Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Abstract

Cloud computing is based on Virtual Machines (VM) or containers, which provide their own software execution environment that can be deployed by facilitating technologies on top of various physical hardware. The use of VMs or containers represents an efficient way to automatize the overall software engineering and operation life-cycle. Some of the benefits include elasticity and high scalability, which increases the utilization efficiency and decreases the operational costs. VMs or containers as software artifacts are created using provider-specific templates and are stored in proprietary or public repositories for further use. However, technology specific choices may reduce their portability, lead to a vendor lock-in, particularly when applications need to run in federated Clouds. In this paper we present the current state of development of the novel concept of a VM repository and operational environment for federated Clouds named ENTICE. The ENTICE environment has been designed to receive unmodified and functionally complete VM images from its users, and transparently tailor and optimise them for specific Cloud infrastructures with respect to their size, configuration, and geographical distribution, such that they are loaded, delivered, and executed faster and with improved QoS compared to their current behaviour. Furthermore, in this work a specific use case scenario for the ENTICE environment has been provided and the underlying novel technologies have been presented.

Scientific workflows: Past, present and future

“…It achieves a good approximation to the Pareto frontier of trade-off solutions [41]. The ENTICE project, http://www.entice-project.eu/, enriched ASKALON with multi-objective optimised placement of fragmented VMs from a distributed repository with faster delivery, enhanced availability, and reduced storage costs [42]. ASKALON provides automatic composition of workflows using semantic descriptions of workflow activities and ontologies; delivering resilience by creating alternative workflows or sub-graphs [44].…”

Distributed environment for efficient virtual machine image management in federated Cloud architectures

“…As we are dealing with an evolutionary multi‐objective algorithm, it is crucial to asses the algorithm's scalability with respect to the number of separate evaluations of the individuals within the population. Figure provides a comparison between the execution times of the optimization framework without support for replication and with replication support. The benchmark data and population size used in this test are identical to the previous one.…”

“…For this purpose, we developed distinctive software modules, specifically tailored for enabling (1) simplified creation of lightweight and highly optimized VMIs tuned for specific application requirements; (2) multi‐objective VMI repository optimization; and (3) efficient reasoning mechanism for streamline support of complex VMI operations. We implemented and integrated the introduced novelties as essentials components of the ENTICE environment …”

“…Figure 9 provides a comparison between the execution times of the optimization framework without support for replication [2] and with replication support. The benchmark data and population size used in this test are identical to the previous one.…”

MDAPSP - Uma arquitetura modular distribuída para auxílio à predição de estruturas de proteínas

“…A capacidade requerida em um experimento computacional é difícil de ser determinada de antemão. O problema com a oferta de recursos é a busca pela melhor configuração, com o menor investimento e sobrecarga mínima (KIMOVSKI et al, 2016). Conforme a característica de um experimento, uma máquina do tipo workstation é suficiente, na qual o usuário tem o controle do hardware, não tem limitações com a largura de rede e responsabiliza-se pelos dados.…”