Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Ma, Xiao; Zhou, Aoying; Zhang, Shan; Li, Qing; Liu, Alex X.; Wang, Shangguang

doi:10.1109/tmc.2021.3115262

Cited by 23 publications

(3 citation statements)

References 77 publications

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“…This dynamic property of the edge layer requires an architecture to support it and the corresponding software to handle it. Thus, the inclusion of this paradigm in the simulator could be an advantage for work that focuses on the development of task scheduling techniques for this kind of system (Ma et al 2021;Wang et al 2021). -Energy consumption: Currently, many task scheduling proposals focus on the sustainable development of the IoT, thus prioritising energy consumption optimisation over the makespan of the tasks to be processed (Ghafari et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Simulate IoT Towards the Cloud-to-Thing Continuum Paradigm for Task Scheduling Assessments.

Barriga,

Chaves-González,

Barriga

et al. 2023

JOT

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Aiming to optimise the performance of the computing layers of Internet of Things (IoT) systems, one of the most widespread techniques is the well-known task scheduling. Not only to develop but also to put task scheduling techniques in production, they have to be tested. Nevertheless, IoT systems are complex scenarios with high technological heterogeneity. Thus, testing task scheduling methods in the IoT context involves an investment of money, time and effort in acquiring devices, their configuration, deployment, etc. To avoid this, the system can be simulated and tests can be conducted through these simulations. Moreover, the underlying technical complexity of IoT systems can be reduced by increasing the abstraction level from which these systems are designed. Simulators based on model-driven development can help both to test and tackle the technological complexity of IoT systems. In this paper, a Domain-Specific Language based on SimulateIoT is proposed for the design, code generation and simulation of IoT systems for the assessment of task scheduling methods. Simulations include the generation and offloading of workflow-based tasks, the components required to handle these tasks, as well as the required resources to integrate the users' task scheduling methods in the simulations. All this, while providing an infrastructure based on the cloud-to-thing continuum paradigm on which to deploy and test these task scheduling environments, i.e., simulations can include the mist, edge, fog and cloud layers and the federation between them. In addition, a case study focused on an Industrial IoT (IIoT) system is illustrated to show the applicability of the proposed simulator.

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Section: Discussionmentioning

confidence: 99%

Simulate IoT Towards the Cloud-to-Thing Continuum Paradigm for Task Scheduling Assessments.

Barriga,

Chaves-González,

Barriga

et al. 2023

JOT

View full text Add to dashboard Cite

show abstract

“…IoT nodes need to meet the characteristics of low latency after collecting data, which cannot be satisfied by traditional cloud computing task scheduling [21][22]. In [23], the authors used the Markov decision process to propose a onedimensional search algorithm to solve the power-limited latency minimization problem, which was an optimal search algorithm. However, this method needs to obtain accurate information, such as the channel quality of the cluster and the task arrival rate in advance, which has certain limitations in practical application.…”

Section: Task Offloading Strategies In Mecmentioning

confidence: 99%

An IoT Data Clustering Algorithm for Efficient and Cost-Effective Human Resource Assessment

Wu¹,

Chen²,

Shen³

2023

J Multimed Inf Syst

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Information and communication technology development has made human resource assessment a top concern for enterprises. However, a key challenge with human resource assessment is adapting to the rapidly changing environment and utilizing various data types for decisionmaking. Unfortunately, the transmission latency of human resource assessment data in cloud computing platforms can negatively impact user experience. The emergence of mobile edge computing provides a new option for computing-intensive tasks like human resource assessment. A new objective function has been proposed to optimize the task offloading strategy, along with a mathematical model that utilizes the deep recurrent Q-network (DRQN) algorithm to leverage multiple Internet of Things (IoT) nodes. As the amount of human resource assessment data collected by IoT nodes continue to grow, extracting the potential laws and assisting human resources in making informed decisions is increasingly important. A fuzzy weighting clustering algorithm has been proposed to address the difficulty of determining the initial cluster number and center in data clustering based on soft subspace combined with a competitive merging mechanism. Simulation results demonstrate that the DRQN algorithm performs well in terms of energy consumption, cost, and latency, proving that the data collected by IoT nodes can meet the needs of human resource assessment and management after real-time processing. The proposed data clustering algorithm also accurately clusters human resource assessment data, meets high-dimensional data clustering needs, and has significant practical application value.

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“…We could not straightly transfer the services to mobile devices from PCs (Elgendy, An ef􀅫icient and secured framework for mobile cloud computing, 2018). So, to discover techniques on how to deliver responsive and appropriate communication services for mobile devices, additional research is needed (Ma, 2021).…”

Section: Enhanced Facilitymentioning

confidence: 99%

Mobile Cloud Computing (MCC): A Survey of Privacy, Security and their Impact on Social Life of Users

Hassan¹,

Bawazir²,

Irshad³

et al. 2022

J. manag. pract. humanit I am. sci

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A foremost debated area in the IT world is Mobile Cloud Computing (MCC) which merges cloud computing and mobile computing; MCC is a prospective technology of mobile services that have been inaugurated together with the fast development in mobile applications and cloud computing technology. MCC is in the early phase of development, so it is essential to have in-depth knowledge of the technology to identify future research. Cloud computing has seen exceptional advancement in the past couple of years regarding its turn of application and development. Thus, development and research in cloud computing are scaling in a positive direction because of the increasing demand for cloud computing. On the other hand, no one is concerned about its effect on mental health. People are more concerned about the privacy, access, and security of their data over the cloud. Users are not aware of the data stored in cloud technology. Therefore, end users are always worried about the security and privacy of personal data. When cloud base services are used, users have mental stress because of the unknown place of the data storage. Consistent use of cloud-based services creates a sense of anxiety among users that are more particular about data privacy. The present article gives a transitory explanation of cloud computing and mobile computing. Applications such as M-healthcare, M-commerce, and M-gaming have been discussed to debate potential applications for mobile cloud computing.

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Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Cited by 23 publications

References 77 publications

Simulate IoT Towards the Cloud-to-Thing Continuum Paradigm for Task Scheduling Assessments.

Simulate IoT Towards the Cloud-to-Thing Continuum Paradigm for Task Scheduling Assessments.

An IoT Data Clustering Algorithm for Efficient and Cost-Effective Human Resource Assessment

Mobile Cloud Computing (MCC): A Survey of Privacy, Security and their Impact on Social Life of Users

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