A key issue of EMOTIVE is the global resource management, in other words, the global management of the different nodes of the system. For instance, choosing which node will execute each task or deciding the moment to migrate a VM in order to optimize the whole resources of the system. This is done by the scheduling layer which takes into account the overheads added in order to maintain and manage the virtualized environment.  EMOTIVE has different scheduler implementations with different capabilities such as SERA, which supports semantic descriptions, or EERM, which takes into account economical parameters. Currently, an effort is being done in order to develop new schedulers that supports machine learning and a distributed decision system based on agents.

  • Support for different schedulers
    -  Provides an API for developing a Scheduler
  • Current implementations:
    -  SERA: Semantically Enhanced Resource Allocator
    -  EERM: Economically Enhanced Resource Manager
    -  GCS: Machine learning enabled scheduling