**2. Earth Observation Data Processing and Distribution Pilot**

#### **2.1. ENTICE environment**

**1.** Increased performance of commercial satellites with defence needs in the range of very

**2.** The development of hybrid procurement schemes between private and public customers. **3.** Appearance of the New Space scheme started in Silicon Valley, which attracted the interest of investors and contributed to the creation and entrance of new actors in the space sector.

To these, we would add the dedicated budget of new countries, such as Kazakhstan, Venezuela and Vietnam, in EO; increased budget in new EO programmes for India, China and South Korea [2] and fast evolution of information and communication technologies, which facilitated the creation of new applications requiring availability of lots of information in the shortest time possible. This contributed to the evolution of the space sector in two manners: (a) the evolution of the sensors to provide highest performance at a lower cost and (b) the launch of more satellites to cover the demand of information. This last explains the increase in the launch of satellites during the last years and interest of satellite operators to operate satellite constellations in order to reduce the revisit time and offer more coverage of the land surface. A proof of this is the number of EO satellites launched between 2006 and 2015: 163 satellites over 50 kg were launched for civil and commercial applications, generating \$18.4 billion in manufacturing market revenues, whereas 419 satellites are expected to be launched over the next decade (2016–2025), generating \$35.5 billion in manufacturing revenues. In terms of EO data sales, the market reached \$1.7 billion in 2015 and it is expected to reach \$3 billion in 2025. This is \$12.2 billion total revenue in the decade 2006–2015 and \$24 billion in the decade 2016– 2025 [3]. The amount of generated data is used, for instance, to accumulate spatial and temporal records of the world itself, of the events and changes that occur in it in a diverse number of applications: security, maritime, agriculture, energy and emergency, among others [4].

However, the infrastructures used to manage EO data are still based on traditional EO systems, which (because of their previous ambit of application) make use of on-site traditional infrastructures or data centers. Their architecture was designed to be monolithic in a localized

Now, the process of recording data from Earth observations generates massive amounts of spatiotemporal geospatial information that has to be intensively processed for a variable and increasing demand. This is a handicap for traditional data centers since they are not designated to manage variable amounts of data. They were designed and sized to operate a certain data volume. They are then limited in terms of flexibility and scalability [5]. The storage of increasing amounts of data over time is also a challenge, since the recordings are also main-

Traditional Earth Observation Payload Data Ground Segments (PDGS) present the following

**ii.** There is a risk of oversizing/undersizing the infrastructure to offer services when highly

single infrastructure.

tained by their owners over time as well [6].

variable demand exists.

limitations to cover the demands of the current EO market:

**i.** Traditional infrastructures are not flexible or easily scalable to operate.

**iii.** They make the cost of acquiring recent images of the Earth very high.

high resolution products, i.e. resolutions between 0.25 and 1 m.

176 Multi-purposeful Application of Geospatial Data

In order to facilitate the implementation in cloud, the EOD pilot makes use of the ENTICE middleware [11], which facilitates autoscaling and flexibility to the ingestion of satellite imagery, its processing and distribution to end users with variable demands. Kecskemeti et al. [12] introduced the ENTICE approach to solve these problems. The ENTICE environment consists of a ubiquitous repository-based technology, which provides optimised virtual machine (VM) image creation, assembly, migration and storage for federated clouds. The webpage of ENTICE can be found in [13].

ENTICE facilitates the implementation of cloud applications by simplifying the creation of lightweight virtual machine images (VMIs) by means of functional descriptors. These functional descriptors define at high and functional levels the VMIs and contribute to define the system Service Level Agreement (SLA) to facilitate the optimization of the VMIs in terms of performance, costs, size and quality of service (QoS) needed. Then, the VMIs are automatically decomposed and distributed to meet the application runtime requirements. In addition, ENTICE facilitates elastic autoscaling. The benefits of using ENTICE are the following:


In the EOD pilot, ENTICE is used as middleware between the federated infrastructure described in Section 3.1 and the gs4EO application software.

*2.2.1. EOD architecture*

of the different modules.

been redesigned and implemented, see **Figure 1**.

Orchestrator has the following functions:

The architecture is composed of the following components:

**Figure 1.** Earth Observation Data Processing and Distribution pilot (EOD)'s architecture.

○ To identify which outputs shall be generated by the processors.

The main objectives of the EOD pilot is to process real data of Deimos-2 satellite in a realistic scenario of normal operation and the validation of the processing chain module as part of the cloud infrastructure. Ramos and Becedas [14] proposed an original architecture of the gs4EO suit to be implemented in cloud. Based on that work, the architecture for the EOD pilot has

Optimization of an Earth Observation Data Processing and Distribution System

http://dx.doi.org/10.5772/intechopen.71423

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• **monitor4EO:** It is a ground station monitor, which ingests the available raw data from the ground stations to the cloud system. It contains an Orchestrator, which manages the tasks

• **process4EO server:** It is the Orchestrator, which is the component that manages the tasks to be done by all the modules of the architecture computed in the cloud infrastructure. The

○ To generate the Job Orders. They contain all the necessary information that the processors need. Furthermore, these eXtensive Markup Language (XML) files include the interfaces
