Microsoft Technology Licensing, LLC

Willis Lang

Justin G Moeller

Ajay Kalhan

Alan D Halverson

Nikhil Teletia

Karthik Saligrama Ramachandra

Ya Lin

Zi Ye

Prashant Varanasi

Karthik Raman

Hideaki Kimura

Peter Alan Carlin

Srinath Shankar

Qun Guo

Shize Xu

Jose Manuel Picado Leiva

David Johns DeWitt

Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

Database simulation modeling framework

Testing methods and systems are provided for testing a resource manager of an application management system. The testing systems include a load orchestrator configured to obtain an artificial metric that is determined based on a utilization model (e.g., CPU usage, memory allocation, or disk usage, number of webserver sessions). The load orchestrator transmits the artificial metric to applications in a cluster of computing nodes. The applications transmit the artificial metric to the resource manager. In response, the resource manager generates control output for managing applications in the cluster based on the artificial metric (e.g., scaling, load balancing, application placement, failover of applications, or defragmenting data). The utilization model may include executable code for generating artificial metric values. The model may be received as a result of an API call. The load orchestrator may be instantiated in an orchestration framework or in each node of the cluster.

Testing of a resource manager of an application management system

Distributed query processing is often performed by a set of nodes that apply MapReduce to a data set and materialize partial results to storage, which are then aggregated to produce the query result. However, this architecture requires a preconfigured set of database nodes; can only fulfill queries that utilize MapReduce processing; and may be slowed down by materializing partial results to storage. Instead, distributed query processing can be achieved by choosing a node for various portions of the query, and generating customized code for the node that only performs the query portion that is allocated to the node. The node executes the code to perform the query portion, and rather than materializing partial results to storage, streams intermediate query results to a next selected node in the distributed query. Nodes selection may be involve matching the details of the query portion with the characteristics and capabilities of the available nodes.

Per-node custom code engine for distributed query processing

A method of bridging a first database and a second database. The method includes maintaining a state machine representing a state of a virtual node in the first database, wherein the state of the virtual node conforms to a native protocol for native nodes of the first database, said native protocol of the first database differing from a foreign protocol of the second database. The method further includes receiving an incoming message for the virtual node from one of the native nodes according to the native protocol, and based on the incoming message, accessing the second database. The method further includes updating the state of the virtual node based on the incoming message according to the native protocol, and based on the state of the virtual node as updated, sending an outgoing message to one or more of the native nodes according to the native protocol.

Controller for bridging database architectures

Computer resources are provisioned for a virtual machine based on expected lifespan. After a request to create a virtual machine is received, the virtual machine can be classified into one of a plurality of longevity classes utilizing a machine learning classifier based on data pertaining to the requestor or the virtual machine. The longevity classes capture different lifespans of the virtual machine between when the virtual machine is created and when the virtual machine is deleted. Subsequently, resources for the virtual machine are provisioned from a hardware resource pool of a set of disjoint resource pools specific to the longevity class of the virtual machine.

Longevity based computer resource provisioning

Managing cluster resources for a cluster. The method includes identifying a plurality of quiescence policies. Each of the quiescence policies define one or more conditions for cluster entities which when met, identify that cluster resources should be reclaimed from cluster entities by quiescing cluster entities. The method further includes, for each quiescence policy, identifying an amount of reclaimed capacity defining the amount of cluster resources that will be reclaimed by implementing the quiescence policy. The method further includes, for each quiescence policy, identifying an amount of reserve capacity defining the amount of cluster resources that should be reserved for resuming quiesced cluster entities. The method further includes; selecting a quiescence policy based on comparing the reclaimed capacities and reserve capacities for the quiescence policies. The method further includes, booking cluster resources for cluster entities on the cluster according to the selected quiescence policy.

Modeling and forecasting reserve capacity for overbooked clusters

Defragmenting a cluster service to service additional capacity requests on the service. A method includes determining an amount of server resources needed for an additional deployment reservation request for a new deployment or increasing reservation of resources of an existing deployment. The method further includes determining a server that currently does not have capacity to service the additional deployment reservation request. The method further includes determining how resources on the server can be freed up by moving other replicas of other deployments on the server to other servers to allow the server to service the additional deployment reservation request.

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Madison, WI, United States of America

Willis Lang