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001 978-3-030-39863-7
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008 200720s2020 sz | s |||| 0|eng d
020 _a9783030398637
_9978-3-030-39863-7
024 7 _a10.1007/978-3-030-39863-7
_2doi
050 4 _aHD45
072 7 _aKJD
_2bicssc
072 7 _aBUS041000
_2bisacsh
072 7 _aKJD
_2thema
082 0 4 _a658.4062
_223
082 0 4 _a658.514
_223
245 1 0 _aManaging Distributed Cloud Applications and Infrastructure
_h[electronic resource] :
_bA Self-Optimising Approach /
_cedited by Theo Lynn, John G. Mooney, Jörg Domaschka, Keith A. Ellis.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Palgrave Macmillan,
_c2020.
300 _aXXIII, 163 p. 62 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aPalgrave Studies in Digital Business & Enabling Technologies,
_x2662-1290
505 0 _aChapter 1 -- Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds -- Chapter 2 -- RECAP Data Acquisition and Analytics Methodology -- Chapter 3 - Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications -- Chapter 4 - Application Placement and Infrastructure Optimisation -- Chapter 5 - Simulating Across the Cloud-to-Edge Continuum -- Chapter 6 - Case Studies in Application Placement and Infrastructure Optimisation.
506 0 _aOpen Access
520 _aThe emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver qualityof service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities. .
650 0 _aTechnological innovations.
650 0 _aElectronic commerce.
650 0 _aComputer engineering.
650 0 _aComputer networks .
650 1 4 _aInnovation and Technology Management.
650 2 4 _ae-Commerce and e-Business.
650 2 4 _aComputer Engineering and Networks.
700 1 _aLynn, Theo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMooney, John G.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDomaschka, Jörg.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aEllis, Keith A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030398620
776 0 8 _iPrinted edition:
_z9783030398644
776 0 8 _iPrinted edition:
_z9783030398651
830 0 _aPalgrave Studies in Digital Business & Enabling Technologies,
_x2662-1290
856 4 0 _uhttps://doi.org/10.1007/978-3-030-39863-7
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
912 _aZDB-2-SOB
999 _c37286
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