Virtual resource allocation for heterogeneous services in full duplex-enabled small cell networks with cache and MEC

Tan, Zhiyuan; Yu, F. Richard; Li, Xi; Ji, Hong; Leung, Victor C. M.

doi:10.1109/infcomw.2017.8116370

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…The scheme still belongs to the processing in the centralized manner, and the solution is based on the heuristics, where as in our study, the proposed algorithm is in the distributed manner and on the basis of deterministic optimization theory. Tan et al [24], [25] proposed an offloading strategy for the scenario of multiple users, multiple MEC servers, and multiple MVNOs in SCNs. To reduce the complexity, ADMM algorithm is adopted to solve the formulated mixed integer programming in the distributed manner.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce the complexity, ADMM algorithm is adopted to solve the formulated mixed integer programming in the distributed manner. Differently, the optimization objective in [24], [25] is to maximize the difference between user rate and operating costs, where as our study mainly focus on the minimizing energy consumption of the UEs. In [26], [27], Guo et al also investigated the energy consumption of the users in SCNs with MEC, and formulated the offloading decision, computation chip, and wireless channel allocation jointly as a MINLP.…”

Section: Related Workmentioning

confidence: 99%

“…Note that objective function (24) and the feasible set are separable for each MVNO, as well as the integer constraints (7), (10), (11). Therefore, problem (24) is decomposed into multiple subproblems solved by each MVNO individually to reduce complexity.…”

Section: B Solving (A Uj Y Uj ) Under Fixed Transmission Power Pmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing

Cheng

Zhang

Yang

et al. 2020

IEEE Trans. on Green Commun. Netw.

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Virtualized small cell networks (SCNs) integrated with mobile edge computing (MEC) is a promising paradigm to provide both wideband access and intensive computation economically for user equipments (UEs) in the scenario of multiple mobile virtual network operators and infrastructure providers. However, the model of offloading in this case is often of high complexity and lacks effective solution. In this paper, by jointly considering offloading, time slice and power allocation, we formulate the energy consumption reduction of the UEs in virtualized SCNs with MEC as a mixed integer nonlinear programming. Our aim is to minimize the total energy consumption of the UEs subject to minimum overall throughput of the network. To solve the problem efficiently, we convert it into a biconvex problem by adding auxiliary variable, which enables the derivation of an efficient iterative algorithm by two subproblems. Towards the first subproblem, we introduce local variables to handle the coupling constraint, and propose an alternating direction method of multipliers (ADMM)-based distributed algorithm, where the closed-form expressions of the optimal solutions in variables updating are derived. For the second subproblem, the closedform expressions of the optimal solution is also derived. Finally, the effectiveness of the proposed algorithm is demonstrated by extensive simulations with different system configurations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing

Cheng

Zhang

Yang

et al. 2020

IEEE Trans. on Green Commun. Netw.

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show abstract

“…Nowadays, the advancements in wireless technologies and the popularity of smart devices have spawned various emerging applications and heterogeneous services [1][2][3]. Those new applications and services put forward higher requirements on data rate and computational capabilities, and at the same time bring explosive mobile traffic, which pose a severe challenge to the construction of the next generation mobile network [4][5].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we consider hybrid energy supply pattern and consider two typical heterogeneous services in MEC network: content delivery service and computing service similar to work [2] and [23]. By sensing the available green energy status of base stations and distributing more load to base stations rich in green energy, our goal is to fully utilize renewable energy to minimize the on-grid power consumption of system with QoS guarantees.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning

2021

KSII TIIS

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The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

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Deep reinforcement learning-based computation offloading and resource allocation in security-aware mobile edge computing

Wang

Zhao

et al. 2021

Wireless Netw

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Virtual resource allocation for heterogeneous services in full duplex-enabled small cell networks with cache and MEC

Cited by 8 publications

References 25 publications

Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing

Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing

Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning

Deep reinforcement learning-based computation offloading and resource allocation in security-aware mobile edge computing

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