Microsoft Technology Licensing, LLC

Weiping Hu

Marwan E Jubran

Aleksandr Mikhailovich Gershaft

Lin Xu

Vinod Grover

Vitalii Tsybulnyk

Sanjay Ramanujan

Yongkang Zhu

Andrew Tianze Wang

Xiaoguang Fan

Various methods and systems for implementing dependency management for an infrastructure are provided. A dependency service manager of the dependency management system includes a plurality of dependency management system components that facilitate automatically discovering and analyzing data to generate dependency data. The dependency data includes dependencies and dependents of dependency service tenants which are tenant services of the infrastructure. The dependency data is generated based on data retrieved by a plurality of collectors. A collector from the plurality of collectors is an agent that retrieves data associated with dependency service tenants based on a corresponding collection time attribute of the collector. A collector accesses data for generating the dependency data at one of the following collection times: design time, deployment time and runtime. The dependency data that is generated is then stored in a data store and communicated to a dependency service interface for user access to the dependency data.

Distributed computing dependency management system

An automated root-cause analysis (RCA) system may provide a fully automated platform that provides dependency and execution order modeling for tasks included in a capacity provisioning process, anomaly detection, ticket correlation, root-cause analysis, monitoring and feedback, and data visualization. The automated RCA system may continuously collect and store data for use in determining a root cause of a blockage on a capacity provisioning process. The blockage may be identified in a ticket generated by a cloud-computing system. The automated RCA system may receive the ticket and attempt to determine the root cause of the blockage based on root causes associated with previous tickets generated by the cloud-computing system. The automated RCA system may identify a true root cause, recommend repair items based on the true root cause, identify one or more responsible teams to drive a fix, and provide an estimated time for completion.

Automatic root cause analysis and prediction for a large dynamic process execution system

A method for nonintrusive network load generation may include determining available resources in a distributed computing system, where the distributed computing system includes a plurality of computing devices and a target deployment. Based on an amount of available resources between the target deployment and a plurality of source computing devices, the plurality of source computing devices may be selected to generate a network load directed from the plurality of source computing devices to the target deployment. The plurality of source computing devices may be a subset of the plurality of computing devices in the distributed computing system. A network-traffic generator service may be provided to the plurality of source computing devices in order to generate the network load directed from the plurality of source computing devices to the target deployment. The performance of the distributed computing system in response to the generated network load may be monitored.

Nonintrusive dynamically-scalable network load generation

Methods, systems, and computer-readable media for automatically configuring an inventory of hardware to interact seamlessly with a datacenter are provided. Initially, customer-specific specifications are collected from a user, which are automatically supplemented with platform-specific specifications. These specifications are used to select the hardware inventory from a set of predefined hardware clusters, where each of the predefined hardware clusters represents compatible configurations of hardware assets and logical assets that have been demonstrated to function congruently. A cluster-configuration file is derived from data held within a stock-keeping unit (SKU) that describes the selected hardware inventory. The cluster-configuration file is populated with abstract symbolic representations that act as placeholders for expected values that are presently unknown. Network resources are assigned by automated conversion of the abstract symbolic representations into concrete values. The cluster-confirmation file is merged with previous versions of datacenter inventory to create an updated version of the datacenter inventory.

Establishing an initial configuration of a hardware inventory

Embodiments are directed to tracking variable location information in optimized code and efficiently collecting and storing reaching definition information. A computer system receives a portion of source code at a compiler, where the compiler is configured to compile and optimize the source code for execution. The computer system tags selected variables in the source code with a tag, where the tag is configured to provide location information for the variable. The computer system optimizes the received portion of source code including changing at least one of the tagged variables. The computer system also tracks the tagged variables as the variables are changed by the compiler during code optimization and persists the variable location information, so that the persisted variable location information is available to other compiler components.

Tracking variable information in optimized code

Intermediate representations of computer code are efficiently generated. More particularly, methods described herein may be used to construct a static single assignment representation of computer code without unnecessary phi-function nodes. Potentially necessary phi-function node assignments may be analyzed to determine whether they directly reach a non-phi use or a necessary phi-use of a corresponding variable. Those that ultimately reach such a use may be determined to be necessary and a pruned static single assignment may be constructed by including those potentially necessary phi-functions determined to be in fact necessary. Also, some phi-function nodes may be determined to be necessary based on their dependency relationship to other phi-functions previously determined to be necessary (e.g., because they directly reach a non-phi use). A phi-function dependency graph may be used to record dependency relationships between phi-function nodes. The analysis can proceed during a forward walk of a control flow representation of the program.

Growing community of inventors

Bellevue, WA, United States of America

Weiping Hu