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

Mach, Pavel; Bečvář, Zdeněk

doi:10.1002/ett.3009

Cited by 32 publications

(19 citation statements)

References 22 publications

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“…Enhancing the high density SBS with computing capabilities is a much more feasible way. The authors in [56] propose a distributed cloud-aware power control algorithm that is suitable for delay sensitive applications. In [57], a delay minimization problem is formulated under the constraint of power consumption.…”

Section: A Objectivesmentioning

confidence: 99%

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

et al. 2017

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As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.Index Terms-Mobile edge computing, mobile edge caching, D2D, SDN, NFV, content delivery, computational offloading.

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Section: A Objectivesmentioning

confidence: 99%

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

et al. 2017

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“…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%

“…• There are several options of the UE's mobility management if the data/application is offloaded to the MEC. In cases of the low mobility, the power control at the SCeNBs/eNBs side can be sufficient to handle mobility (up to 98% of offloaded applications can be successfully delivered back to the UE [109]). This is true as long as the adaption of transmission power enables keeping the UE at the same serving station during the computation offloading.…”

Section: Lessons Learnedmentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

2,695

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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“…The authors of [116] proposed an algorithm for the realtime application offloading in mobile edge computing. The real-time computation demanding of the UE is challenging because the UEs operate with limited battery power.…”

Section: ) Capcmentioning

confidence: 99%

“…The simultaneous communication of multiple users with the MEC servers could cause congestion, hence degrading the reliability of the whole network. Algorithms [111]- [116] having the high offloading capability, as presented in Fig. 11 that minimizes the offloading issue.…”

Section: Offloading Awarenessmentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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

Cited by 32 publications

References 22 publications

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

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