Bandwidth Gain From Mobile Edge Computing and Caching in Wireless Multicast Systems

Sun, Yaping; Chen, Zhiyong; Tao, Meixia; Liu, Hui

doi:10.1109/twc.2020.2979147

Cited by 54 publications

(26 citation statements)

References 42 publications

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“…We remark here that the proposed EARA algorithm allows for enhancing the hierarchical FL performance by enabling the EUs to have two configuration options: Single Connectivity Allocation (SCA), and Dual Connectivity Allocation (DCA). The former refers to assigning each EU to only one edge, while the later refers to the possibility of an EU to be connected to one or two edge nodes simultaneously, leveraging Dual Connectivity (DC) 2 and multicast transmission [34], [35]. We remark that DC is one of the substantial technologies adopted by 5G to increase the network throughput and mobility robustness by enabling the users to simultaneously connect with macro and small-cell base stations [36].…”

Section: Proposed Solutionmentioning

confidence: 99%

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Abdellatif¹,

Mhaisen²,

Mohamed³

et al. 2021

Preprint

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Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in IoT heterogeneous systems and propose an optimized solution for user assignment and resource allocation on multiple edge nodes. In particular, this work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. In particular, our numerical results highlight the effectiveness of our approach and its ability to provide 4-6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75-85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

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Section: Proposed Solutionmentioning

confidence: 99%

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Abdellatif¹,

Mhaisen²,

Mohamed³

et al. 2021

Preprint

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“…Caching decisions and transmission (offloading and downloading) time duration were then jointly optimized to minimize the expected energy cost therein. In addition, [15] assumed that the edge server has the input and the output data of all the computation tasks in a multi-user MEC system. Accordingly, it jointly optimized the local caching decisions of task input and/or output data and computing mode of mobile devices to minimize the transmission bandwidth.…”

Section: A Related Workmentioning

confidence: 99%

“…Service caching refers to fetching a priori task input data, program files or task results of frequently demanded computation services at edge servers or mobile devices, thus alleviating transmission and execution burden for (partially) repeated request in the future. As a result, service caching further unleashes potential of MEC in terms of energy efficiency and low latency [12][13][14][15][16][17][18]. Note that content-oriented caching has been a well-investigated topic aimed for improving user-perceived quality of experience by reducing network congestion, especially for video content delivery (see references [19,20] and therein).…”

Section: Introductionmentioning

confidence: 99%

“…However, there are several different aspects lying between content-oriented and computation-oriented caching. i) Compared with content-oriented caching that mainly fetches data over backhaul, service caching takes place in the shared wireless medium, and is thus more vulnerable to channel hostilities such as channel noise, fading and mutual interference [14,15]. ii) Computation-oriented tasks are usually contextaware and customized to real-time data generated locally at mobile devices, and therefore the validity of task input/output data and/or program files may last relatively shorter than the content, incurring significant overhead due to service-caching redeployment [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing

Chen¹,

Xing²,

Lin³

et al. 2021

Preprint

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With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, mobile edge computing (MEC) has emerged as a promising technique to reinforce the computation capability of the resource-constrained mobile devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to (partially) reuse the computation tasks (e.g., input/output data and program files etc.), thus effectively reducing the delay incurred by data retransmissions and/or the computation burden due to repeated execution of the same task. In a multiuser cache-assisted MEC system, designs for service caching depend on users' preference for different types of services, which is at times highly correlated to the locations where the requests are made. In this paper, we exploit users' location-dependent service preference profiles to formulate a cache placement optimization problem in a multiuser MEC system. Specifically, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. In a frequency-division multiple access (FDMA) setup, we jointly optimize the binary cache placement, edge computation resources and bandwidth allocation to minimize the expected weighted-sum energy of the edge server and the users with respect to the users' preference profile, subject to the bandwidth and the computation limitations, and the latency constraints. To effectively solve the mixed-integer non-convex problem, we propose a deep learning based offline cache placement scheme using a novel stochastic quantization based discrete-action generation method. In special cases, we also attain suboptimal caching decisions with low complexity leveraging the structure of the optimal solution. The simulations verify the performance of the proposed scheme and the effectiveness of service caching in general.

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“…In [10], the authors proposed the joint optimization of caching and computation in a multiuser cache-assisted MEC system, where MEC servers cache computation results for future demands. Edge computing, caching and multicast were considered in [11], which can tackle the wireless bandwidth bottleneck problem. However, the main concern of computation results caching and task offloading on above MEC system is computing and caching capabilities of the edge servers, they do not consider how to use limited spectrum resources to further reduce latency.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Caching and Computation in Multi-Server NOMA-MEC System via Reinforcement Learning

Zhao

2020

IEEE Access

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With the development of emerging applications such as augmented reality, more and more computing tasks are sensitive to delay. Caching popular task computation results on the mobile edge computing (MEC) server is an effective solution to meet the latency requirements. When multiple users request the same task, if the computation result is cached on the MEC server, it will return the computation result directly to the user to reduce the delay for repeated computation. In this paper, we use the caching to assist the calculation. Non-orthogonal multiple access (NOMA) is used to further reduce the delay for computation offloading. The optimization problem is formulated as how to make caching and offloading decision to minimize the delay of whole system. In the case of unknown popularity, we use Gated Recurrent Unit (GRU) algorithm to predict the task popularity in time-varying system, and place the computing results of tasks with high popularity on the corresponding server. Based on the predicted popularity, a multi-agent Deep-Q-network (MADQN) algorithm is used to solve the caching and offloading problem. The simulation results show that the prediction error of GRU algorithm can be reduced by increasing the learning rate. Meanwhile, the proposed MADQN can effectively reduce the delay compared with other methods. INDEX TERMS mobile edge computing (MEC), non-orthogonal multiple access (NOMA), caching, multiagent Deep-Q-network (MADQN)

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Bandwidth Gain From Mobile Edge Computing and Caching in Wireless Multicast Systems

Cited by 54 publications

References 42 publications

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing

Joint Optimization of Caching and Computation in Multi-Server NOMA-MEC System via Reinforcement Learning

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