Learning Layers

Learning Layers

Scaling up Technologies for Informal Learning in SME Clusters

Infrastructure

The Layers infrastructure is the technical backend for the Learning Layers project and enables the development and deployment of services and tools in a secure and private way. An overview of its components is depicted in Fig. 1. It is a ready-to-deploy, custom packaged cloud solution that can be delivered on small-, medium-, and large-scale cloud environments.

image/svg+xml Layers Adapter OpenID Connect/LDAP Social Semantic Server las2peer Layers Box Tag Recommendation Resource Recommendation Expert Identification Overlapping CommunityDetection Consensus Building Social RequirementsEngineering Community ApplicationEditing Semantic VideoAnnotation Cloud VideoTranscoding Simple Web-BasedVisual Analytics Community Evaluation & Learning Analytics Federation Client-Side Enabling Technologies DireWolf yjs Layers App Store(LAPPS) Layers Tools Expert Identification

Figure 1: The technical infrastructure of Layers

The Layers Infrastructure consists of the Layers Box, a state-of-the-art Docker-based container system that can be deployed on any machine ranging from Mac Mini sized computers over server racks up to any cloud provider. It comes with an industrial-strength user management based on OpenID Connect and Lightweight Directory Access Protocol (LDAP), a technology that is equally employed by Google and Microsoft. However, the big advantage over cloud providers such as Microsoft, Google or Amazon is not only that full control of the data is ensured in case the Box is hosted on-premise, but also that the Layers infrastructure is open-source and free. Based on business needs, a particular set of required services can be deployed easily.

Currently a wide range of Learning Layers tools, services and client-side frameworks are already using the existing infrastructure. It comes prepacked with the Social Semantic Server and the las2peer service network. The Social Semantic Server is a service-based framework which is tailored towards informal learning applications, for example recommender services. las2peer is a peer-to-peer-based microservice environment that focuses on privacy and data ownership with services like code generation, community success, social requirements engineering and cloud video transcoding. All services are then easily accessible via the Layers Adapter, a REST-based interface to the Web. The Layers Adapter is built on the industry-strength webserver and load balancer nginx. Federation of multiple boxes allows both to scale up the numbers of users and access content across various companies and networks.

To access apps leveraging the Layers Box services, a custom app store serves as the one-stop-shop for downloading apps onto client devices.

The following slideshow gives a short overview about the ideas and concepts behind the Layers Box:

Unpacking the Layers Box by Istvan Koren

Part of the content on these pages is described in details in the outcome of the Learning Layers work packages WP5 and WP6, documented in deliverables D6.1 [1], D6.2 [2] and D6.3/Report 4 [3] which you can find here. For a short summary on the Layers Box concept, please watch the following video:

References

  1. M. Derntl, R. Klamma, I. Koren, P. Nicolaescu, D. Renzel, K. Ngua, J. Purma, D. Zaki, T. Treasure-Jones, G. Attwell, O. Gray, T. Ley, V. Tomberg, C. Henry, C. Whitehead, D. Theiler, C. Trattner, R. Maier, M. Manhart, M. Schett, and S. Thalmann, “Initial Architecture for Fast Small-Scale Deployment,” Learning Layers Project, Deliverable D6.1, 2013.
  2. M. Derntl, M. Kravcik, R. Klamma, I. Koren, P. Nicolaescu, D. Renzel, A. Hannemann, M. Shahriari, J. Purma, M. Bachl, E. Bellamy, R. Elferink, V. Tomberg, D. Theiler, and P. Santos, “Customizable Architecture for Flexible Small-Scale Deployment,” Learning Layers Project, Deliverable D6.2, 2014.
  3. R. Klamma, I. Koren, P. Nicolaescu, D. Renzel, M. Kravčík, M. Shahriari, M. Derntl, G. Peffer, and R. Elferink, “DevOpsUse - Scaling Continuous Innovation,” Learning Layers Project, Deliverable D6.3/Report 4, 2015.