TailCutter: Wisely Cutting Tail Latency in Cloud CDNs Under Cost Constraints

Cui, Yong; Dai, Ningwei; Lai, Zeqi; Li, Minming; Li, Zhenhua; Hu, Yuming; Ren, Kui; Chen, Yuchi

doi:10.1109/tnet.2019.2926142

Cited by 18 publications

(7 citation statements)

References 52 publications

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“…Cui et al [31] identified the high tail latency problem in cloud CDN via analyzing a large-scale dataset collected from 783,944 users in a major cloud CDN. A workload scheduling mechanism was presented aiming to optimize the tail latency while meeting the cost constraint given by application providers.…”

Section: Cost-effective Cloud Computingmentioning

confidence: 99%

Cost-effective land cover classification for remote sensing images

Li¹,

Wang²,

Gao³

et al. 2022

J Cloud Comp

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Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.

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Section: Cost-effective Cloud Computingmentioning

confidence: 99%

Cost-effective land cover classification for remote sensing images

Li¹,

Wang²,

Gao³

et al. 2022

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…Cui et al [22] identified the high tail latency problem in cloud CDN via analyzing a large-scale dataset collected from 783,944 users in a major cloud CDN. A workload scheduling mechanism was presented aiming to optimize the tail latency while meeting the cost constraint given by application providers.…”

Section: Cost-effective Cloud Computingmentioning

confidence: 99%

Cost-effective Land Cover Classification for Remote Sensing Images

Li,

Wang,

et al. 2021

Preprint

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Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving the cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $721,580.46 for the government in each single-use when the desired accuracy is 90%.

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“…At last, numerical results are given to show how successful the solution is. In order to identify the source of the high tail delay problem in cloud CDNs, the authors of the aforementioned article [12] examine a massive dataset acquired from a well-known cloud CDN supplier and compare it to benchmark data collected on Amazon's Web Services and Microsoft Azure. The efficacy of cloud CDNs can be drastically diminished due to this issue.…”

Section: Related Workmentioning

confidence: 99%

Multiple data centers intended for latency minimization using artificial intelligence algorithms

Yenugula,

Kodam,

2020

Int. J. Comput. Artif. Intell.

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Lightweight deep learning data and algorithms center technology have recently advanced to the point where multiple inferences of models tasks can be executed simultaneously on limited data center resources. This allows us to work together towards a common goal instead of focusing on achieving high quality in each individual task. On the other hand, real-time applications are never good with multi-model inferences because of the high total operating latency. For multi-model deployment, algorithms should be fine-tuned to reduce latency as much as possible without jeopardizing safetycritical scenarios. In this study, we employ an open neural networks exchange (ONNX) execution engine to investigate model inference and develop a real-time job scheduling technique for deploying multiple models. Afterwards, a container-based application deployment approach is suggested, and inference jobs are allocated to various containers according to the scheduling techniques. The suggested technique may drastically cut down on total running delay in real-time applications, according to the experimental findings.

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TailCutter: Wisely Cutting Tail Latency in Cloud CDNs Under Cost Constraints

Cited by 18 publications

References 52 publications

Cost-effective land cover classification for remote sensing images

Cost-effective land cover classification for remote sensing images

Cost-effective Land Cover Classification for Remote Sensing Images

Multiple data centers intended for latency minimization using artificial intelligence algorithms

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