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

Ouyang, Tao; Zhou, Zhi; Chen, Xu

doi:10.1109/iwqos.2018.8624174

Cited by 60 publications

(78 citation statements)

References 41 publications

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“…An effective mobility-aware offloading decision algorithm was proposed in [108], by integrating mobility prediction. In [109], performance optimization under a long-term cost budget constraint was investigated. Lyapunov optimization was applied, while the task migration cost was accounted for.…”

Section: A Vehicle As a Clientmentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

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Section: A Vehicle As a Clientmentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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“…When obtaining a content (either from the remote cloud server or from a local agent), an ED i ∈ I can achieve a certain valuation (denoted by w i ) from the content. 3 Moreover, when choosing to be an agent and cache the content, an ED i ∈ I will incur a certain caching cost, denoted by c i . The serving capacity of each agent for sharing contents with requesters is constrained by realistic factors such as the limited uplink communication capability.…”

Section: Ed Modelmentioning

confidence: 99%

“…When different contents are correlated, the coupled decisions of caching and sharing need to be jointly considered. We leave the analysis of such a scenario to our future work 3. That is, there is no difference in terms of the content valuation achieved for a requesting ED when obtaining the content from the CP or an agent.…”

mentioning

confidence: 99%

Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

Jiang

Gao

Wang

et al. 2020

IEEE Internet Things J.

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Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision-making of different stakeholders. In this paper, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the EDs can decide whether to cache and share contents for the CP, and the CP can decide to share a certain revenue with EDs as the incentive of caching and sharing contents. We formulate such an interaction as a two-stage Stackelberg game. In Stage I, the CP aims to maximize its own profit by deciding the ratio of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs by choosing to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs' best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the non-atomic game theory. Then, we identify the piece-wise structure and the unimodal feature of the CP's profit function, based on which we design a tailored low-complexity one-dimensional search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that both the CP's profit and the EDs' total welfare can be improved significantly (e.g., by 120% and 50%, respectively) by using the proposed Crowd-MECS, comparing with the Non-MEC system where the CP serves all EDs directly.

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“…Applications for Smart cities and entertainment such as smart transportation, integrated medical services, production control, industry 4.0, multimedia streaming, and online games have delay requirements below 50 ms, 1 which can be even stricter for mission‐critical applications which may be less than 10 ms 2‐4 . Therefore, we believe that industry and academia need to spend efforts that consider strategies to minimize service latency by extending and making the traditional cloud work more flexibly.…”

Section: The Inherent Heterogeneity Of the Internet Of Vehiclesmentioning

confidence: 99%

FogWise: On the limits of the coexistence of heterogeneous applications on Fog computing and Internet of Vehicles

Mendonca

Dias

d’Orey

et al. 2020

Trans Emerging Tel Tech

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Mobile devices, Internet of Things applications, and connected vehicles in the Internet of Vehicles (IoV) are proliferating. Recently, vehicles have emerged as a possibility to become communication and processing infrastructures called vehicular Fog computing (VFC). Thus, science and industry forecast increasing demand for communication and cloud infrastructures to deal with data traffic, processing, and storage. Still, they do not evaluate that properly. This work investigates how heterogeneity affects the ability of IoV and VFC to meet application requirements. We leverage Fog computing, which extends Edge and Cloud computing and complements them, especially to meet str applications with strict latency requirements. We built a simulation model that evaluates how vehicles behave in such an environment. We argue that vehicles need to follow request‐processing‐response‐actuation programming model based on distributed auction protocol to match clients and servers and still meet latency requirements. We extend and propose requirements that both applications and architectures should meet. Preliminary results indicate thresholds in terms of vehicle density, request rate, request size, client‐server ratio, and auction method in which VFC can operate to meet application requirements under mobility.

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

Cited by 60 publications

References 41 publications

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

FogWise: On the limits of the coexistence of heterogeneous applications on Fog computing and Internet of Vehicles

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