Performance prediction in dynamic clouds using transfer learning

• 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)