ENTICE
Sponsor
The ENTICE project received funding from the European Commission's Horizon 2020 ICT fund in 2015.
Project data
Department in charge
Existing research mostly focuses on pre-optimising algorithms, which are not applicable to already available VM images. With its VM synthesiser, ENTICE will extend pre-optimising approaches so that image dependency descriptions are mostly automatically generated. The project will also introduce new comprehensive post-optimising algorithms so that existing VM images can be automatically adapted to dynamic Cloud environments.
Participants
UNIVERSITAET INNSBRUCK, Austria (Coordinator)
MAGYAR TUDOMANYOS AKADEMIA SZAMITASTECHNIKAI ES AUTOMATIZALASI KUTATOINTEZET, Hungary
UNIVERZA V LJUBLJANI, Slovenia
FLEXIANT LIMITED, United Kingdom
WELLNESS TELECOM SL. Spain
DEIMOS CASTILLA LA MANCHA SL, Spain
FLEXIOPS LIMITED, United Kingdom
Project goals
Virtualization is a key enabler of cloud computing that allows to run multiple virtual machines (VM) with their own software environment and applications on top of physical hardware with the promise to increase efficient usage of hardware resources at lower cost and increased elasticity. Typically VMs are created using provider-specific templates (so called VM images) that are stored in proprietary repositories which leads to provider lock-in and hinder portability or simultaneous use of multiple federated Clouds. Optimization at the level of the VM images is needed both by the Cloud applications as well as by the underlying Cloud providers for improved resource usage, speed, elasticity, redundancy, fault tolerance and other much desired Quality of Service (QoS)-related features. Critical barriers exist that prevent many users from industry, business and academia to effectively use cloud resources and virtualization environments for their computing and data processing needs. In this project we will create a novel (ENTICE) environment targeting federated Cloud infrastructures for:
(i) simplifying the creation of lightweight and highly optimized VM images;
(ii) automatic decomposition and distribution of VM images based on multi-objective optimization (performance, economic costs, storage size, and QoS requirements) and a knowledge collection and reasoning infrastructure, and
(iii) auto-scaling of Cloud resources that supports interoperability of VMs across Cloud infrastructures without provider lock-in.
We gathered an interesting selection of complementary use cases from energy management to earth observation and cloud orchestration which will be used to validate the ENTICE environment and that are provided by two SME and one industrial partners of the project.
