The European Ground Motion Service (EGMS) is part of the Copernicus Land Monitoring Service (CLMS) managed by the EEA (European Environment Agency) [1]. EGMS is based on the full resolution InSAR processing of ESA Sentinel-1 (S1) acquisitions over Europe (Copernicus Participating states) [2]. The first release or baseline includes ground motion time-series between 2015 and 2020. This open data service has yearly updates.The EGMS employs persistent scatterer (PS) and distributed scatterer (DS) in combination with a Global Navigation Satellite System (GNSS) model to calibrate the ground motion products. This public dataset consists of three products levels (L2a/Basic, L2b/Calibrated and L3/Ortho). The Basic and Calibrated product levels are full resolution (20x5m) Line of sight (Los) velocity maps from ascending/descending orbits. The Ortho product offers horizontal (East-West) and vertical (Up-Down) anchored to the reference geodetic model resampled at 100x100m.The objective of this paper is to describe the independent validation of this continental scale ground motion time-series dataset. The goal is to ensure that the EGMS products are consistent with user requirements and product specifications and they cover the expected range of applications. To evaluate the fitness of the EGMS ground motion data service 7 reproducible validation activities (VA) have been developed gathering validation data from 50 sites across 12 European countries. Last but not least, a software environment has been developed to carry out all the validation activities and describe/store the data coming from the validation sites.

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As clouds increase in size and as machines of different types are added to the infrastructure in order to maximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting different architectures poses significant challenges. To efficiently access heterogeneous resources and, at the same time, to exploit these resources to reduce application development effort, to make optimisations easier and to simplify service deployment, requires a re-evaluation of our approach to service delivery. We propose a novel cloud management and delivery architecture based on the principles of self-organisation and self-management that shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. Our goal is to address inefficient use of resources and consequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems particularly in mind. The framework is general but also endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service provision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.@en