Coding for Heterogeneous UAV-Based Networked Airborne Computing

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 distributed computing systems. Despite such a salient feature, many design challenges and opportunities remain. In this paper, we focus on practical computing systems with heterogeneous computing resources, and design 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. Through formal theoretical analyses, extensive simulation studies, and comprehensive real experiments on the Amazon EC2 computing clusters, we demonstrate promising performance of the proposed BPCC scheme, in terms of high computational efficiency and robustness to uncertain disturbances.

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“…This paper extends our earlier work presented in [24], and further makes the following new contributions.…”

Section: Introductionsupporting

confidence: 74%

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

Wang

Xie

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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“…Nevertheless, there are many design problems, i.e., computing frameworks are vulnerable to uncertain disturbances, such as node failures, communication congestion, and straggler nodes [18]. Only in recent years, coding techniques gained great success in improving the resilience of communication, storage, and cache systems to uncertain system noises [35]. The authors have [36] first presented the used of CDC to speed up matrix multiplication and data shuffling.…”

Section: Coded Distributed Computingmentioning

confidence: 99%

“…subject to: ( 19)-( 22), ( 44)- (56) where O U AV allocation ( t) is the UAV type allocation cost in time slot t and it is defined in (17) in Section IV. O T ask allocation (p t) is the task allocation cost within period p and period p is in time slot t. The task allocation is defined in (35) in Section V-B.…”

Section: E Problem Formulationmentioning

confidence: 99%

Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted Edge Computing

Ng¹,

Lim²,

Xiong³

et al. 2022

Preprint

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Unmanned aerial vehicles (UAVs) have gained wide research interests due to their technological advancement and high mobility. The UAVs are equipped with increasingly advanced capabilities to run computationally intensive applications enabled by machine learning techniques. However, because of both energy and computation constraints, the UAVs face issues hovering in the sky while performing computation due to weather uncertainty. To overcome the computation constraints, the UAVs can partially or fully offload their computation tasks to the edge servers. In ordinary computation offloading operations, the UAVs can retrieve the result from the returned output. Nevertheless, if the UAVs are unable to retrieve the entire result from the edge servers, i.e., straggling edge servers, this operation will fail. In this paper, we propose a coded distributed computing approach for computation offloading to mitigate straggling edge servers. The UAVs can retrieve the returned result when the number of returned copies is greater than or equal to the recovery threshold. There is a shortfall if the returned copies are less than the recovery threshold. To minimize the cost of the network, energy consumption by the UAVs, and prevent over and under subscription of the resources, we devise a two-phase Stochastic Coded Offloading Scheme (SCOS). In the first phase, the appropriate UAVs are allocated to the charging stations amid weather uncertainty. In the second phase, we use the z-stage Stochastic Integer Programming (SIP) to optimize the number of computation subtasks offloaded and computed locally, while taking into account the computation shortfall and demand uncertainty. By using a real dataset, the simulation results show that our proposed scheme is fully dynamic, and minimizes the cost of the network and UAV energy consumption amid stochastic uncertainties.

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“…However, given the limited computational capabilities of devices, the latency involved can still be significant. As such, the studies in [145] and [217] consider the batch-processing based coded computing (BPCC) framework which allows each device to return only the partially completed results to a master node in batches, even before the task is fully completed. These partial results are then utilized to generate approximations to the full solution through the singular value decomposition (SVD) approach [144].…”

Section: B Edge Computingmentioning

confidence: 99%

“…Moreover, they have not been implemented on typical end devices to validate the feasibility of the schemes in practical implementation. For the future works, studies on using CDC schemes in edge computing applications may adopt the approach of [145], [217], in which the schemes are implemented under the context of practical hardware constraints.…”

Section: Summary and Lessons Learnedmentioning

confidence: 99%

A Survey of Coded Distributed Computing

Ng¹,

Lim²,

Luong³

et al. 2020

Preprint

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Distributed computing has become a common approach for large-scale computation of tasks due to benefits such as high reliability, scalability, computation speed, and costeffectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC.

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Coding for Heterogeneous UAV-Based Networked Airborne Computing

Cited by 18 publications

References 22 publications

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

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

Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted Edge Computing

A Survey of Coded Distributed Computing

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