A utility-based resource allocation scheme in cloud-assisted vehicular network architecture

Meng, Hanlin; Zheng, Kan; Zhao, Hui; Ma, Lin

doi:10.1109/iccw.2015.7247447

Cited by 28 publications

(19 citation statements)

References 10 publications

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“…Moreover, there is a clear separation between the device level and the edge level. In the literature, such edge servers are named for example cloudlets [41], [42], micro data centers [43], [44], nano data centers [45] or local cloud [46]. They can be located for example in shops, enterprises, or co-located with the base stations of the telecom access network.…”

Section: Used Breakdown Of Architecturesmentioning

confidence: 99%

“…In the offline case, Wang et al [77] show that their problem is equivalent to the shortest-path problem and solve it by using dynamic programming. Meng et al [46] solve Bellman equations recursively, Rodrigues et al [70] use integration techniques, and Arkian et al [51] consider fuzzy logic and Q-learning.…”

Section: Resource Optimizationmentioning

confidence: 99%

“…Liu [54] Stationary Stationary Mascitti [53] Mobile Liu [55] Mobile Meng [46] Stationary+Mobile Stationary Qi [82] Mobile Stationary Mtibaa [83] Mobile…”

Section: Device Cloudmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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Section: Used Breakdown Of Architecturesmentioning

confidence: 99%

Section: Resource Optimizationmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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“…Moreover, there is a clear separation between the device level and the edge level. In the literature, such edge devices are named for example cloudlets [37,38], micro data centers [39,40], nano data centers [41] or local cloud [42]. They can be located for example in shops, enterprises, or co-located with the base stations of the telecom access network.…”

Section: Architecture Categoriesmentioning

confidence: 99%

Latency-aware Resource Management at the Edge

Toczé

2020

Linköping Studies in Science and Technology. Licentiate Thesis

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“…Chen et al [19] proposed a D2D Crowd framework for mobile edge computing, where a crowd of devices leverage network-assisted D2D collabora-tion15 for computation and communication resource sharing with key objective of achieving energy-efficient collaborative task executions at the network edge for mobile users. In cloud-enabled IoV, literature [20] investigated a cloudassisted vehicular network architecture in which each cloud has its own features, and a corresponding optimal scheme was obtained by solving a Semi-Markov Decision Process aimed at maximizing the system's expected average reward. To improve network capacity and system computing capability, [21] extended the original cloud radio access network (C-RAN) to integrate local cloud services to provide a lowcost, scalable, self-organizing, and effective solution called enhanced C-RAN with essential technologies of D2D and heterogeneous networks based on a matrix game theoretical approach.…”

Section: Related Workmentioning

confidence: 99%

Game Theory Based Opportunistic Computation Offloading in Cloud-Enabled IoV

et al. 2019

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With the growing popularity of the fifth-generation (5G) wireless systems and cloud-enabled Internet of Vehicles, vehicular cloud has been introduced as a novel mobile device computing mode, which enables vehicles to offload their computation-intensive tasks to neighbors. In this paper, we first present a 5G cloud-enabled scenario of vehicular cloud computing where a vehicular terminal works either as a service provider with idle computation resources or a requestor who has a computation-intensive task that can be executed either locally or offloaded to nearby providers via opportunistic vehicle-to-vehicle communications. Then, we study the following issues: 1) how to determine the appropriate offloading rate of requestors; 2) how to select the most appropriate computation service provider; 3) how to identify the ideal pricing strategy for each service provider. We address the above-mentioned problems by developing a two-player Stackelberg-game-based opportunistic computation offloading scheme under situations involving complete and incomplete information that primarily considers task completion duration and service price. We simplify the former case into a common resource assignment problem with mathematical solutions. For the latter case, Stackelberg equilibriums of the offloading game are derived, and the corresponding existence conditions are concretely discussed. Finally, a Monte-Carlo simulation-based performance evaluation shows that the proposed methods can significantly reduce the task completion duration while ensuring the profit of service providers, thus achieving mutually satisfactory computation offloading decisions. INDEX TERMS Computation offloading, 5G cloud-enabled IoV, vehicle-to-vehicle communication, Stackelberg equilibrium.

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A utility-based resource allocation scheme in cloud-assisted vehicular network architecture

Cited by 28 publications

References 10 publications

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Latency-aware Resource Management at the Edge

Game Theory Based Opportunistic Computation Offloading in Cloud-Enabled IoV

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