Many-to-Many Matching with Externalities for Device-to-Device Communications

Zhao, Jingjing; Liu, Yuanwei; Chai, Kok Keong; Chen, Yue; Elkashlan, Maged

doi:10.1109/lwc.2016.2642099

Cited by 61 publications

(51 citation statements)

References 13 publications

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“…Since the rate is affected by the other UEs (i.e., the players in the same set of the matching game) via interference if they are matched to the same server, the UE's preference also depends on the matching state of the other UEs. The interdependency between the players' preferences is called externalities [35] in which the player's preference dynamically changes with the matching state of the other players in the same set. Thus, the UE's preference over matching states should be adopted.…”

Section: B Ue-server Association Using Many-to-one Matching With Extmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Liu

Bennis

Debbah

et al. 2019

IEEE Trans. Commun.

341

183

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To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation.However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource ). 2 allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to baselines. Index Terms 5G and beyond, mobile edge computing (MEC), fog networking and computing, ultra-reliable low latency communications (URLLC), extreme value theory. I. INTRODUCTION Motivated by the surging traffic demands spurred by online video and Internet-of-things (IoT) applications, including machine type and mission-critical communication (e.g., augmented/virtual reality (AR/VR) and drones), mobile edge computing (MEC)/fog computing are emerging technologies that distribute computations, communication, control, and storage at the network edge [2]-[6]. When executing the computation-intensive applications at mobile devices, the performance and user's quality of experience are significantly affected by the device's limited computation capability. Additionally, intensive computations are energy-consuming which severely shortens the lifetime of battery-limited devices. To address the computation and energy issues, mobile devices can wirelessly offload their tasks to proximal MEC servers. On the other hand, offloading tasks incurs additional latency which cannot be overlooked and should be taken into account in the system design. Hence, the energy-delay tradeoff has received significant attention and has been studied in various MEC systems [7]-[22]. A. Related WorkIn [7], Kwak et al. focused on an energy minimization problem for local computation and task offloading in a single-user MEC system. The authors further studied a multi-user system, which takes into account both the energy cost and monetary cost of task offloading [8]. Therein, the cost-delay tradeoff was investigated in terms of competition and coo...

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Section: B Ue-server Association Using Many-to-one Matching With Extmentioning

confidence: 99%

“…The third decomposed problem P3 given in (35) is addressed at the server side, where each MEC server j ∈ S solves…”

Section: Computational Resource Scheduling At the Server Sidementioning

confidence: 99%

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Liu

Bennis

Debbah

et al. 2019

IEEE Trans. Commun.

341

183

View full text Add to dashboard Cite

show abstract

“…To address this issue, the following leverages the concept of two-side exchange stability [38], which assumes that no user node can remain unmatched, thereby allowing to swap user tasks to fog nodes. Some preliminary definitions are introduced next, following the notation in [20].…”

Section: A Matching Games With Externalitiesmentioning

confidence: 99%

“…Matching theory is a mathematical framework that has originated in economics to describe interactions between two sets of agents with preferences on each other [18]. It has also been applied to wireless networks [19], including for resource allocation in several contexts ranging from device-to-device communications and 5G to the Internet of Things [20][21][22][23]. However, to the best of the authors' knowledge, it has not been employed for task offloading in DNN inference acceleration.…”

Section: Introductionmentioning

confidence: 99%

Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Mohammed

Joe‐Wong

Babbar

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

135

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Deep neural networks (DNN) are the de-facto solution behind many intelligent applications of today, ranging from machine translation to autonomous driving. DNNs are accurate but resource-intensive, especially for embedded devices such as mobile phones and smart objects in the Internet of Things. To overcome the related resource constraints, DNN inference is generally offloaded to the edge or to the cloud. This is accomplished by partitioning the DNN and distributing computations at the two different ends. However, most of existing solutions simply split the DNN into two parts, one running locally or at the edge, and the other one in the cloud. In contrast, this article proposes a technique to divide a DNN in multiple partitions that can be processed locally by end devices or offloaded to one or multiple powerful nodes, such as in fog networks. The proposed scheme includes both an adaptive DNN partitioning scheme and a distributed algorithm to offload computations based on a matching game approach. Results obtained by using a selfdriving car dataset and several DNN benchmarks show that the proposed solution significantly reduces the total latency for DNN inference compared to other distributed approaches and is 2.6 to 4.2 times faster than the state of the art. Index Terms-DNN inference, task partitioning, task offloading, distributed algorithm, matching game.

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“…In order to achieve a higher quality cellular network, a key short-distance technology with enormous potential termed device-to-device (D2D) has kindled the interest of academia [23]. To be more specific, D2D networks enable direct links between proximal devices without the aid of cellular networks [24]. When comparing with the traditional architecture in 4G networks, the received power at the intended D2D receiver (D2D-Rx) is typically much higher due to the shorter link distance [25].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of D2D Millimeter-Wave Networks With Poisson Cluster Processes

Liu

Nallanathan

2017

IEEE Trans. Commun.

Self Cite

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This paper investigates the performance of millimeter wave (mmWave) communications in clustered device-todevice (D2D) networks. The locations of D2D transceivers are modeled as a Poisson Cluster Process (PCP). In each cluster, devices are equipped with multiple antennas, and the active D2D transmitter (D2D-Tx) utilizes mmWave to serve one of the proximate D2D receivers (D2D-Rxs). Specifically, we introduce three user association strategies: 1) Uniformly distributed D2D-Tx model; 2) Nearest D2D-Tx model; 3) Closest line-ofsite (LOS) D2D-Tx model. To characterize the performance of the considered scenarios, we derive new analytical expressions for the coverage probability and area spectral efficiency (ASE). Additionally, in order to efficiently illustrating the general trends of our system, a closed-form lower bound for the special case interfered by intra-cluster LOS links is derived. We provide Monte Carlo simulations to corroborate the theoretical results and show that: 1) The coverage probability is mainly affected by the intra-cluster interference with LOS links; 2) There exists an optimum number of simultaneously active D2D-Txs in each cluster for maximizing ASE; and 3) Closest LOS model outperforms the other two scenarios but at the cost of extra system overhead.

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Many-to-Many Matching with Externalities for Device-to-Device Communications

Cited by 61 publications

References 13 publications

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Modeling and Analysis of D2D Millimeter-Wave Networks With Poisson Cluster Processes

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