A transfer learning-aided decision support system for multi-cloud brokers
Creators: name: Samreen, Faiza
Publisher: name: Lancaster University
Year: 2017
Subjects: Cloud Computing
Cloud Broker
Decision Support System (DSS)
Machine Learning
Transfer Learning

description: Decision-making in a cloud environment is a formidable task due to the
      proliferation of service offerings, pricing models, and technology
      standards. A customer entering the diverse cloud market is likely to be
      overwhelmed with a host of difficult choices in terms of service
      selection. This applies to all levels of service, but Infrastructure as a
      Service (IaaS) level is particularly important for the end user given the
      fact that IaaS provides more choices and control for application
      developers. In the IaaS domain, however, there is no straightforward
      method to compare virtual machine performance and, more generally
      cost/performance trade-offs, within or across cloud providers. A wrong
      decision can result in a financial loss as well as a reduced application
      performance. A cloud broker can help in resolving such issues by acting as
      an intermediary between the cloud provider and the cloud consumer – hence,
      serving as a decision support system for assisting the customer in the
      decision process.
      
      
      In this thesis, we exploit machine learning for building an intelligent
      decision support system which assists customers in making
      application-driven decisions in a multi-cloud environment. The thesis
      examines a representative set of appropriate inference and prediction
      based learning techniques, that are essential for capturing application
      behaviour on different deployment setups, such as Polynomial Regression
      and Support Vector Regression (SVR). In addition, the thesis examines the
      efficiency of the learning techniques, recognising that machine learning
      can impose significant training overhead. The thesis also introduces a
      novel transfer learning aided technique, leading to substantial reduction
      in this overhead. By definition, transfer learning aims to solve the new
      problem faster or with a better solution by using the previously learned
      knowledge. Quantitatively, we observed a reduction of approximately 60% in
      the learning time and cost by transferring the existing knowledge about
      the application and cloud platform in order to learn a new prediction
      model for some other application or cloud provider. Intensive
      experimentation has been performed in this study for learning and
      evaluation of proposed decision support system. Explicitly, we have used
      three different representative applications over two cloud providers,
      namely Amazon and Google. Our proposed decision support system, enriched
      with transfer learning methods, is capable of generating decisions that
      are viable across different applications in a multi-cloud environment.
      Finally, we also discuss lessons learned in terms of architectural
      principles and techniques for intelligent multi-cloud brokerage.