Main objectives

Architectural building blocks

  • processing on the edge, including cloud computing infrastructure and edge resources
  • orchestration Of distributed processing nodes, cope with sudden changes in Big Data
  • self-adaptation to real-time changes, sense and cope with dynamics in velocity and variety
  • proactivity regarding the anticipation of need for changes in the processing infrastructure

Technical objectives

  • network virtualization
  • dynamic monitoring in real-time processing architectures for Big Data
  • situation-aware and context-driven adaptation recommender systems
  • real-time mobile stream processing
  • pro-active cloud computing

Network virtualization

    Intersite network virtualization and security management:

  • combine hardware and software network resources into a single administrative unit, including multi-cloud environments from by different cloud providers
  • focus on usage control and access control technologies on different operational layers

Going beyond state-of-the-art

  • deploy a virtualized OpenvSwitch infrastructure to build a set of virtual networks
  • reinforced security at network level
  • formulate firewalls and access detection rules
  • control the SDN (software-defined network) controllers via a virtual network orchestrator
  • obtain statistics about network usage and provide meaningful input to the resource manager

Dynamic monitoring in real-time Big Data processing architectures

    Multi-layer cloud resource management and monitoring:

  • monitor communication scenarios between different laaS cloud resource providers, mobile phones, embedded devices and generally resources at the edge
  • coordinate and orchestrate distributed resources

Going beyond state-of-the-art

    Detection of anomalous situations on the fly (not predefined anomalies):

  • combine predective capabilities with the ability to recommend cloud resource adaptations
  • develop a new semantic-based model for distributed real-time processing architectures
  • monitor the real-time processing architecture
  • support the meta-modelling of the adaptation process
  • enable real-time changes of the processing pipeline

Situation-aware and context-driven adaptation recommender systems

    Distribution management:

  • allow for the definition of distribution constraints for proper behavior of data-intensive cloud applications (examples: response time, security constraints, …)
  • constraints to be expressed during design-time, further refinement by extensions when instantiating an application, verified in real-time
  • together with recommendations on new distributed opportunities

Going beyond state-of-the-art

    Combine predictive capabilities with the ability to recommend cloud resource adaptations:

  • develop a big data situation metamodel that can model situations relevant to cloud and edge resources topology, status and generic capabilites
  • propose algorithms for devising proactive adaptation actions

Real-time mobile stream processing

    Adaptive scheduling of IoT big data processing tasks between devices and the cloud:

  • recommendations for the scheduling mechanisms to be given depending on the context and the situation
  • scheduling system support by a mobile context analyzer, a situation detection mechanism, a resource adaptation recommender and a data-intensive application recommender

Going beyond state-of-the-art

    Use conventional big data analytics and integrate promising concepts of edge computing:

  • investigate the concept of bandwidth and medium utilization and decide on edge, cloud or hybrid computing
  • investigate on acceleration concepts for a local analysis
  • global analytics control local analytics and business rules

Pro-active cloud computing

    Pro-adaptive cloud adaptation:

  • realization of the integrated platform that adapts the mapping of the real-time processing network on the cloud / edge according to the dynamics of the workload
  • workload dynamics are seen as variations of the data size or heterogeneity of the resources and will be recognized by a situation detection mechanism and a workload predictor

Going beyond state-of-the-art

    Autonomous cloud management platform („proactive cloud automation”) that:

  • provides a workflow catalogue system for provisioning and deployment workflows,
  • using a scalable scheduler,
  • the ability to connect to a variety of cloud providers