000 05273nam a22005895i 4500
001 978-3-319-73876-5
003 DE-He213
005 20210201191516.0
007 cr nn 008mamaa
008 180210s2018 gw | s |||| 0|eng d
020 _a9783319738765
_9978-3-319-73876-5
050 4 _aQA76.76.A65
072 7 _aUNH
_2bicssc
072 7 _aCOM032000
_2bisacsh
072 7 _aUNH
_2thema
072 7 _aUDBD
_2thema
082 0 4 _a005.7
_223
100 1 _aMistry, Sajib.
_eauthor.
_0(orcid)0000-0001-7513-3789
_1https://orcid.org/0000-0001-7513-3789
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEconomic Models for Managing Cloud Services
_h[electronic resource] /
_cby Sajib Mistry, Athman Bouguettaya, Hai Dong.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIX, 141 p. 53 illus., 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
505 0 _a1 Introduction -- 2 Cloud Service Composition: The State of the Art -- 3 Long-term IaaS Composition for Deterministic Requests -- 4 Long-term IaaS Composition for Stochastic Requests -- 5 Long-term Qualitative IaaS Composition -- 6 Service Providers' Long-term QoS Prediction Model -- 7 Conclusion.
520 _aThe authors introduce both the quantitative and qualitative economic models as optimization tools for the selection of long-term cloud service requests. The economic models fit almost intuitively in the way business is usually done and maximize the profit of a cloud provider for a long-term period. The authors propose a new multivariate Hidden Markov and Autoregressive Integrated Moving Average (HMM-ARIMA) model to predict various patterns of runtime resource utilization. A heuristic-based Integer Linear Programming (ILP) optimization approach is developed to maximize the runtime resource utilization. It deploys a Dynamic Bayesian Network (DBN) to model the dynamic pricing and long-term operating cost. A new Hybrid Adaptive Genetic Algorithm (HAGA) is proposed that optimizes a non-linear profit function periodically to address the stochastic arrival of requests. Next, the authors explore the Temporal Conditional Preference Network (TempCP-Net) as the qualitative economic model to represent the high-level IaaS business strategies. The temporal qualitative preferences are indexed in a multidimensional k-d tree to efficiently compute the preference ranking at runtime. A three-dimensional Q-learning approach is developed to find an optimal qualitative composition using statistical analysis on historical request patterns. Finally, the authors propose a new multivariate approach to predict future Quality of Service (QoS) performances of peer service providers to efficiently configure a TempCP-Net. It discusses the experimental results and evaluates the efficiency of the proposed composition framework using Google Cluster data, real-world QoS data, and synthetic data. It also explores the significance of the proposed approach in creating an economically viable and stable cloud market. This book can be utilized as a useful reference to anyone who is interested in theory, practice, and application of economic models in cloud computing. This book will be an invaluable guide for small and medium entrepreneurs who have invested or plan to invest in cloud infrastructures and services. Overall, this book is suitable for a wide audience that includes students, researchers, and practitioners studying or working in service-oriented computing and cloud computing. .
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aApplication software.
650 0 _aManagement information systems.
650 0 _aComputer science.
650 0 _aComputer communication systems.
650 1 4 _aInformation Systems Applications (incl. Internet).
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18040
650 2 4 _aManagement of Computing and Information Systems.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I24067
650 2 4 _aComputer Communication Networks.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I13022
700 1 _aBouguettaya, Athman.
_eauthor.
_0(orcid)0000-0003-1254-8092
_1https://orcid.org/0000-0003-1254-8092
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aDong, Hai.
_eauthor.
_0(orcid)0000-0002-7033-5688
_1https://orcid.org/0000-0002-7033-5688
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319738758
776 0 8 _iPrinted edition:
_z9783319738772
776 0 8 _iPrinted edition:
_z9783319892603
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-73876-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
999 _c244272
_d244271