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Erschienen in: Cluster Computing 2/2018

01.08.2017

CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing

verfasst von: Sukhpal Singh Gill, Inderveer Chana, Maninder Singh, Rajkumar Buyya

Erschienen in: Cluster Computing | Ausgabe 2/2018

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Abstract

Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, self-protection against security attacks and self-optimization for maximum resource utilization. We have evaluated the performance of the proposed approach in a real cloud environment and the experimental results show that the proposed approach performs better in terms of cost, execution time, SLA violation, resource contention and also provides security against attacks.

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Metadaten
Titel
CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing
verfasst von
Sukhpal Singh Gill
Inderveer Chana
Maninder Singh
Rajkumar Buyya
Publikationsdatum
01.08.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1040-z

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