Abstract: Techniques for installing agents on host computing systems in data centers are disclosed. In one example, load information and resource capability associated with a host computing system in a data center may be determined. Further, a maximum number of concurrent installations to be performed on the host computing system may be determined based on the load information and the resource capability. Furthermore, a channel with the maximum number of concurrent installations may be configured for the host computing system and agents may be installed on the host computing system based on the configured channel.

Abstract: The current document is directed to improved distributed service-oriented applications developed according to a new and improved architecture for developing distributed service-oriented applications. The new and improved architecture includes a stateless-communications-protocol interface to external users and clients, services implemented by actors that communicate using message passing, and a distributed data grid for persistent storage of data. Distributed service-oriented applications developed according to the new and improved architecture are referred to as “RAD-squared applications” (“RAD{circumflex over (?)}2 applications”). The acronym “RAD{circumflex over (?)}2” stands for “Rapid Application Development with REST-actor-data-grid” and the acronym “REST” stands for the Representational State Transfer (“REST”) protocol. Alternative stateless communications protocols can be used as alternatives to REST in RAD{circumflex over (?)}2 applications.

Abstract: A content driven service discovery and agent monitoring capabilities on Managed Endpoints methodology and system is disclosed. In a computer-implemented method, content information corresponding to an agent of a monitoring system is generated. Content information is pushed to the agent. The content information is used to alter the agent such that an altered agent is generated. The altered agent is generated without requiring a complete update of the agent.

Abstract: In one example, a system may include an application monitoring server and an endpoint in communication with the application monitoring server. Example endpoint may include an agent to collect performance metrics associated with a program running in the endpoint and a discovery unit in communication with the agent. The discovery unit may include a metric parser to receive the performance metrics in a source format and parse the received performance metrics. Further, the discovery unit may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. The plurality of resources may be associated with an application being executed in the endpoint. Furthermore, the discovery unit may include a transmitting unit to transmit the performance metrics and first information associated with the plurality of resources to the application monitoring server via a network.

Abstract: In one example, a system may include an application monitoring server and an endpoint in communication with the application monitoring server. Example endpoint may include an agent to collect performance metrics associated with a program running in the endpoint and a discovery unit in communication with the agent. The discovery unit may include a metric parser to receive the performance metrics in a source format and parse the received performance metrics. Further, the discovery unit may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. The plurality of resources may be associated with an application being executed in the endpoint. Furthermore, the discovery unit may include a transmitting unit to transmit the performance metrics and first information associated with the plurality of resources to the application monitoring server via a network.

Abstract: In one example, an application monitoring server may include a metric parser to receive performance metrics associated with an endpoint. Example performance metrics may be in a source format. Further, the metric parser may parse the received performance metrics. Furthermore, the application monitoring server may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. Example plurality of resources may be associated with an application being executed in the endpoint. Further, the resource extractor may present information associated with the plurality of resources on a graphical user interface.

Abstract: In one example, an application monitoring server may include a metric parser to receive performance metrics associated with an endpoint. Example performance metrics may be in a source format. Further, the metric parser may parse the received performance metrics. Furthermore, the application monitoring server may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. Example plurality of resources may be associated with an application being executed in the endpoint. Further, the resource extractor may present information associated with the plurality of resources on a graphical user interface.

Abstract: A content driven service discovery and agent monitoring capabilities on Managed Endpoints methodology and system is disclosed. In a computer-implemented method, content information corresponding to an agent of a monitoring system is generated. Content information is pushed to the agent. The content information is used to alter the agent such that an altered agent is generated. The altered agent is generated without requiring a complete update of the agent.

Abstract: Techniques for installing agents on host computing systems in data centers are disclosed. In one example, load information and resource capability associated with a host computing system in a data center may be determined. Further, a maximum number of concurrent installations to be performed on the host computing system may be determined based on the load information and the resource capability. Furthermore, a channel with the maximum number of concurrent installations may be configured for the host computing system and agents may be installed on the host computing system based on the configured channel.

