Cloudlets Activation Scheme for Scalable Mobile Edge Computing with Transmission Power Control and Virtual Machine Migration

Rodrigues, Tiago Gama; Suto, Katsuya; Nishiyama, Hiroki; Kato, Nei; Temma, Katsuhiro

doi:10.1109/tc.2018.2818144

Cited by 169 publications

(43 citation statements)

References 43 publications

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“…However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limitation and quality threshold. Thus, the edge servers activation scheme for scalable MEC is proposed in [27]. Furthermore, the UAV can play an important role in the mobile edge computing system.…”

Section: Related Workmentioning

confidence: 99%

“…To make P1 more tractable and considering minimizing the serving time of each IoTD, we transform and relax the original P1 into problem (27) as follows…”

Section: A Problem Transformation and Wpt Duration Optimizationmentioning

confidence: 99%

“…The object function of problem (27) consists of three independent parts, including the computing energy, the wireless powering energy and the hovering energy. Therefore, problem (27) can be decomposed into N × M independent sub-problems of the wireless powering durations τ. The object function (27a) is minimal when the equality of the wireless powering energy constraint holds for (15), thus we prove Theorem 2.…”

Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Yang

Wang

et al. 2019

IEEE Trans. Veh. Technol.

205

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This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer nonconvex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems.Index Terms-Internet of things, unmanned aerial vehicle (UAV), mobile edge computing (MEC), wireless power transfer (WPT), resources allocation, flow-shop scheduling.

show abstract

Section: Related Workmentioning

confidence: 99%

“…To make P1 more tractable and considering minimizing the serving time of each IoTD, we transform and relax the original P1 into problem (27) as follows…”

Section: A Problem Transformation and Wpt Duration Optimizationmentioning

confidence: 99%

Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Yang

Wang

et al. 2019

IEEE Trans. Veh. Technol.

205

View full text Add to dashboard Cite

show abstract

“…These features of edge computing structure can largely alleviate the aforementioned problem. Several instructive examples have been presented [17]- [25]. Bilal and Erbad [26] present an approach to improve users' experience of video generation and streaming with mobile edge computing.…”

Section: B Opportunities In Edge Computingmentioning

confidence: 99%

Human in the Loop: Distributed Deep Model for Mobile Crowdsensing

Ota

Dong

2018

IEEE Internet Things J.

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With the proliferation of mobile devices, crowdsensing has become an appealing technique to collect and process big data. Meanwhile, the rise of 5th generation wireless systems (5G), especially the new cellular base stations with computing ability, brings about the revolutionary edge computing. Although many approaches regarding the mobile crowdsensing have emerged in the last few years, very few of them are focused on the combination of edge computing and crowdsensing. In the paper, we adopt the state-of-the-art edge computing method to solve the crowdsensing problem with the real-time sensing data, and more importantly, make human be in the loop again, in order to respect the users' willing and privacy. A distributed deep learning model is adopted to extract features from the captured data, which is not only a compression process to reduce the communication cost, but an encryption procedure for safety protection. The proposed model enables the crowdsensing system to fully harness the computing capacity of edge nodes and devices, and obtain a strong data analysis ability to process the captured data. Simulations demonstrate that our approach is robust and efficient, and outperforms other strategies in several related tasks.

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“…Minimizing the execution delay is studied in [16][17][18][19]. Under the constraint of execution delay, [20][21][22][23] minimized the energy consumption and [24] maximized the system scalability. A balance between execution delay and energy consumption for computation offloading was considered in [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

Kuang

Gong

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

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Cloudlets Activation Scheme for Scalable Mobile Edge Computing with Transmission Power Control and Virtual Machine Migration

Cited by 169 publications

References 43 publications

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Human in the Loop: Distributed Deep Model for Mobile Crowdsensing

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

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