• English
    • Deutsch
View Item 
  •   OPARU Home
  • Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
  • Publikationen
  • View Item
  •   OPARU Home
  • Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
  • Publikationen
  • View Item
  • English 
    • English
    • Deutsch
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Done yet? A critical introspective of the cloud management toolbox

Thumbnail
Download
leznik_itmc_2018.pdf (504.6Kb)
Erstveröffentlichung
2018-08-16
DOI
10.18725/OPARU-10602
Beitrag zu einer Konferenz


Authors
Leznik, Mark
Volpert, Simon
Griesinger, Frank
Seybold, Daniel
Domaschka, Jörg
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutions
Institut für Organisation und Management von Informationssystemen
Published in
2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) ; 2018 (2018). - ISBN 978-1-5386-1470-9, ISBN 978-1-5386-1469-3
Link to original publication
https://dx.doi.org/10.1109/ICE.2018.8436348
Peer review
ja
Document version
acceptedVersion
Conference
ICE/IEEE ITMC International Conference on Engineering, Technology and Innovation, 2018-06-17 - 2018-06-20, Stuttgart
License
Standard
https://oparu.uni-ulm.de/xmlui/license_v3
Abstract
With the rapid rise of the cloud computing paradigm, the manual maintenance and provisioning of the technological layers behind it, both in their hardware and virtualized form, became cumbersome and error- prone. This has opened up the need for automated capacity planning strategies in heterogeneous cloud computing environments. However, even with mechanisms to fully accommodate customers and ful ll service- level agreements, providers often tend to over-provision their hardware and virtual resources. A proliferation of unused capacity leads to higher energy costs, and correspondingly, the price for cloud technology services. Capacity planning algorithms rely on data collected from the utilized resources. Yet, the amount of data aggregated through the monitoring of hardware and virtual instances does not allow for a manual supervision, much less data analysis or a correlation and anomaly detection. Current data science advancements enable the assistance of e cient automation, scheduling and provisioning of cloud computing resources based on supervised and unsupervised machine learning techniques. In this work, we present the current state of the art in monitoring, storage, analysis and adaptation approaches for the data produced by cloud computing environments, to enable proactive, dynamic resource provisioning.
EU Project
MELODIC / Multi-cloud Execution-ware for Large-scale Optimized Data-Intensive Computing / EC / H2020 / 731664
RECAP / Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications / EC / H2020 / 732667
Earlier version(s)
http://dx.doi.org/10.18725/OPARU-9631
Subject Headings
Cloud Computing [GND]
Data Science [GND]
Information technology; Management [LCSH]
Service-oriented architecture (Computer science) [LCSH]
Computing platforms [LCSH]
Machine learning [LCSH]
Keywords
IaaS; PaaS; Autonomics
Dewey Decimal Group
DDC 004 / Data processing & computer science

Metadata
Show full item record

Citation example

Leznik, Mark et al. (2018): Done yet? A critical introspective of the cloud management toolbox. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-10602

Other citation formats



About OPARU | Contact Us
Impressum | Privacy statement
 

 

Advanced Search

Browse

All of OPARUCommunities & CollectionsFacultiesInstitutionsPersonsResource typesUlm SerialsDewey Decimal ClassesFundingThis CollectionFacultiesInstitutionsPersonsResource typesUlm SerialsDewey Decimal ClassesFunding

My Account

LoginRegister

Statistics

View Usage Statistics

About OPARU | Contact Us
Impressum | Privacy statement