Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

Raj, Suman; Gupta, Harshil; Simmhan, Yogesh

doi:10.1109/ccgrid57682.2023.00063

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid) 2023

DOI: 10.1109/ccgrid57682.2023.00063

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Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

Suman Raj

Harshil Gupta

Yogesh Simmhan

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Cited by 3 publications

References 30 publications

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Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Da Silva Barros,

Giroire,

Aparicio-Pardo

et al. 2024

2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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With the advent and the growing usage of Machine Learning as a Service (MLaaS), cloud and network systems are now offering the possibility to deploy ML tasks on heterogeneous clusters. Then, network and cloud operators have to schedule these tasks, determining both when and on which devices to execute them. In parallel, several solutions, such as neural network compression, were proposed to build small models which can run on limited hardware. These solutions allow choosing the model size at inference time for any targeted processing time without having to re-train the network.In this work, we consider the Deadline Scheduling with Compressible Tasks (DSCT) problem: a novel scheduling problem with task deadlines where the tasks can be compressed. Each task can be executed with a certain compression, presenting a trade-off between its compression level (and, its processing time) and its obtained utility. The objective is to maximize the tasks utilities. We propose an approximation algorithm with proved guarantees to solve the problem. We validate its efficiency with extensive simulation, obtaining near optimal results. As application scenario, we study the problem when the tasks are Deep Learning classification jobs, and the objective is to maximize their global accuracy, but we believe that this new framework and solutions apply to a wide range of application cases.

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Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Da Silva Barros,

Giroire,

Aparicio-Pardo

et al. 2024

2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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show abstract

Towards a Mobile App Platform for Personalized UAV Fleets using Edge and Cloud

Raj

Simmhan

2023

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)

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Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget

Da Silva Barros,

Ferre,

Giroire

et al. 2024

Proceedings of the 53rd International Conference on Parallel Processing

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Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

Cited by 3 publications

References 30 publications

Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Towards a Mobile App Platform for Personalized UAV Fleets using Edge and Cloud

Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget

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