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Data-Driven End-to-...
Data-Driven End-to-End Delay Violation Probability Prediction with Extreme Value Mixture Models
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- Mostafavi, Seyed Samie (författare)
- KTH,Teknisk informationsvetenskap
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- Dán, György (författare)
- KTH,Nätverk och systemteknik
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- Gross, James, Professor, 1975- (författare)
- KTH,Teknisk informationsvetenskap
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2021
- 2021
- Engelska.
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Ingår i: 6th ACM/IEEE Symposium on Edge Computing, SEC 2021. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 416-422
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- With the advent of edge computing, there is increasing interest in wireless latency-critical services. Such applications require the end-to-end delay of the network infrastructure (communication and computation) to be less than a target delay with a certain probability, e.g., 10(-2)-10(-5). To deal with this guarantee level, the first step is to predict the transient delay violation probability (DVP) of the packets traversing the network. The guarantee level puts a threshold on the tail of the end-to-end delay distribution; thus, it makes data-driven DVP prediction a challenging task. We propose to use the extreme value mixture model in the mixture density network (MDN) method for this task. We implemented it in a multi-hop queuing-theoretic system to predict the DVP of each packet from the network state variables. This work is a first step toward utilizing the DVP predictions, possibly in the resource allocation scheme or queuing discipline. Numerically, we show that our proposed approach outperforms state-of-the-art Gaussian mixture model-based predictors by orders of magnitude, in particular for scenarios with guarantee levels above 10(-2).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Nyckelord
- edge computing
- delay violation probability
- time sensitive networks
- extreme value mixture models
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)