Microsoft Technology Licensing, LLC

Xinhua Ji

Arild Einar Skjolsvold

Alexander Dadiomov

Mayank Agarwal

Maneesh Sah

Jegan Devaraju

Rushi Srinivas Surla

Bradley Gene Calder

Ju Wang

Hazim Shafi

Eric Ledoux

Ralph Burton Harris, Iii

Matthew L Jacobs

Hosung Song

James Rapp

Daniel Griffing

Ben Nesson

Drake A Campbell

Paulo Cesar Sales Janotti

Vikram Padmakar Bapat

Alexey Kamenev

Marcus Swenson

Ryan A Nowak

William T Colburn

Sumadhur Reddy Bolli

Liang Xie

Dengkui Xi

Dengyao Mo

Jared Robert Van Leeuwen

George Essex Englebeck

Ralph Burton Harris Iii

Marcus Kimball Swenson

Techniques are provided for managing resources among clusters of computing devices in a computing system. Resource reassignment message are generated for indicating that servers are reassigned and in response to resource compute loads exceed or fall below certain thresholds. Techniques also include establishing communications with the reassigned servers to assign compute loads without physically relocating the servers from one cluster to another cluster.

Cluster resource management in distributed computing systems

Techniques for managing resources among clusters of computing devices in a computing system are described herein. In one embodiment, a method includes receiving, via a computer network, a resource reassignment message indicating that a server is reassigned from a first cluster to a second cluster and in response to the received resource reassignment message, establishing communications with the server reassigned from the first cluster to the second cluster via the computer network. The method further includes subsequent to establishing communications with the server via the computer network, assigning a compute load to the server reassigned from the first cluster to the second cluster without physically relocating the server from the first cluster to the second cluster.

A system for load-balancing partitions of file servers of a file storage service of a cloud computing system is provided. The system collects latency information relating to access requests to access storage that is partitioned. The access requests are processed by the file servers. Each file server is assigned partitions, which are indexes to the storage. For each file server, the system generates, from the latency information, partition statistics relating to partition latency associated with each partition assigned to that file server. For each partition, the system applies rules to the statistics to determine actions to take to mitigate performance problems. Each rule has a condition and an action. When the condition of the rule is satisfied based on the partition statistics for the partition, the system records an indication to take the action of the rule.

Tracking internal latencies for load balancing of file server

A traffic manager for a distributed data storage system includes an iterative spike identifier to identify N levels of traffic spikes in traffic data on an account basis in a distributed data storage system, where N is greater than zero. A traffic cycle identifier selectively identifies cyclic traffic spikes in at least one of the N levels of traffic spikes for each of a plurality of accounts using autocorrelation and peak detection. A partition manager communicates with the traffic cycle identifier and selectively partitions one of the plurality of accounts based on based on the autocorrelation and the peak detection.

Volatile account identification and isolation and resource management in distributed data storage systems

One or more systems and/or techniques for generating an adaptive tree structure for storing event data based upon data density of events are disclosed. In particular, the adaptive tree structure may comprise one or more levels of nodes, where a level may correspond to a resolution of events. Nodes may correspond to particular time spans over which event data was recorded. A node may be designated as a raw node comprising raw events or a summary node comprising summary events based upon the number of events occurring within a time span covered by the node.

Adaptive tree structure for visualizing data

A method includes determining that a trigger condition of a triggered optimization module of a plurality of optimization modules is met and optimizing scalable storage based on an optimization routine. The optimization routine includes providing a plurality of candidate operations and for a selected optimization module of the plurality of optimization modules that has a higher priority than the triggered optimization module, removing a candidate operation from the plurality of candidate operations that would diminish a modeled state of the scalable storage for the selected optimization module. The optimization routine also includes determining at least one operation of the plurality of candidate operations that would improve the modeled state of the scalable storage for the triggered optimization module and updating the modeled state of the scalable storage to model executing the at least one operation. The method further includes executing the at least one operation.

Load balancing scalable storage utilizing optimization modules

One method includes receiving partitions, generating assignment plans for assignment of the partitions to servers based on dimensional values of dimensions as determined by different assignment heuristics, selecting one of the assignment plans for execution based on analyzing the dimensional values in accordance with the assignment plans, and executing the selected assignment plan on scalable storage. Another method includes determining, for a triggered optimization module that a server is over utilized on a dimension, selecting candidate operations for partitions assigned to the server, for a higher priority optimization module than the triggered optimization module, removing a candidate operation from the candidate operations that would diminish a modeled state of scalable storage, determining an operation of the candidate operations that would improve the modeled state of the scalable storage with respect to a metric of the dimension on the server, and executing the operation on the scalable storage.

Dimension based load balancing

The present invention extends to methods, systems, and computer program products for linking diagnostic visualizations to regions of application code. Diagnostic visualizations emitted during execution of an application are displayed. The diagnostic visualizations partially represent the abstract objective of the application (e.g., as envisioned by a developer). Diagnostic data for at least one of a plurality of components is displayed. The diagnostic data indicates the performance of the at least one of the plurality of components during execution of the application. The displayed one or more diagnostic visualizations and the displayed diagnostic data is correlated to link the one or more diagnostic visualizations to the at least one of the plurality of components. Linking the one or more diagnostic visualizations to the at least one of the plurality of components can better indicate how the application's behavior reconciles the abstract objective.

Linking diagnostic visualizations to application code

A method of providing a visualization of states of a process includes identifying a set of potential states that can occur during the process and a corresponding set of colors, with each color in the set of colors corresponding to one of the potential states. A fixed ordering is established for the set of colors. A timeline graph including a plurality of colored vertical bars is displayed. Each colored vertical bar corresponds to a time interval of the process and includes at least one color selected from the set of colors based on at least one state occurring during the time interval corresponding to the vertical bar. The plurality of colored vertical bars includes a set of multiple-color vertical bars that each include a plurality of colors appearing in a vertical order based on the fixed ordering.

Visualization of states of a process

The present invention extends to methods, systems, and computer program products for calculating zoom level timeline data. Event aggregators are configured to read execution data from sequentially received input buffers and pre-calculate data chunks for a specified zoom level to create a zoom tree. The density of execution data can be used to determine when pre-calculation is beneficial. In response to a user request, pre-calculated data can be combined with responsively calculated data for presentation to a user. User requests can specify zoom levels and time ranges over which data is to be presented.

Calculating zoom level timeline data

The construction or use of a hierarchical time tree that represents an execution time of a program. To construct the hierarchical time tree, the time frame corresponding to the root node is the execution time during which the plurality of call stack samples were gathered from the program. Beginning at the root node, and proceeding recursively, each node is then populated in a manner that all of the call stacks for a given time period need not be accessed in order to provide a report regarding the given time period.

Dynamic calculation of sample profile reports

The present invention extends to methods, systems, and computer program products for time-based navigation within resource utilization data. A computer system is configured to present resource utilization data representing performance of computer resources. The resource utilization data is displayed on a diagnostic data trace during the execution of the application. The user can select a desired time range and the resource utilization data within the time range will be displayed at other traces. The diagnostic data trace is still presented so that the user can understand the relation between the selected time range and the overall time length. Further, the user can modify the selected time range by change the extents of the selected time range using resizing tool. The resource utilization data within the modified selected time range, similarly, is also displayed along with the diagnostic data trace.

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Redmond, WA, United States of America

Xinhua Ji