Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Sean Stijven and Ruben Bossche and 
                 Ekaterina Vladislavleva and Kurt Vanmechelen and 
                 Jan Broeckhove and Mark Kotanchek",
  title =        "Optimizing a Cloud Contract Portfolio Using Genetic
                 Programming-Based Load Models",
  booktitle =    "Genetic Programming Theory and Practice XI",
  year =         "2013",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Jason H. Moore and Mark Kotanchek",
  publisher =    "Springer",
  chapter =      "3",
  pages =        "47--63",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Cloud
                 computing, Symbolic regression, Time series, Load
                 prediction, Variable selection, Forecasting",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_3",
  abstract =     "infrastructure-as-a-Service (IaaS) cloud providers
                 offer a number of different tariff structures. The user
                 has to balance the flexibility of the often quoted
                 pay-by-the-hour, fixed price (on demand) model against
                 the lower-cost-per-hour rate of a reserved contract.
                 These tariff structures offer a significantly reduced
                 cost per server hour (up to 50percent), in exchange for
                 an up-front payment by the consumer. In order to reduce
                 costs using these reserved contracts, a user has to
                 make an estimation of its future compute demands, and
                 purchase reserved contracts accordingly. The key to
                 optimising these cost benefits is to have an accurate
                 model of the customer's future compute load, where that
                 load can have a variety of trends and cyclic behaviour
                 on multiple time scales. In this chapter, we use
                 genetic programming to develop load models for a number
                 of large-scale web sites based on real-world data. The
                 predicted future load is subsequently used by a
                 resource manager to optimise the amount of IaaS servers
                 a consumer should allocate at a cloud provider, and the
                 optimal tariff plans (from a cost perspective) for that
                 allocation. Our results illustrate the benefits of load
                 forecasting for cost-efficient IaaS portfolio
                 selection. They also might be of interest for the
                 Genetic Programming (GP) community as a demonstration
                 that GP symbolic regression can be successfully used
                 for modelling discrete time series and has a tremendous
                 potential for time lag identification and model
                 structure discovery.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2013:GPTP} published after the
                 workshop in 2013",

Genetic Programming entries for Sean Stijven Ruben Van den Bossche Ekaterina (Katya) Vladislavleva Kurt Vanmechelen Jan Broeckhove Mark Kotanchek