Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

15.05.2019 | Focus

A cognitive/intelligent resource provisioning for cloud computing services: opportunities and challenges

Zeitschrift:
Soft Computing
Autoren:
Mahfoudh Saeed Al-Asaly, Mohammad Mehedi Hassan, Ahmed Alsanad
Wichtige Hinweise
Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud’s services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud’s resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud’s resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud’s resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe​​​​​​​




Testen Sie jetzt 30 Tage kostenlos.

Literatur
Über diesen Artikel

Premium Partner

    Bildnachweise