Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Mora, Higinio; Gimeno, Francisco José Mora; Pont, María Teresa Signes; Volckaert, Bruno

doi:10.1155/2019/3951495

Cited by 31 publications

(14 citation statements)

References 59 publications

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“…However, mobile agents relatively do not require much external assistance; the optimization of them themselves is more important and effective. For this situation, Fernando et al 24 and Mora et al 25 also require further researches on mobile agent to meet increasingly complex needs. Our reference list also includes Wang et al 26,27 Wang et al 26 proposes a microservice coordination method for MEC, in which Markov decision process framework is used to coordinate problems and a reinforcement learning approach is adopted to find the optimal solution.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…These auxiliary modules are not necessary for mobile agents but will make mobile agents unable to exert their intelligence. Then, different agents may have different migration strategies and algorithms, but servers in the container framework often use only one general algorithm when guiding container migration, 25 making it difficult to manage and utilize multiple methods. At last, the optimization work done by existing researches may not be suitable for mobile agents, because they are optimizations of the framework.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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Mobile agent‐based service migration in mobile edge computing

Guo

Jiang

et al. 2020

Int J Communication

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Summary In order to improve the efficiency of service migration in mobile edge computing (MEC), and obtain higher quality of service and better user experience, we take mobile agent technology, instead of container, as the application component hosting technology. The advantages of mobile agent include small size, high flexibility, autonomy, and suitability for heterogeneous environments. For 5G communications, how to offer cross‐platform support and deal with frequent roaming of mobile nodes is our main concern. Therefore, this paper provides a service migration framework using mobile agent in MEC environment and performance evaluation. Experiments with tasks of different complexity show that in terms of efficiency of service migration, mobile agent technology obviously outperforms container technology. The main contributions of this paper include the following: (1) we solve the problem that too many auxiliary modules were used in the container architecture, (2) we use the agent container and the resource manager (RM) for performance evaluations to investigate the differences between mobile agent and container technology, and (3) we use the decision tree to confirm the execution cost of each node so as to analyze why the mobile agent responds to migration commands slowly.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Mobile agent‐based service migration in mobile edge computing

Guo

Jiang

et al. 2020

Int J Communication

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“…For the third solution and for comparison purpose, we take the version of the solution proposed by [5] and denote it Original Simulated Annealing Offloading (OSAO). In this solution, the local execution probability increases with the increase of the computing density.…”

Section: Original Simulated Annealing Offloadingmentioning

confidence: 99%

“…Mobile Edge Computing (MEC) concept [1][2][3], it has been recognized as the next generation computing infrastructure that is based on Mobile Cloud Computing paradigm [4][5][6][7]. It can offer nearby customized services that require good transmission bandwidth, additional data storage and processing.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

Ghmary¹,

Chanyour²,

Hmimz³

et al. 2019

IJECE

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<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>

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“…This paradigm plays a key role in the development of citizen-centric applications since edge computing can be deployed on several layers at the edge of the network, e.g. fog, mobile edge computing (MEC), ad hoc clouds, and cloudlets [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

et al. 2019

Self Cite

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New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city's multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments. INDEX TERMS Mobile cloud computing, data processing, distributed architectures, smart city, urban computing.

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Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Cited by 31 publications

References 59 publications

Mobile agent‐based service migration in mobile edge computing

Mobile agent‐based service migration in mobile edge computing

Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

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