Cooperative Computation Offloading for UAVs: A Joint Radio and Computing Resource Allocation Approach

Zhu, Shichao; Gui, Lin; Chen, Jiacheng; Zhang, Qi; Zhang, Ning

doi:10.1109/edge.2018.00017

Cited by 29 publications

(24 citation statements)

References 10 publications

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“…This opportunity is considered in our method. The work in [10] proposes a cooperative method to minimize the energy consumption and task execution latency. The considered method is for Unmanned Aerial Vehicle (UAV) applications that capture photos/videos which are offloaded to an edge server.…”

Section: Related Work and Contributionmentioning

confidence: 99%

On the Optimality of Task Offloading in Mobile Edge Computing Environments

Alghamdi

Anagnostopoulos

Pezaros

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose goal is to enhance the mobile devices' capabilities to face the requirements of new applications. Computation offloading faces the challenges of where and when to offload data to perform computing (analytics) tasks. In this paper, we tackle this problem by adopting the principles of Optimal Stopping Theory contributing with two time-optimized sequential decision making models. A performance evaluation is provided using real world data sets compared with baseline deterministic and stochastic models. The results show that our approach optimizes such decision in single user and competitive users scenarios. Index Terms-Mobile edge computing, tasks offloading, optimal stopping theory, sequential decision making.

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Section: Related Work and Contributionmentioning

confidence: 99%

On the Optimality of Task Offloading in Mobile Edge Computing Environments

Alghamdi

Anagnostopoulos

Pezaros

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…The work in [28] proposes a cooperative mobile edge computing to minimize the energy consumption and task execution latency. The considered use case is for UAV application that captures photos or videos regularly for different tasks such as identifying a certain object or acquiring the traffic condition.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…The offloaded tasks can be computing tasks over offloaded data e.g., image recognition, image processing, data correlation analysis, inferential and predictive analytics [9], statistical learning models building and/or models selection [8], [3]. The mobile node can be a smart phone as in [23], UAVs as considered in [28] or a VN as proposed in [25]. For each server, there is also a transmission delay from the mobile node to the server, which represents the expected time for uploading the data to the MEC server and receive the processed data/analytics results back.…”

Section: A System Modelmentioning

confidence: 99%

“…Based on the OST, our contribution is to determine the best strategy of when to choose the server with the minimum execution delay for minimizing the expected holistic cost when offloading. We believe that the adapted algorithm can be suitable for MEC applications such VN [25], UAV [28] or for data mining applications such activity recognition as in [23] as the mobile nodes roam between a set of MEC servers deployed at the edge of the network.…”

Section: Introductionmentioning

confidence: 99%

“…As the new emerging applications require intensive computation processes, computation offloading provides a solution to overcome the limitation of the mobile device. Examples of applications that can benefit from computing offloading are mobile AR [6], gaming, Internet of Things (IoT) applications, data analytics tasks at the edge [15], VN [25] and Unmanned Aerial Vehicles (UAV) [28] . A study showed the benefit of using offloading for the Percipio AR application on a real MEC testbed [7].…”

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confidence: 99%

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Time-Optimized Task Offloading Decision Making in Mobile Edge Computing

Alghamdi

Anagnostopoulos

Pezaros

2019

2019 Wireless Days (WD)

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Mobile Edge Computing application domains such as vehicular networks, unmanned aerial vehicles, data analytics tasks at the edge and augmented reality have recently emerged. Under such domains, while mobile nodes are moving and have certain tasks to be offloaded to Edge Servers, choosing an appropriate time and an ideally suited server to guarantee the quality of service can be challenging. We tackle the offloading decision making problem by adopting the principles of Optimal Stopping Theory to minimize the execution delay in a sequential decision manner. A performance evaluation is provided by using real data sets compared with the optimal solution. The results show that our approach significantly minimizes the execution delay for task execution and the results are very close to the optimal solution.Index Terms-Mobile edge computing, tasks offloading, optimal stopping theory, sequential decision making.To deal with the disadvantages of MCC, the Mobile Edge Computing (MEC) paradigm has emerged. The rationale architecture of this concept is to offer cloud services closer to mobile devices by placing many data-centres at the edge of the network. The network edge refers to different places e.g., mobile network at the Base Station (BS), or indoor places such Wi-Fi and 3G/4G access points [17].Motivation & Challenge: An essential use case of MEC is the computing task/data offloading. Computation offloading is the task of sending a computation task and data to a remote server for delegating this computation [1]. As the new emerging applications require intensive computation processes, computation offloading provides a solution to overcome the limitation of the mobile device. Examples of applications that can benefit from computing offloading are mobile AR [6], gaming, Internet of Things (IoT) applications, data analytics tasks at the edge [15], VN [25] and Unmanned Aerial Vehicles (UAV) [28] . A study showed the benefit of using offloading for the Percipio AR application on a real MEC testbed [7]. The study showed that the computation offloading reduces latency up to 88% and energy consumption of mobile devices up to 93%.Computation offloading faces several challenges, the most significant ones being: (i) the decision of when to offload tasks/data to a MEC server and (ii) mobility patterns/behaviour of the users in such MEC environments. The decision making of tasks/data offloading is of high importance as it is expected to directly affect the Quality of Service (QoS) of the user application including the inherent latency due to the offloading process. Therefore, different parameters have to be considered when to decide to offload tasks and/or data including: the current MEC server load and the transmission / communication status between the mobile node and the MEC server. The decision can be spatial or temporal as stated in [10]. The spatial decision refers to realising the computing tasks locally at the mobile device, in the cloud, or at the edge server [11]. The temporal decision refers to a situation where it is a...

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