“ENTICE in Space” : Deimos & Big Space Data

In today’s blog we explore the way in which ENTICE will highly decrease the delivery time of large satellite images captured in Earth’s orbit. Deimos’ research focuses in the development of future internet technologies in order to improve the following Earth Observation services and to highly reduce the costs associated with on premises deployment

  • Acquisition of raw data: when the imagery data is ingested from the satellite into the ground station, the system is notified and the ingestion component automatically ingests the raw data into the cloud for processing.
  • Processing of the raw data: once the data is ingested, it is processed in the product processors. There are several processing levels.
  • Archiving and cataloguing geo-images: the different products obtained from the processing of raw data are archived and catalogued in order to provide these images to end users or to provide high-added value services.
  • Offering user services: this is the front-end of the system. It allows end users to select which product they want to visualize or to download.

Processing and distribution of big space data still presents a critical challenge: the treatment of massive and large-sized data obtained from Earth Observation satellite recordings. Remote sensing industries implement on-site conventional infrastructures to acquire, store, process and distribute the geo-information generated. However these solutions do not cover sudden changes in the demand of services and the access to the information presents large latencies.

By using ENTICE environment, it is expected to highly decrease the delivery time of the satellite imagery to end users and to improve their accessibility, increasing the competitiveness of the EO industries. It will be possible thanks to ENTICE environment, which will drastically reduce of the required time for the creation and the deployment of the VMs. This will allow to support high demanding changes adjusting the infrastructure to serve these overloads, taking advantage of the flexibility and lower time provided by the ENTICE environment.

Virtual Machine Image provisioning as a supply chain

In today’s blog we take a look at Virtual Machine Images (VMIs) and imagine the production of software as a factory process and ask why don’t we apply similar manufacturing processes?

So imagine, in this scenario there are blueprints for the product; methods for applying the necessary technologies; supplies (code) coming from other parties; tools and utilities necessary for development. There are also processes that strongly resemble an assembly line and obviously, extensive testing and validation.

Similarly, one can consider Software as a Service provisioning as an industrial process. There are requests coming from the developers, they are transformed into plans that describe the way how different software components must be collected, composed, deployed, maintained and quality controlled. A timely – and sometimes time critical – deployment requires not just the presence of the necessary software but also the selection of the right resources, assuring their availability, their proper configuration and orchestration. As it can be seen, behind the masquerade of a “service” there is a complex coordination of various software, hardware and sometimes human entities both in time and space. What can we do about this coordination?

Well, if these procedures resemble industrial production lines, why don’t we approach them using well established manufacturing methods? Entice is aimed at storing, transferring and deploying Virtual Machine images in a dynamic, on demand way. Virtual Machine images are created, decomposed, parts are transferred, from the scattered image fragments the necessary components are gathered and re-assembled, all steps executed with strong criteria for optimum.

If we look beyond the challenges posed by software technology, the timely coordination of these processes largely determines the usability and success of the idea. We envision manufacturing metaphors and applying industrial approaches such as supply chain management to model and coordinate the individual processes related to synthesis, analysis, decomposition and composition of Virtual Machine images in order to assure the realization of Entice objectives.

ENTICE Begins Work to Develop Optimised Virtual Machine Images

We are proud to announce the launch of the ENTICE project took place in the evocative surrounds of Innsbruck this month. Our consortium of leading research organisations and universities will combine with leading commercial partners from across Europe and set out to defeat a set of critical barriers that prevent many users from industry, business and academia from effectively using Cloud resources and virtualised environments for their computing and data processing needs.

Meeting for the first time in Innsbruck, Austria, our project partners began work on addressing the five identified barriers that we have set out to overcome:

  1. Manual, error-prone and time consuming VM image creation,
  2. Monolithic VM images with large deployment and migration overheads,
  3. Proprietary un-optimised VM repositories,
  4. Inelastic resource provisioning, and
  5. Lack of information to support effective VM image optimisation.

Drawing together experience experts in the fields of research and industry, the ENTICE consortium contains partners from Spain, Slovenia, Hungary and the UK ad will be led by the University of Innsbruck, in Austria.

In this project, we will research and create a novel VM repository and operational environment named ENTICE for federated Cloud infrastructures aiming to:

  • Simplify the creation of lightweight and highly optimised VM images tuned for functional descriptions of applications;
  • Automatically decompose and distribute VM images based on multi-objective optimisation (performance, economic costs, storage size, and QoS needs) and a knowledge base and reasoning infrastructure to meet application runtime requirements; and
  • Elastic auto-scale applications on Cloud resources based on their fluctuating load with optimised VM interoperability across Cloud infrastructures and without provider lock-in, in order to finally fulfill the promises that virtualization technology has failed to deliver so far.

Virtualization is a key technology in Cloud computing that allows users to run multiple virtual machines (VM) with their own application environment on top of physical hardware. Virtualization enables scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilisation efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS, such as response time) to the end-users. Typically, VMs are created using provider specific templates (so-called VM images) that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimisation at the level of the VM images 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.

Existing research mostly focuses on pre-optimising algorithms, which are not applicable to already available VM images. With its VM synthesiser, ENTICE will extend preoptimising 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.

ENTICE project aims to achieve a major breakthrough in simplified, storage-efficient and cost-effective creation, usage, and portability of VM images to dramatically improve elasticity and load balancing across federated Cloud infrastructures. ENTICE will reach far beyond existing systems by its unique and highly ambitious goal to distribute VM images across Cloud infrastructures for multiple possibly conlicting optimisation objectives including performance and QoS-related goals, operational costs, and storage from which applications and Cloud providers can bene_t. In the following we describe the most important research challenges that must be addressed to bring the current state-of-the-art to a new innovation level.

We’d love to hear what you think about the ENTICE project and are always keen to hear from collaboration partners and other interested stakeholders. Please feel free to get in touch to discuss any aspect of the project and to find out more.