Parallel and Distributed Resource Allocation With Minimum Traffic Disruption for Network Virtualization

Nguyen, Hung Khanh; Zhang, Yanru; Chang, Zheng; Han, Zhu

doi:10.1109/tcomm.2017.2650994

Cited by 24 publications

(7 citation statements)

References 40 publications

(62 reference statements)

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“…At the (t+1)-th iteration, global variable {D, u, G, V} can be updated by solving optimization problem P 4 (Nguyen et al, 2017;Prak and Boyd, 2017).…”

Section: Admm-based Parallel Optimization Algorithmmentioning

confidence: 99%

Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

2022

Front. Energy Res.

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Internet of things of cloud computing offers high-performance computing, storage and networking services, but there are still suffers from a high transmission and processing latency, poor scalability and other problems. Internet of things of edge computing can better meet the increasing requirements of electricity consumers for service quality, especially the increasingly stringent need for low delay. On the other hand, edge intelligent network technology can offers edge smart sensing while significantly improve the efficiency of task execution, but it will lead to a massive collaborative task scheduling optimization problem. In order to solve this problem, This paper studies an ubiquitous power internet of things (UPIoT) smart sensing network edge computing model and an improved multi node cluster cooperative scheduling optimization strategy. The cluster server is added to the edge aware computing network, and an improved low delay edge task collaborative scheduling algorithm (LLETCS) is designed by using the vertical cooperation and multi node cluster collaborative computing scheme between edge aware networks. Then the problem is transformed based on linear reconstruction technology, and a parallel optimization framework for solving the problem is proposed. The simulation results suggest that the proposed scheme can more effectively reduce the UPIoT edge computing latency, and improve the quality of service in UPIoT smart sensing networks.

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“…At the (t+1)-th iteration, global variable {D, u, G, V} can be updated by solving optimization problem P 4 (Nguyen et al, 2017;Prak and Boyd, 2017).…”

Section: Admm-based Parallel Optimization Algorithmmentioning

confidence: 99%

Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

2022

Front. Energy Res.

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“…2) ADMM-based solution algorithm: To utilize ADMM to solve the formulated problem, we introduce a set of new variables representing local copies of the global optimal solutions [29] [31]. Firstly, local copy β m of the related global user association factor β is defined for the InP m and β m can be roughly interpreted as the opinion of InP m about the global variable.…”

Section: Proposed Admm-based Solutionmentioning

confidence: 99%

Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network With Hybrid Energy Supply

Chang

Chen

2022

IEEE Trans. Wireless Commun.

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In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the nearoptimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

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“…The global variables fΘ, λ, μ, γg are updated in the ½t + 1-th iteration by settling the following optimization problem [51]:…”

Section: Global Variable Updatementioning

confidence: 99%

Cooperative Offloading in D2D‐Enabled Three‐Tier MEC Networks for IoT

Zhang

Xiao

et al. 2021

Wireless Communications and Mobile Computing

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Mobile/multi-access edge computing (MEC) takes advantage of its proximity to end-users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device-to-device (D2D) cooperation can help to process small-scale delay-sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D-enabled three-tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three-tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed-integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D-enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.

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Parallel and Distributed Resource Allocation With Minimum Traffic Disruption for Network Virtualization

Cited by 24 publications

References 40 publications

Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network With Hybrid Energy Supply

Cooperative Offloading in D2D‐Enabled Three‐Tier MEC Networks for IoT

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