Microsoft Technology Licensing, LLC

Other

John Morgan Oslake

Pavel Anatolievich Dournov

Vivek Ravindranath Narasayya

Ajay Kalhan

Efstathios Papaefstathiou

Pankaj Arora

Jonathan C Hardwick

Shize Xu

Junfeng Dong

Jiaqi Liu

Changsong Li

Arnd Christian König

Manoj A Syamala

David Edward Guimbellot

Matt Hollingsworth

Glenn R Peterson

Shawn Bice

Narayan Nevrekar

Kevin John Savage

Subramanian Muralidhar

Colleen Elizabeth Hamilton

Jeffrey Robert Currier

Richard Shawn Bice

Kenneth Van Hyning

Praveen Seshadri

Jin Feng

Yi Shan

Ken Van Hyning

Ganesh C Balakrishnan

Manoj Syamala

Arnd Christian KÖNIG

Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Memory management for multiple process instances

Techniques for differential overbooking on a cloud database. These techniques may include determining a reservation amount of a multi-tenant resource for a first service of a based upon an overbooking characteristic of the first service, and determining that a total usage value of the multi-tenant resource by a plurality of services is greater than a threshold value. In addition, the techniques may include determining a service usage value of the multi-tenant resource by the first service, determining a first overage value of the first service based on the service usage value and the reservation amount, and performing a resource reclamation process over the multi-tenant resource based on the first overage value of the first service.

Differential overbooking in a cloud computing environment

Methods for determining cache activity and for optimizing cache reclamation are performed by systems and devices. A cache entry access is determined at an access time, and a data object of the cache entry for a current time window is identified that includes a time stamp for a previous access and a counter index. A conditional counter operation is then performed on the counter associated with the index to increment the counter when the time stamp is outside the time window or to maintain the counter when the time stamp is within the time window. A counter index that identifies another counter for a previous time window where the other counter value was incremented for the previous cache entry access causes the other counter to be decremented. A cache configuration command to reclaim, or additionally allocate space to, the cache is generated based on the values of the counters.

System and method for determining cache activity and optimizing cache reclamation

A solution is disclosed for memory management of serverless databases that includes: based at least on detecting a trigger event, determining whether memory is to be reclaimed; based at least on determining that memory is to be reclaimed, determining an amount of memory to be reclaimed; identifying memory to be reclaimed; and reclaiming the identified memory. Disclosed solutions are flexible, enabling customization of the aggressiveness and manner of memory reclamation. This permits users to specify a tailored balance point between performance and cost, for arrangements that bill users based on resource usage (e.g., memory consumed by a serverless database). In some examples, users specify a ramp-down parameter that is used to determine how much memory can be evicted in a particular reclamation event, time intervals (or another criteria) for triggering a reclamation event, and a definition for whether a cache is active.

Memory management for serverless databases

Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Service level objective for cloud hosted applications

A method includes displaying a plurality of user modifiable utilization views of a monitored software application, where each of the plurality of user modifiable utilization views is customizable to fit a domain of analysis by identifying one or more resource types of multiple resource components. Each of the plurality of user modifiable utilization views is displayed in one more graphical user interfaces. The method includes determining a desired ordering of the one or more resource types within a hierarchy of types and filtering one or more details of the multiple resource components to create a customized view. A user can navigate between each of the plurality of user modifiable utilization views to dynamically construct related target analysis views including filtered details across one or more other domains of analysis.

Visualization for resource capacity management

A method includes determining a total storage input/output capacity of a set of storage elements available for use by a software application located on an operating system volume of a computer system. The total storage input/output capacity is determined by evaluating storage input/output capacity of the storage elements at a physical device level and at a file level. The method includes measuring a number of input/output storage operations performed on behalf of the software application. The method also includes determining a storage input/output utilization of the software application based on the measured number of input/output storage operations as compared to the total storage input/output capacity of the set of storage elements.

Storage input/output utilization associated with a software application

Systems and methods for managing database applications are disclosed. A system includes a fabric that identifies a set of data-tier application components. Each of the data-tier application components includes a logical representation of a collection of database elements. The fabric identifies a set of database runtime resources hosting the set of data-tier application components, and the fabric identifies computing resources used by the set of database runtime resources to host the set of data-tier application components. The system also includes a management point to receive a fabric policy. One or more actions of the fabric policy are automatically applied to affected entities identified by the fabric to bring fabric elements into compliance with the fabric policy.

Data-tier application component fabric management

A system is disclosed that includes an interface to receive a resource governance rule applicable to one or more hardware resources supporting a monitored software application. The system includes utilization logic to measure usage of a particular hardware resource by the monitored software application. An available utilization of the particular hardware resource is determined based on the resource governance rule, and a total utilization of the particular hardware resource is also determined. One or more utilization metrics are determined based on historical usage of the particular hardware resource as compared to a governed capacity and a non-governed capacity available to the particular hardware resource. The system also includes recommendation logic to determine a recommended action to be taken with respect to the monitored software application.

Expert system and visualization for multi-server capacity management

Aspects of the subject matter described herein relate to modeling and simulating software contention. In aspects, a model declares properties of a scarce software object and models software components that need the scarce software object to execute. A simulator uses the model to set up data structures that track locking activity related to the software object. The simulator simulates the interaction between software components and hardware including seizing and releasing locks on the software object. The simulator may use a function or other similar mechanism to distribute seize and release operations with respect to a lock across multiple instances of the software object. The simulator may use data generated by a simulation to provide an indication of lock objects and hardware utilization, transaction latencies, and other statistics.

Modeling and simulating software contention

Simulating an application. A method that may be practiced in a computing environment configured for simulating an application modeled by an application model deployed in a performance scenario of a computing system by deploying service models of the application model to device models modeling devices. The method includes referencing a performance scenario to obtain a transaction being modeled as originating from a first device model. The transaction invokes of a first service model. The first service model specifies hardware actions for simulation. The first service model is referenced to determine the hardware actions for simulation and the next referenced service. The next referenced service specifies hardware actions to be added to the transaction and may specify invocation of other service models. A chain of hardware actions is generated by following the invocation path of the service models. The hardware actions are applied to device models to simulate the transaction.

Dynamic software performance models

Operational resource modeling is usable to analyze application and computer system performance over a wide range of hypothetical scenarios. Operational resource modeling involves creating and training one or more resource models, and/or simulating hypothetical scenarios using resource models.

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Seattle, WA, United States of America

John Morgan Oslake