Abstract: Methods and systems for a networked storage environment are provided. One method includes using a storage service level (SSL) object and a protection service level (PSL) object for storing a plurality of configurable attributes by a processor. A combination of the configurable attributes define service level classes (SLCs) in a networked storage system using a plurality of resources. The SSL object and the PSL object use a same schema and naming convention for defining the plurality of configurable attributes regardless of configuration attribute type and resource type. The method also includes obtaining by the processor, attributes defining a second SLC for a storage volume assigned to a first SLC; and updating by the processor, a quality of service (QOS) policy for the storage volume for using a resource that meets attributes of the second SLC. The storage volume is assigned the resource to process requests for data.

Abstract: Methods and systems for a networked storage environment are provided. One method includes using a storage service level (SSL) object and a protection service level (PSL) object for storing a plurality of configurable attributes by a processor. A combination of the configurable attributes define service level classes (SLCs) in a networked storage system using a plurality of resources. The SSL object and the PSL object use a same schema and naming convention for defining the plurality of configurable attributes regardless of configuration attribute type and resource type. The method also includes obtaining by the processor, attributes defining a second SLC for a storage volume assigned to a first SLC; and updating by the processor, a quality of service (QOS) policy for the storage volume for using a resource that meets attributes of the second SLC. The storage volume is assigned the resource to process requests for data.

Abstract: Methods and systems for a networked storage environment are provided. One method includes storing a plurality of configurable attributes at a data structure by a processor, where a combination of the configurable attributes is used to define a custom service level class (SLC) in a networked storage environments using a plurality of resources for storing data; receiving a request by the processor to create the custom SLC based of a set of configurable attributes; identifying a resource of the networked storage environment that meets the requirements defined by the attributes of the custom SLC; and configuring the resource for meeting the requirements of the custom SLC.

Abstract: Methods and systems for a networked storage environment are provided. One method includes storing a plurality of configurable attributes at a data structure by a processor, where a combination of the configurable attributes is used to define a custom service level class (SLC) in a networked storage environments using a plurality of resources for storing data; receiving a request by the processor to create the custom SLC based of a set of configurable attributes; identifying a resource of the networked storage environment that meets the requirements defined by the attributes of the custom SLC; and configuring the resource for meeting the requirements of the custom SLC.

Abstract: Methods and systems for a networked storage environment are provided. One method includes storing a plurality of configurable attributes at a data structure by a processor, where a combination of the configurable attributes is used to define a custom service level class (SLC) in a networked storage environments using a plurality of resources for storing data; receiving a request by the processor to create the custom SLC based of a set of configurable attributes; identifying a resource of the networked storage environment that meets the requirements defined by the attributes of the custom SLC; and configuring the resource for meeting the requirements of the custom SLC.

Abstract: A Composite Pattern with BLOB data types is used to model a hierarchical network, and includes a path-like construct for locating each component within the network model. Database procedures are used to efficiently search, modify and retrieve individual nodes from the network model using the database server's memory pool so that client applications are not required to retrieve and deserialize the entire Composite-BLOB hierarchy in order to make modifications or search for individual elements, thereby substantially reducing the transfer of data between the application layer and database. To avoid the need for dynamic memory restructuring during deserialization, the size required to store component data at each composite is stored when the composite is serialized, and during deserialization, the size is retrieved and used to obtain sufficient memory for the deserialized composite.

Abstract: A Composite Pattern with BLOB data types is used to model a hierarchical network, and includes a path-like construct for locating each component within the network model. Database procedures are used to efficiently search, modify and retrieve individual nodes from the network model using the database server's memory pool so that client applications are not required to retrieve and deserialize the entire Composite-BLOB hierarchy in order to make modifications or search for individual elements, thereby substantially reducing the transfer of data between the application layer and database. To avoid the need for dynamic memory restructuring during deserialization, the size required to store component data at each composite is stored when the composite is serialized, and during deserialization, the size is retrieved and used to obtain sufficient memory for the deserialized composite.