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Open AccessJournal ArticleDOI

Automatic Resource Allocation for High Availability Cloud Services

Stefano Marrone, +1 more
- Vol. 52, Iss: 1, pp 980-987
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TLDR
The approach is based on model-driven principles and uses both UML and Bayesian Networks to capture, analyse and optimise cloud deployment configurations and is extensible to the operational phases of the life-cycle.
Abstract
This paper proposes an approach to support cloud brokers finding optimal configurations in the deployment of dependability and security sensitive cloud applications. The approach is based on model-driven principles and uses both UML and Bayesian Networks to capture, analyse and optimise cloud deployment configurations. While the paper is most focused on the initial allocation phase, the approach is extensible to the operational phases of the life-cycle. In such a way, a continuous improvement of cloud applications may be realised by monitoring, enforcing and re-negotiating cloud resources following detected anomalies and failures.

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Citations
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A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing

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Pattern-based orchestration and automatic verification of composite cloud services

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Proceedings ArticleDOI

Automatic Cloud Services Composition for Big Data Management

TL;DR: This work proposes a methodology for composition of services used for analyses of different Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.
References
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Journal ArticleDOI

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Journal ArticleDOI

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Book

Learning Bayesian networks

TL;DR: This chapter discusses Bayesian Networks, a framework for Bayesian Structure Learning, and some of the algorithms used in this framework.
Journal ArticleDOI

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
Journal ArticleDOI

Guest Editor's Introduction: Model-Driven Engineering

TL;DR: Model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
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