Computing in the air: An open airborne computing platform

Wang, Baoqian; Xie, Junfei; Li, Songwei; Wan, Yan; Gu, Yixin; Fu, Shengli; Lu, Kejie

doi:10.1049/iet-com.2019.0515

Cited by 12 publications

(4 citation statements)

References 48 publications

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“…Driven by the urgent need for more advanced UAV platforms of high onboard computing capability, we started to develop the UAV-based networked airborne computing platform [4], [44], [45], which incorporates the computing resources of multiple UAVs through networking and resource sharing. Currently, we have built a prototype that uses NVIDIA Jetson TX2 of high computing power as the computing unit and implements virtualization for enhanced computing and resource management capabilities [45].…”

Section: B Uav-based Airborne Computingmentioning

confidence: 99%

“…Specifically, we first implement three benchmark schemes and two proposed BPCC schemes in the Amazon EC2 computing platform [50], which is a classical cloud computing system. We then evaluate these schemes in a UAV-based airborne computing platform, which is developed in our recent studies [4], [44]- [46].…”

Section: Experiments In Real Computing Systemsmentioning

confidence: 99%

“…To evaluate the performance of BPCC schemes in the edge computing environment, we utilize the UAV-based airborne computing platform developed in our recent studies [4], [44]- [46] to create a heterogeneous computing system. Specifically, in our UAV computing platform [45], we use NVidia Jetson TX2 as the computing unit, which has six CPU cores, we implement Docker to virtualize computing resources, and we use Nanostation LocoM2 to enable broad-band long-distance UAV-to-UAV communications with 70M bps data rate.…”

Section: B Experiments On the Uav-based Airborne Computing Platform 1...mentioning

confidence: 99%

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On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems

Wang

Xie

et al. 2019

Preprint

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In recent years, coded distributed computing (CDC) has attracted significant attention, because it can efficiently facilitate many delay-sensitive computation tasks against unexpected latencies in different distributed computing systems. Despite such a salient feature, there are still many design challenges and opportunities. In this paper, we focus on practical computing systems with heterogeneous computing resources, and propose a novel CDC approach, called batch-processing based coded computing (BPCC), which exploits the fact that every computing node can obtain some coded results before it completes the whole task. To this end, we first describe the main idea of the BPCC framework, and then formulate an optimization problem for BPCC to minimize the task completion time by configuring the computation load and number of batches assigned to each computing node. Based on whether batch-induced overhead can be neglected or not, we develop two BPCC schemes, namely BPCC-1 and BPCC-2, for negligible and linear batching overheads, respectively. Through solid theoretical analyses, extensive simulation studies, and comprehensive real experiments on two heterogeneous distributed computing systems: 1) an Amazon EC2 computing cluster, and 2) an unmanned aerial vehicle (UAV)-based airborne computing platform, we demonstrate the high computational and energy efficiency of the proposed BPCC schemes.

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Section: B Uav-based Airborne Computingmentioning

confidence: 99%

Section: Experiments In Real Computing Systemsmentioning

confidence: 99%

Section: B Experiments On the Uav-based Airborne Computing Platform 1...mentioning

confidence: 99%

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On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems

Wang

Xie

et al. 2019

Preprint

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“…Existing studies have mainly considered smartphones [4] or ground vehicles [5] as the main resources. A few recent studies attempted to move computing resources up to the unmanned aerial vehicles (UAVs) and create a networked airborne computing system [6], [7] that allows UAVs to share resources between each other through direct flight-to-flight communications. This can be considered as one type of MAHC, but studies along this direction are still very limited.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing

Wang¹,

Xie²,

Lu³

et al. 2021

Preprint

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Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However, offloading a computation task from a mobile device to other mobile devices is a challenging task due to frequent topology changes and link failures because of node mobility, unstable and unknown communication environments, and the heterogeneous nature of these devices. To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.

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Advancing Networked Airborne Computing with MmWave for Air-to-Air Communications

Zhang,

Xie,

Wan

et al. 2024

Lecture Notes in Networks and Systems

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Computing in the air: An open airborne computing platform

Cited by 12 publications

References 48 publications

On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems

On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems

Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing

Advancing Networked Airborne Computing with MmWave for Air-to-Air Communications

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