Use Cases towards a Decentralized Repository for Transparent and Efficient Virtual Machine Operations


Virtualization is a key enabling technology in Cloud computing that allows users to run multiple virtual machines (VMs) with their own application environment on top of physical hardware. It permits scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilization efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS) to the end-users. Typically, VMs are created using provider-specific templates that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimization at the level of the virtual machine image is needed both by the applications and by the underlying Cloud providers for improved resource usage, operational costs, elasticity, storage use, and other desired QoS-related features. To overcome those issues, the ENTICE project researches and creates a novel VM repository and operational environment for federated Cloud infrastructures. There exists a large variety of industrial applications that can strongly benefit by the ENTICE environment. In this paper we present an interesting selection of complementary use cases that drive the definition of the essential requirements for the ENTICE environment, and more importantly, validate the introduced innovations.

DOI: 10.1109/PDP.2017.47

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@article{Prodan2017UseCT, title={Use Cases towards a Decentralized Repository for Transparent and Efficient Virtual Machine Operations}, author={Radu Prodan and Thomas Fahringer and Dragi Kimovski and Gabor Kecskemeti and Attila Csaba Marosi and Vlado Stankovski and Jonathan Becedas and Jose Julio Ramos and Craig Sheridan and Darren Whigham and Carlos Rodrigo Rubia Marcos}, journal={2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)}, year={2017}, pages={478-485} }