Cloud Computing and Content Delivery Network use within Earth Observation Ground Segments: experiences and lessons learnt
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1 Cloud Computing and Content Delivery Network use within Earth Observation Ground Segments: experiences and lessons learnt J.Farres EOP-GS ESRIN 6/6/2012 Page 1
2 Agenda 1. Introduction 2. ESA Experiences 1. EO Dissemination on Level 3 2. EO Re-processing on Amazon 3. Dissemination and Processing on Hetzner 4. Exploitation platform with Helix Nebula 3. Summary of lessons learnt Page 2
3 Introduction ICT Costs savings Dissemination peaks IaaS Hosting (VPS, Rental) CDN Processing bursting PaaS SaaS Cloud Computing Collaboration platform A model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [NIST] Page 3
4 Agenda 1. Introduction 2. ESA Experiences 1. EO Dissemination on Level 3 2. EO Re-processing on Amazon 3. Dissemination and Processing on Hetzner 4. Exploitation platform with Helix Nebula 3. Summary of lessons learnt Page 4
5 Case 1: EO Dissemination on Level 3 (1) Purpose Allow for very performing dissemination of EO products at the event of natural disasters. Project / Service Timeframe: Provider: Level 3, off-the-shelf services Data: System: 6-7TB, SAR, GOCE Portal / EOLI + EO products secured by ESA login Page 5
6 Case 1: EO Dissemination on Level 3 (2) Pros 1. Excellent dissemination performance 2. from US and Europe 3. Good integration with ESA user authentification + EOLI 1. High Price Cons 2. Price based on bytes stored and bytes downloaded, hence penalizes large EO products 4. Very good monitoring and reporting 5. allowing to detect and stop abuse Page 6
7 Case 2: EO Re-processing on Amazon(1) Purpose Fast re-processing of large EO products collections for CalVal purposes. Project / Service Timeframe: 2009 and 2011 Provider: Amazon, EC2, S3 Data: System: ERS SAR Wave, MIPAS (30,000 products) 200 Virtual Servers configured as Working Nodes to an ESA grid. Usage: 11 CPU years of processing in 5 weeks Page 7
8 Case 2: EO Re-processing on Amazon(2) Pros 1. Excellent processing scalability 2. Efficient bulk-in/out data service (via HD) 3. The faster the cheaper as it reduces storing costs Cons 1. Complex and changing pricing, e.g. periods with cheaper hosts and free data upload. Page 8
9 Case 3: Dissemination and Processing on Hetzner (1) Purpose Couple large processing capability with fast dissemination for low cost. Project / Service Timeframe: 2011 Provider: Hetzner (Hosting services) Data: System: 60TB 1 Head: Catalogue, Processor Register, Cluster Head n Nodes: Data Dissemination point, Cluster Node Designed as back-end for multiple front-ends Usage: GeoHazards SuperSites (38,000 SAR images and 3,000 users) Page 9
10 Case 2: Dissemination and Processing on Hetzner (2) Pros 1. Good archive scalability (chunks of 8TB) 2. Much cheaper than Level 3 or Amazon services Cons 1. System scales storage, dissemination and processing capabilities simultaneously. 2. Lower service levels than Level 3 or Amazon 3. Synergy of processing - dissemination: processing peaks followed by dissemination peaks 4. Storage costs service two functions: dissemination and processing Page 10
11 Case 4: Exploitation platform with Helix Nebula SSEP (1) Purpose Pilot a collaborative platform for EO exploitation using multisourced cloud provisioning. Project / Service Timeframe: Providers: ATOS, CloudSigma, Interoute, T-Systems Data: GeoHazards data (ESA, CNES, DLR, ) Processors: ESA, CNR, Gamma, System (TBD): Large capabilities: storage, dissemination & processing Platform for deployment of partner data, processors Based on existing GS sw components Page 11
12 Case 4: Exploitation platform with Helix Nebula SSEP (2) Expectations ESA can distribute data dissemination and processing functions over several cloud providers (multi-sourcing) at best value. Other agencies can do the same on the same platform/cloud. Users can provision their ICT from the cloud near the data/services provided by ESA and other agencies. Users can deploy EO value added services directly on the cloud, i.e. employ pay-per-use (SaaS) models for their commercialization. Page 12
13 Agenda 1. Introduction 2. ESA Experiences 1. EO Dissemination on Level 3 2. EO Re-processing on Amazon 3. Dissemination and Processing on Hetzner 4. Exploitation platform with Helix Nebula 3. Summary of lessons learnt Page 13
14 Lessons Learnt: ICT provisioning 1. As soon as ICT needs can be predicted and planned, IaaS is more expensive than other hosting solutions like (VPS, Renting). 2. Flexibility of Public IaaS is less appealing when internal resources are pooled, virtualized and managed as an internal cloud. 3. On the other hand, IaaS services allow to size down internal ICT resources to the fixed need and ensure their maximum utilization; e.g. using external provisioning for the variable need. Hybrid ICT provisioning Page 14
15 Lessons Learnt: Service Levels Terms & Conditions in Public Clouds express surprising low commitment. Cloud opportunities can become risks when applied to critical systems. Develop multi-sourcing Plan contingency scenarios for services hosted in Public Clouds Page 15
16 Lessons Learnt: Application Areas in EO GS Dissemination and on-demand processing because is very variable (depending on user demand) Secondary archive and re-processing because is limited in time Temporary resources for integration, testing and demonstration because is limited in time System sizing because needs are unknown Important EO GS areas can gain from Cloud Computing Page 16
17 Lessons learnt: Users User expectations on Cloud Computing are very high All data discoverable and accessible online with same performances as consumer services. Availability of long time series of coherent data from different providers. Availability of collaboration platforms and tools where to exchange experiences and information and where data from different providers and even different disciplines can be accessed, combined and exploited. Users will be able to perform processing directly on the cloud using virtual servers. The Cloud provides a well accepted paradigm for pay-per-use User communities adopt social networks patterns: sharing objects, providing feed-back Be up to the users expectations and do not loose their trust Page 17
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