A Seamless Integration of Computationally-Enhanced Base Stations into Mobile Networks towards 5G

Puente, Miguel Angel; Bečvář, Zdeněk; Rohlik, Matej; Lobillo, Felicia; Strinati, Emilio Calvanese

doi:10.1109/vtcspring.2015.7145645

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…2). The SCM may be deployed in a centralized manner either as a standalone SCM located within the RAN, close to a cluster of the SCeNBs, or as an extension to a MME [53] [54]. Moreover, the SCM can be deployed also in a distributed hierarchical manner, where a local SCM (L-SCM) or a virtual L-SCM (VL-SCM) manages the computing and storage resources of the SCeNBs' clusters in vicinity while a remote SCM (R-SCM), located in the CN, has resources of all SCeNBs connected to the CN at its disposal [55] (see Fig.…”

Section: ) Small Cell Cloud (Scc)mentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

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2,512

1,138

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Abstract-Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

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Section: ) Small Cell Cloud (Scc)mentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

Self Cite

2,512

1,138

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“…A number of studies on the computation offloading have been conducted to improve the energy efficiency of energy-constraint devices [11][12][13][14][15][16][17][18][19][20][21][22]. These can be categorized into: (1) framework design [11][12][13][14][15][16]; and (2) algorithm design [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al [14] proposed convergence of cloud and cellular systems, abbreviated as CONCERT, based on a concept of control/data plane decoupling and hierarchically placement of the resources within the network to manage flexibly and elastically networks and cloud services. Puente et al [15] presented a seamless approach for the deployment of edge clouds where conventional mobile traffic and computing related traffic are segregated and handled individually at base stations. However, since these works do not consider the device-to-device offloading, IoT devices with high computing power cannot be exploited efficiently.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Lee

Jang

et al. 2019

Energies

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The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

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“…In the model, we distinguish two types of users; indoor users always connected to the SCeNBs (SUEs) and outdoor users always connected to the eNB (MUEs). The SCeNBs are supposed to be enhanced by additional computational and storage capabilities in order to enable the SCC services . The SCeNBs' computing capabilities can be merged together (by means of virtualization) in order to serve high demanding requests or applications offloaded from the SUEs.…”

Section: System Modelmentioning

confidence: 99%

“…The promising approach addressing both aforementioned problems is to exploit idea of mobile edge computing, which is enabled by a Small Cell‐Cloud (SCC) concept . The basic idea of this approach is to enhance small cells such as microcells, picocells or femtocells with additional computational and storage capabilities . Because the high number of small cells (SCeNBs) is supposed to be deployed in future mobile networks, combined computing strength of the SCeNBs can easily replace the conventional centralized cloud, at least for delay‐sensitive services such as augmented reality, speech and image recognition, or gaming.…”

Section: Introductionmentioning

confidence: 99%

Cloud‐aware power control for real‐time application offloading in mobile edge computing

Mach

Bečvář

2015

Trans. Emerging Tel. Tech.

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Running computationally demanding real‐time applications at the mobile user equipment (UE) is complicated because of limited battery life time of the UEs. One solution is to offload demanding computing tasks to a centralized cloud. Nevertheless, this option introduces significant delay consisting in delivery of the offloaded tasks to the cloud and back. Such delay is inconvenient for real‐time applications. To cope with high delay, a concept of mobile edge computing has been introduced. The feasible way enabling mobile edge computing is to enhance the small cell base stations (SCeNBs) with computing capabilities. However, a high density of the SCeNBs together with even low mobility of users can result in often outage situation or handover when delay sensitive data cannot be delivered from the computing SCeNBs in time and become irrelevant. In this paper, we propose distributed cloud‐aware power control algorithm, which targets to increase the ratio of delivery of computation results to the UE within required delay. Then, we enhance cloud‐aware power control by adaptive algorithm. This algorithm exploits iterative process to find appropriate time when the power control is triggered in order to further improve the performance of the cloud‐aware power control. The simulations demonstrate notable increase in the ratio of delivered offloaded tasks from the computing SCeNBs comparing with competitive schemes. At the same time, the amount of served data to the users exploiting common, non‐cloud, services is increased. As the proposed solution is distributed, related signaling overhead is negligible. Copyright © 2015 John Wiley & Sons, Ltd.

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A Seamless Integration of Computationally-Enhanced Base Stations into Mobile Networks towards 5G

Cited by 16 publications

References 4 publications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Cloud‐aware power control for real‐time application offloading in mobile edge computing

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