Two-Tier Matching Game in Small Cell Networks for Mobile Edge Computing

Du, Yu; Li, Jun; Shi, Long; Liu, Tingting; Shu, Feng; Han, Zhu

doi:10.1109/tsc.2019.2937777

Cited by 26 publications

(13 citation statements)

References 33 publications

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“…The baseline scheme referred to in Fig. 12, is used in several previous works [16]- [19], [21]- [24], [36] and consists in allocating resources at each time slot where a request is received. However, adopting such policy may make the EN reallocate resources continuously at each node to satisfy the requests, which may have a direct impact on the latency.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The frequency of checking ED resources (through resource representation model) depends essentially on the frequency of reallocating the resources. More precisely, the resource's status is checked every time the resource allocation is needed instead of being checked at each given time slot as in [16]- [19], [21]- [24]. We could solve this problem either statically or dynamically.…”

Section: B Proposed Solutionmentioning

confidence: 99%

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Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Abouaomar

Cherkaoui

Mlika

et al. 2021

IEEE Internet Things J.

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Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing is introduced to enable low latency by moving the tasks processing closer to the users at the edge of the network. The edge of the network is characterized by the heterogeneity of edge devices forming it; thus, it is crucial to devise novel solutions that take into account the different physical resources of each edge device. In this paper, we propose a resource representation scheme, allowing each edge device to expose its resource information to the supervisor of the edge node through the mobile edge computing application programming interfaces proposed by European Telecommunications Standards Institute. The information about the edge device resource is exposed to the supervisor of the EN each time a resource allocation is required. To this end, we leverage a Lyapunov optimization framework to dynamically allocate resources at the edge devices. To test our proposed model, we performed intensive theoretical and experimental simulations on a testbed to validate the proposed scheme and its impact on different system's parameters. The simulations have shown that our proposed approach outperforms other benchmark approaches and provides low latency and optimal resource consumption.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: B Proposed Solutionmentioning

confidence: 99%

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Abouaomar

Cherkaoui

Mlika

et al. 2021

IEEE Internet Things J.

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show abstract

“…In [26], the joint optimization of MD association and computation offloading was performed to achieve a goal of energy consumption minimization for a mobile edge computing system with multiple tasks. In [27], a two-tier matching algorithm was developed to solve a problem of computing resource management and trading for small-cell networks. In [28], the joint optimization of uploading power of MDs, MD association and UAV (unmanned aerial vehicle) trajectory was performed to maximize the sum bit offloaded from MDs to UAVs.…”

Section: A Related Workmentioning

confidence: 99%

“…where θ takes a small enough value (e.g., 1e ´8) to avoid "0/0". By combining (31) and (32), we can achieve α nk " θ if x nk " 0, and have (26) if x nk " 1; under (33) and (34), we can attain β nki " θ if c ki y nk " 0, and have (27) if c ki y nk " 1; under (35) and (36), we can obtain γ nk " θ if x nk " 0, and have (28) if x nk " 1; according to (37) and (38), we can easily get η nki " θ if c ki p1 ´bni q y nk " 0, and have ( 29) if c ki p1 ´bni q y nk " 1. ❑ Now, the whole procedure used for solving the problem P3 can be found in Algorithm 4.…”

Section: Solving the Resource Allocation Subproblemmentioning

confidence: 99%

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Mobile Device Association and Resource Allocation in Small-Cell IoT Networks with Mobile Edge Computing and Caching

Zhou¹,

Yue²,

Dong³

et al. 2022

Preprint

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To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the framework of mobile edge computing and caching has been widely regarded as a promising solution. When such a framework is implemented in small-cell IoT (Internet of Tings) networks, it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. Since these servers are integrated into small base stations (BSs), the assignment of them refers to not only the BS selection (i.e., MD association), but also the selection of computing and caching modes. To mitigate the network interference and thus enhance the system performance, some highly-effective resource partitioning mechanisms are introduced for access and backhaul links firstly. After that a problem with minimizing the sum of MDs' weighted delays is formulated to attain a goal of joint MD association and resource allocation under limited resources. Considering that the MD association and resource allocation parameters are coupling in such a formulated problem, we develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. To ensure that the designed algorithm begins from a feasible initial solution, we develop an initiation algorithm according to the conventional best channel association, which is used for comparison and the input of coalition game in the simulation. Simulation results show that the algorithm designed for minimizing the sum of MDs' weighted delays may achieve a better performance than the initiation (best channel association) algorithm in general.

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Joint multi-user DNN partitioning and task offloading in mobile edge computing

Liao

Huang

et al. 2023

Ad Hoc Networks

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Two-Tier Matching Game in Small Cell Networks for Mobile Edge Computing

Cited by 26 publications

References 33 publications

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Mobile Device Association and Resource Allocation in Small-Cell IoT Networks with Mobile Edge Computing and Caching

Joint multi-user DNN partitioning and task offloading in mobile edge computing

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