Mobility Support for Fog Computing: An SDN Approach

Bi, Yuanguo; Han, Guangjie; Lin, Chuan; Deng, Qingxu; Guo, Lei; Li, Fuliang

doi:10.1109/mcom.2018.1700908

Cited by 79 publications

(32 citation statements)

References 10 publications

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“…In edge computing, edge nodes and user devices have high mobility. Currently, several works focus on supporting mobility in fog computing by applying such approaches as a SDN-based method [107] and a mathematical method [108]. However, in these approaches, location privacy cannot be ensured due to location disclosure to edge nodes.…”

Section: B Future Research Directionsmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

148

103

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Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications such as environmental monitoring, smart navigation and smart healthcare come with new requirements such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

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Section: B Future Research Directionsmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

148

103

View full text Add to dashboard Cite

show abstract

“…An emerging technique, software-defined network (SDN) [8] is proposed to provide seamless and transparent mobility support to users. In the SDN based fog computing architecture [9], the routing logic and intelligent management logic are deployed on the SDN controller, which dramatically simplifies network operation and management.…”

Section: Base Stationmentioning

confidence: 99%

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

Ouyang

Zhou

Chen

2018

2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)

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Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we first design an approximation algorithm based on Markov approximation to seek a nearoptimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes.

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“…The authors of [4] propose a more all-encompassing solution that introduces an SDN layer between the cloud and fog layers. They implement signaling operations through the SDN that allows them to implement both proactive and reactive handovers.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

“…However, none of these cater to accommodating the nuances fast-moving vehicles in a fog computing environment. Studies such as [2], [4] and [26] also test their approaches using synthetic user movements which simulate users and vehicles moving within tight parameters. In contrast to this, our test simulations utilize a dataset of actual vehicle movements.…”

Section: Our Contributionmentioning

confidence: 99%

Using machine learning for handover optimization in vehicular fog computing

Memon

Maheswaran

2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long shortterm memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set.

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Mobility Support for Fog Computing: An SDN Approach

Cited by 79 publications

References 10 publications

A Survey on Secure Data Analytics in Edge Computing

A Survey on Secure Data Analytics in Edge Computing

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

Using machine learning for handover optimization in vehicular fog computing

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