Advanced QoS Provisioning and Mobile Fog Computing for 5G

Shuminoski, Tomislav; Kitanov, Stojan; Janevski, Toni

doi:10.1155/2018/5109394

Cited by 13 publications

(9 citation statements)

References 53 publications

(69 reference statements)

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“…By structure, Fog computing is potentially viable to inject QoS/QoE guarantees by providing local accesses to the users rather than communicating every time to the Cloud. In literature, various issues like energy-efficient design, latency, and minimizing congestion as a whole are explored in a reactive (as and when demand comes) or a proactive (in hindsight) manner in general [21,28,29] or considering particular application in mind [30,31].…”

Section: Related Workmentioning

confidence: 99%

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective

Bandyopadhyay

Singh

Mukhopadhyay

et al. 2020

Sensors

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In the Internet of Things (IoT) + Fog + Cloud architecture, with the unprecedented growth of IoT devices, one of the challenging issues that needs to be tackled is to allocate Fog service providers (FSPs) to IoT devices, especially in a game-theoretic environment. Here, the issue of allocation of FSPs to the IoT devices is sifted with game-theoretic idea so that utility maximizing agents may be benign. In this scenario, we have multiple IoT devices and multiple FSPs, and the IoT devices give preference ordering over the subset of FSPs. Given such a scenario, the goal is to allocate at most one FSP to each of the IoT devices. We propose mechanisms based on the theory of mechanism design without money to allocate FSPs to the IoT devices. The proposed mechanisms have been designed in a flexible manner to address the long and short duration access of the FSPs to the IoT devices. For analytical results, we have proved the economic robustness, and probabilistic analyses have been carried out for allocation of IoT devices to the FSPs. In simulation, mechanism efficiency is laid out under different scenarios with an implementation in Python.

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Section: Related Workmentioning

confidence: 99%

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective

Bandyopadhyay

Singh

Mukhopadhyay

et al. 2020

Sensors

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“…We try to deploy service on device which has the minimal service response time. This is regulated in (16).…”

Section: Constraintsmentioning

confidence: 99%

“…Therefore, fog computing is appropriate for applications which are not only processed on the middle computing networks to meet low latency requirement but also processed on cloudy-level for complex, difficult requirements. Therefore, others try to prove that fog computing and its characteristics are effective on many specified applications, e.g., improving healthcare systems and their performance [11][12][13], vehicular networks and road safety [14,15], provisioning 5G mobile networks [16,17], surveillance video stream processing [18], and saving energy in cloud computing [19]. Finally, others focus on specified problems in fog computing environment, e.g., survey on security issues [20,21], building security schema [22,23], and building simulation framework named iFogSim [24] and MyiFogSim [25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Resource Provisioning on Fog Landscapes

Pham-Nguyen

Tran-Minh

2019

Security and Communication Networks

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A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

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“…However, it would result in heavy traffic burden over the transmission network and large responding latency, since the cloud servers are far away from end users. By deploying fog servers for providing supplementary resources between cloud and users, mobile fog computing (MFC) is designed as a novel computing paradigm in order to reduce responding latency of services and avoid traffic congestion of network [5]. Hence, in the cloud-aware MFC network [6], most services of users can be performed at the fog servers and others at the cloud servers.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

Qin

Zhou

et al. 2020

Wireless Communications and Mobile Computing

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With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

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Advanced QoS Provisioning and Mobile Fog Computing for 5G

Cited by 13 publications

References 53 publications

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective

Dynamic Resource Provisioning on Fog Landscapes

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

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