Universities, R&D Groups and Academic Networks

CloudLightning is a 36-month H2020 project to develop an intelligent, power-efficient cloud computing infrastructure that will provide energy savings to cloud service providers and simplify access to cloud resources for cloud consumers.

Project Cloud TRANSIT is a small research project founded by German Federal Ministry of Education and Research (). It comprises several partners from academia and industry.

• Lübeck University of Applied Sciences
• University of Lübeck, Institute of Telematics
• Center of Excellence CoSA
• fat IT solutionlab

The project focuses especially the needs of small and medium sized enterprises coming along with cloud adaption of cloud-native application provisioning. Cloud-native applications are often characterized by a highly implicit technological dependency on hosting cloud infrastructures. Furthermore, cloud standardization coverage decreases year by year. So especially cloud-native applications are vulnerable to vendor lock-in which often prevents small and medium sized enterprises from providing innovative cloud services or applications for public.

The project Cloud TRANSIT investigates, how to design cloud-based applications and services to reduce technological dependencies on underlying cloud infrastructures.

• The aim is to provide methodologies and tools to define secure, transferable and elastic services being deployable to any IaaS cloud infrastructure.
• The general approach is to use container cluster solutions to bridge IaaS infrastructures to provide a portable cloud runtime environment.
• Migration of these services from one private or public cloud infrastructure to another cloud infrastructure should be possible.
• This kind of transferability reduces vendor lock-in apprehensions and fosters cloud-adoption.
• The solution should be manageable by small and medium sized enterprises (in extreme with 1-person IT staffs).

 http://cosa.fh-luebeck.de/cloud_transit

The current landscape at enterprises is that they have adopted a number of data management technologies, both a combination of traditional SQL ones and new ones such as NoSQL, NewSQL, in-memory analytics and so on. This trend is resulting in what has been so-called Polyglot Persistence, that is, having data stored on different kinds of databases. The adoption of new technologies such as NoSQL has enabled to solve issues such as having more suitable data models and associated query languages/APIs for dealing with some data management problems. Examples of this are key-value data stores, document-oriented data stores and graph databases.

However, the Polyglot Persistence has raised new challenges and created new difficulties. Two main pains in Polyglot Persistence environments are related to the updates and queries across data stores. The first pain is related to the consistency of updates. Most NoSQL data stores are non-transactional what is an actual pain by itself since consistency is not guarantee for business operations that require to modify multiple rows. The problem is worsened in the polyglot persistence scenario, since now a business operation might require to modify multiple data stores and a failure or concurrent access might result in getting an inconsistent polyglot database due to the lack of transactional consistency guarantees. There are also pains with the queries. This pain lies in that different databases speak different query languages or APIs and when a query across data stores is needed this is simply not supported because maybe a database speaks SQL and another a proprietary API, as it happens with MongoDB.

CoherentPaaS addresses the aforementioned pains of Polyglot Persistence environments. It leverages a novel technology able to scale out transactional processing. This technology is being integrated with different data stores such as SQL, NoSQL, in-memory analytics, etc. This results in individual data stores with full ACID properties such as a full ACID MongoDB. But more interestingly, since all these data stores get integrated with the same scalable transactional engine, CoherentPaaS supports queries across data stores. The approach adopted by CoherentPaaS is quite novel. It enables to combine the native query languages of the underlying data stores with SQL.

CoherentPaaS combines in an integrated platform SQL (OLTP, in-memory analytics, OLAP), NoSQL (key-value, document-oriented and graph databases) and CEP/data streaming. In this platform applications can start global transactions and update any combination of data stores with full transactional semantics. Applications can also make queries across data stores combining the simplicity of SQL with the power of the underlying native query languages.

More details can be found at the original web site: http://coherentpaas.eu/

LeanBigData targets at building an ultra-scalable and ultra-efficient integrated big data platform addressing important open issues in big data analytics. Current big data infrastructure scale to large amounts of data and system sizes, however, in a very inefficient way consuming disproportionally high resources per data item processed. Furthermore, the lack of integrated big data management technologies to process streaming events and different workloads over stored data results in the complexity to integrate disparate big data systems and the overhead of copying data across systems. What is more, data analysis cycles to refine queries and identify facts of interest take hours, days, or weeks, whereas business processes demand today shorter cycles. LeanBigData will address these issues by:

·         Delivering ultra-scalable big data management systems: NoSQL key-value data store, a distributed CEP system, and a distributed SQL query engine.

·         Providing an integrated big data platform to avoid the inefficiencies and delays introduced by current ETL-based integration approaches of disparate technologies.

·         Supporting an end-to-end big data analytics solution removing the main sources of delays

More details can be found at the official web site:  http://leanbigdata.eu/