**1. Introduction**

Traditionally, Earth Observation systems have been operated by governments and public organizations; the primary investors being US, China, Russia, Japan and Europe mainly because of worldwide common objectives such as climate change, sustainable development and objectives at national level.

However, from 2015 to 2016, the Earth Observation from space paradigm is changing with the globalization of the market, the evolution of the information and communication technologies and the high investment of private entities in the field.

This boost of commercial interest in Earth Observation can be explained because of the parallel evolution of three main pillars, as stated by Denis et al. in [1]:

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**1.** Increased performance of commercial satellites with defence needs in the range of very high resolution products, i.e. resolutions between 0.25 and 1 m.

**iv.** The customers cannot access directly neither fast to the information they need because

Optimization of an Earth Observation Data Processing and Distribution System

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However, the use of cloud computing technology can eliminate the previous drawbacks to improve EO services because it is elastic, scalable, it works on demand through virtualization of resources, offers virtually unlimited storage and computation capability, it is worldwide

**i.** The virtual machine images (VMIs) are not optimized, being highly oversized, impacting in the costs of using the infrastructure and in the dynamic resources provisioning.

**ii.** The deployment of virtual machines (VM) in cloud is not in real time. The deployment normally takes between 10 and 20 minutes, which directly affects to the flexibility and

**iii.** Although the pay per use model should intrinsically have reduced costs, since the customer only pays for what he uses, the costs of using cloud computing are still high.

**iv.** There are some major worldwide champions in the offer of cloud services such as Amazon, Google, Microsoft and IBM, which make difficult the migration of a system from a cloud infrastructure to another different cloud infrastructure, existing vendor lock-in. This limits the democratization of these services and makes an entrance barrier for new cloud providers.

Within the ENTICE H2020 project (project no. 644179), we intend to demonstrate that processing the data recorded from Earth observations in a cloud environment with the middleware ENTICE optimizes the efficiency and overcomes the critical barriers of cloud computing and data processing needs. Among other advantages, ENTICE provides independence from a specific infrastructure provider and facilitates the distribution of VMs in distributed

In this work, we present the implementation of the Earth Observation Data (EOD) pilot, which mainly consists of the implementation in cloud of the already commercial Ground Segment for Earth Observation (gs4EO) suit, commercialized by Deimos [9], which is currently opera-

For this purpose, we simulate a real scenario with the Deimos-2 satellite running in a federated cloud infrastructure, in which we obtain real performance metrics and present real system requirements for normal operations with the satellite. Through this experimentation, we

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

demonstrate the EOD concept as a solution for the new EO market paradigm.

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

Nevertheless, the current cloud computing technology still presents some limitations:

this has to be processed and ad-hoc distributed.

connected and it is based on a pay per use model [7, 8].

dynamic scalability of the system.

tional in the Deimos-2 satellite mission [10].

infrastructures.

**2.1. ENTICE environment**


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 single infrastructure.

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

Traditional Earth Observation Payload Data Ground Segments (PDGS) present the following limitations to cover the demands of the current EO market:


**iv.** The customers cannot access directly neither fast to the information they need because this has to be processed and ad-hoc distributed.

However, the use of cloud computing technology can eliminate the previous drawbacks to improve EO services because it is elastic, scalable, it works on demand through virtualization of resources, offers virtually unlimited storage and computation capability, it is worldwide connected and it is based on a pay per use model [7, 8].

Nevertheless, the current cloud computing technology still presents some limitations:


Within the ENTICE H2020 project (project no. 644179), we intend to demonstrate that processing the data recorded from Earth observations in a cloud environment with the middleware ENTICE optimizes the efficiency and overcomes the critical barriers of cloud computing and data processing needs. Among other advantages, ENTICE provides independence from a specific infrastructure provider and facilitates the distribution of VMs in distributed infrastructures.

In this work, we present the implementation of the Earth Observation Data (EOD) pilot, which mainly consists of the implementation in cloud of the already commercial Ground Segment for Earth Observation (gs4EO) suit, commercialized by Deimos [9], which is currently operational in the Deimos-2 satellite mission [10].

For this purpose, we simulate a real scenario with the Deimos-2 satellite running in a federated cloud infrastructure, in which we obtain real performance metrics and present real system requirements for normal operations with the satellite. Through this experimentation, we demonstrate the EOD concept as a solution for the new EO market paradigm.
