the task scheduling problem in IoT systems in cloud computing environment. Our goal is to minimize the task runtime. It is well known that the problem of the task scheduling has been a challenging problem. In the last decade, despite being theoretically hard problem, researchers design lots of state-of-the-art algorithms for solving this problem. In our work, we propose a novel efficient reinforcement learning (RL) algorithm to solve the task scheduling problem in IoT systems (EATS), which combines combinatorial optimization to make our proposed algorithm have stable lower bounds. We process a batch of tasks at a time, make decisions on task selection through reinforcement learning, and solve them further through combinatorial optimization methods. The results of the experiments show that our proposed algorithm has outstanding performance in different environments.

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“…Relevant research mainly focuses on edge computing, energy management, task unloading, etc. Mobile terminal devices that are computing intensive or sensitive to delay can offload computing tasks to edge servers for computing to reasonably complete task scheduling [13][14][15], energy management and resource allocation [16,17], thereby improving the efficiency of the cloud computing environment [18][19][20].…”

Section: Related Studiesmentioning

confidence: 99%

Efficiency and optimization of government service resource allocation in a cloud computing environment

Ya-guang

Yin²,

Wang

et al. 2023

J Cloud Comp

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According to the connotation and structure of government service resources, data of government service resources in L city from 2019 to 2021 are used to calculate the efficiency of government service resource allocation in each county and region in different periods, particularly by adding the government cloud platform and cloud computing resources to the government service resource data and applying the data envelopment analysis (DEA) method, which has practical significance for the development and innovation of government services. On this basis, patterns and evolutionary trends of government service resource allocation efficiency in each region during the study period are analyzed and discussed. Results are as follows. i) Overall efficiency level in the allocation of government service resources in L city is not high, showing an increasing annual trend among the high and low staggering. ii) Relative difference of allocation efficiency of government service resources is a common phenomenon of regional development, the existence and evolution of which are the direct or indirect influence and reflection of various aspects, such as economic strength and reform effort. iii) Data analysis for the specific points indicates that increased input does not necessarily lead to increased efficiency, some indicators have insufficient input or redundant output. Therefore, optimization of the physical, human, and financial resource allocation methods; and the intelligent online processing of government services achieved by the adoption of government cloud platform and cloud computing resources are the current objective choices to realize maximum efficiency in the allocation of government service resources.

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QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach

Cited by 41 publications

References 44 publications

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

Reinforcement Learning-Based Intelligent Task Scheduling for Large-Scale IoT Systems

Efficiency and optimization of government service resource allocation in a cloud computing environment

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