Abstract:
A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.
Abstract:
A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.
Abstract:
A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.
Abstract:
A system and method for autoscaling a multi-tier application, that has components executing on a plurality of tiers of a virtual data center, allocates resources to each of the plurality of tiers based on cost and performance. An application performance is determined, and a new application performance is estimated based at least partially on an application reservation and an application limit. An optimized utility of the application is calculated based on the cost to execute the application, the application reservation, and the application limit. A scaling factor for each tier is then determined to scale up or down a number of virtual machines operating in each of the tiers.
Abstract:
A placement simulator is used for testing a placement engine in a virtual machine environment. The placement simulator includes a simulation controller, an event manager, and an inventory manager. The simulation controller receives input data for a simulated datacenter. The event manager invokes event handlers for a sequence of events from the input data. The inventory manager stores states of inventory objects to simulate deployment of virtual infrastructure resources by the placement engine based on the sequence of the events.
Abstract:
A system and method for performing automatic remediation in a distributed computer system with multiple clusters of host computers uses the same placement selection algorithm for initial placements and for remediation placements of clients. The placement selection algorithm is executed to generate a placement solution when a remediation request in response to a remediation-requiring condition in the distributed computer system for at least one client running in one of the multiple clusters of host computers is detected and a remediation placement problem for the client is constructed. The placement solution is then implemented for the client for remediation
Abstract:
A system and method for autoscaling a multi-tier application, that has components executing on a plurality of tiers of a virtual data center, allocates resources to each of the plurality of tiers based on cost and performance. An application performance is determined, and a new application performance is estimated based at least partially on an application reservation and an application limit. An optimized utility of the application is calculated based on the cost to execute the application, the application reservation, and the application limit. A scaling factor for each tier is then determined to scale up or down a number of virtual machines operating in each of the tiers.
Abstract:
The current document is directed to an analysis subsystem within a large distributed computing system, such as a virtual data center or cloud-computing facility, that monitors the operational states associated with a multi-tiered application and provides useful information for determining one or more causes of various types of failures and undesirable operational states that may arise during operation of the multi-tiered application. In one implementation, the analysis subsystem collects metrics provided by various different types of metrics sources within the computational system and employs principal feature analysis to select a generally small subset of the collected metrics particularly relevant to monitoring a multi-tiered application and diagnosing underlying causes of operational states of the multi-tiered application. The analysis subsystem develops one or more conditional probability distributions with respect to the subset of metrics. These one or more conditional probability distributions, in turn, allow the analysis subsystem to provide useful information for analysis of the causes of failures and undesirable system states associated with the multi-tiered application.
Abstract:
A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.
Abstract:
A system and method for autoscaling a multi-tier application, that has components executing on a plurality of tiers of a virtual data center, allocates resources to each of the plurality of tiers based on cost and performance. An application performance is determined, and a new application performance is estimated based at least partially on an application reservation and an application limit. An optimized utility of the application is calculated based on the cost to execute the application, the application reservation, and the application limit. A scaling factor for each tier is then determined to scale up or down a number of virtual machines operating in each of the tiers.