Elastra connects the power use in the data center to the application architects and deployment decision makers.
Plan Composer function lets customers set their own policies based on application needs and specific power metrics (such as wattage, PUE, number of cores, etc.). Therefore, if an application requires 4GB of RAM and two cores for optimal performance, and if the customer is concerned with straight wattage, Elastra’s product will automatically route it to the lowest-power 4GB, dual-core virtual machine available.
Gigaom has a post on Elastra’s Cloud Computing infrastructure addressing greener services.
Elastra has incorporated energy efficiency intelligence into its Cloud Server solution, allowing customers to define which efficiency metrics are important to them and then rely on the software to route each application to the optimal resources with their internal cloud environments. Elastra’s efforts are just the latest in a growing trend toward saving data center costs by using the least possible amount of power to accomplish any given task. Especially in the internal cloud space, power management capabilities are becoming a must-have, with vendors from Appistry to VMware offering tools to migrate workloads dynamically and power down unneeded servers.
Digging into the press release I found Elestra uses a modeling approach.
Elastra accomplishes this through two technologies available in the product. The first technology is the ECML and EDML semantic modeling languages. ECML is a language used to describe an application (software, requirements, and policies) and EDML is used to describe the resources (virtual machines, storage, and network) available in a data center. These languages can be easily extended to enhance the definition of the applications and resources.
These modeling languages coupled with the Plan-Composer in the Elastra Cloud Server enables users to synthesize a plan for execution. The Plan-Composer analyzes the proposed application designs (expressed thru ECML) and data center resources (expressed thru EDML), comparing them against a library of actions and outcomes. It then generates a plan based on the energy efficiency policies of the organization that can be executed by the Cloud Server against a customer’s infrastructure.
The cool part is Elestra uses OWL and RDF to support their modeling approach.
Elastic Modeling Languages
The Elastic Modeling Languages are a set of modular languages, defined in OWL v2, that express the end-to-end design requirements, control and operational specifications, and data centre resources & configurations required to enable automated application deployment & management.
While the foundation of the modeling languages is in OWL and RDF, developers can interoperate with the Elastra Cloud Server through its RESTful interfaces; all functions available to the Elastra Workbench are available through this interface, which are based on Atom collections and serialized JSON, XML, or RDF (XML or Turtle) entries.
Declarative models are useful ways to drive complexity out of IT application design and configuration, in favor of more concise statements of intent. Given a declaration of preferences or constraints, an IT management system can compose multiple models together much more effectively than if the models were predominantly procedural, and also formally verify for conflicts or mistakes. On the other hand, not everything can be declarative; at some point, procedures are usually required to specify the “last mile” of provision, installation, or configuration.