Cloud-aware power control for cloud-enabled small cells

Mach, Pavel; Bečvář, Zdeněk

doi:10.1109/glocomw.2014.7063570

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…In case when the UEs' mobility is low and limited, e.g., when the UEs are slowly moving inside a building, a proper setting of the transmission power of the serving and/or neighboring SCeNBs can help to guarantee QoS. This is considered in [108], where the authors propose a cloud-aware power control (CaPC) algorithm helping to manage the offloading of real-time applications with strict delay requirements. The main objective of the CaPC is to maximize the amount of the offloaded applications processed by the MEC with a given latency constrain.…”

Section: A Power Controlmentioning

confidence: 99%

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Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

Self Cite

2,698

1,204

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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: A Power Controlmentioning

confidence: 99%

“…The main disadvantage of the CaPC presented in [109] is that the time when the fine adjustment of the transmission Fig. 19: Principle of CaPC according to [108] [109].…”

Section: A Power Controlmentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

Self Cite

2,698

1,204

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“…In [29], authors presented a user-oriented use case of MEC from the perspective of computational offloading and mobility management. They first discuss the power control approach where the mobility management entity regulates the transmission power of the eNB/SCeNB, which is mostly used in scenarios where the UEs mobility is confined within a given space such as an office room [29], [137], [138]. The principle of this approach is depicted in Figure 4.…”

Section: General Iotmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

649

328

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The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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“…In , we have proposed cloud‐aware power control (CaPC) algorithm guaranteeing that the mobile SUEs are able to receive offloaded tasks computed in the cloud within tolerable delay to reduce the amount of lost delay‐sensitive offloaded tasks. The CaPC is composed of coarse and fine settings of transmission power.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of our former work, and the novelty is summarized as follows: We enhance the CaPC by new feature that adaptively sets the time instant when the CaPC is triggered. This reduces interference among the SCeNBs, and it also reduces additional delay of delay sensitive offloaded tasks that are delivered back to the SUE. The CaPC is thoroughly described and triggering of the CaPC is extended for consideration of the SINR level at the time when the CaPC is initiated. Performance evaluation is significantly extended.…”

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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Cloud-aware power control for cloud-enabled small cells

Cited by 13 publications

References 10 publications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Survey on Multi-Access Edge Computing for Internet of Things Realization

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

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