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Performance predict...
Performance prediction in dynamic clouds using transfer learning
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- Moradi, F. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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- Stadler, Rolf (författare)
- KTH,RISE,SICS,Nätverk och systemteknik,Swedish Inst Comp Sci RISE SICS, Stockholm, Sweden.
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- Johnsson, A. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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Ericsson Research, Sweden Ericsson Res, Stockholm, Sweden (creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2019
- 2019
- Engelska.
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Ingår i: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9783903176157 ; , s. 242-250
- Relaterad länk:
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Abstract
Ämnesord
Stäng
- Learning a performance model for a cloud service is challenging since its operational environment changes during execution, which requires re-training of the model in order to maintain prediction accuracy. Training a new model from scratch generally involves extensive new measurements and often generates a data-collection overhead that negatively affects the service performance.In this paper, we investigate an approach for re-training neural-network models, which is based on transfer learning. Under this approach, a limited number of neural-network layers are re-trained while others remain unchanged. We study the accuracy of the re-trained model and the efficiency of the method with respect to the number of re-trained layers and the number of new measurements. The evaluation is performed using traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We study model re-training after changes in load pattern, infrastructure configuration, service configuration, and target metric. We find that our method significantly reduces the number of new measurements required to compute a new model after a change. The reduction exceeds an order of magnitude in most cases.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- Machine Learning
- Neural Networks
- Performance Prediction
- Service Management
- Transfer Learning
- Forecasting
- Learning systems
- Video on demand
- Neural network model
- Operational environments
- Prediction accuracy
- Service configuration
- Video on demand services
- Network layers
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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