Difference between revisions of "Nova solver scheduler"
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A new OpenStack Nova scheduler driver based on constraints-based optimization solvers. | A new OpenStack Nova scheduler driver based on constraints-based optimization solvers. | ||
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* Blueprints: https://blueprints.launchpad.net/nova-solver-scheduler | * Blueprints: https://blueprints.launchpad.net/nova-solver-scheduler | ||
− | + | == Overview == | |
Nova Solver Scheduler is an OpenStack Nova scheduler driver that provides smart, efficient, and optimization based compute resource scheduling in OpenStack. It is a pluggable scheduler driver, that can leverage existing constraint solvers available in open source such as PULP, CVXOPT, Google OR-TOOLS, etc. It can be easily extended to add complex constraint models for various use cases, and to solve complex scheduling problems with pulggable solving frameworks. | Nova Solver Scheduler is an OpenStack Nova scheduler driver that provides smart, efficient, and optimization based compute resource scheduling in OpenStack. It is a pluggable scheduler driver, that can leverage existing constraint solvers available in open source such as PULP, CVXOPT, Google OR-TOOLS, etc. It can be easily extended to add complex constraint models for various use cases, and to solve complex scheduling problems with pulggable solving frameworks. |
Latest revision as of 15:02, 4 June 2015
A new OpenStack Nova scheduler driver based on constraints-based optimization solvers.
- Source: http://git.openstack.org/cgit/stackforge/nova-solver-scheduler
- Bugs: https://bugs.launchpad.net/nova-solver-scheduler
- Blueprints: https://blueprints.launchpad.net/nova-solver-scheduler
Overview
Nova Solver Scheduler is an OpenStack Nova scheduler driver that provides smart, efficient, and optimization based compute resource scheduling in OpenStack. It is a pluggable scheduler driver, that can leverage existing constraint solvers available in open source such as PULP, CVXOPT, Google OR-TOOLS, etc. It can be easily extended to add complex constraint models for various use cases, and to solve complex scheduling problems with pulggable solving frameworks.