Microsoft Technology Licensing, LLC

Ilya Grebnov

Charles Lamanna

Stephen Christopher Siciliano

Kevin Kaiwon Lam

Balasubramanian Shyamsundar

Prabir Krishna Shrestha

Samuel Lenz Banina

Sunay Vaishnav

Himanshu Agrawal

Joon Y Chung

Priti Sambandam

André Miguel Coelho De Oliveira Rodrigues

Embodiments of facilitating creation of cloud computing applications are disclosed therein. In one embodiment, a method includes receiving user input of a plurality of workflow items as a sequence of graphical objects on a graphical user interface. The workflow items individually are related to one or more cloud computing services. The method also includes determining an execution sequence of the plurality of workflow items based on data dependencies individually between pairs of the workflow items, the determined execution sequence being different than the sequence of the graphical objects. The determined execution sequence includes executing pairs of the workflow items without data dependencies therebetween generally in parallel and sequentially executing other pairs of the workflow items with data dependencies therebetween in accordance with the data dependencies.

Designer tool for managing cloud computing services

Computational tasks are mapped with computational locations in a distributed system such as a cloud computing environment. Mapping does not rely on workload estimates. Instead, tasks whose prerequisite tasks or other preconditions are determined to be mutually exclusive are co-located, while other tasks are mapped to different locations than one another. Locations are servers, processor cores, virtual machines, applications, or computational processes, for example. Mutual exclusivity may be determined by detecting that preconditions require different values of a shared variable in order to be satisfied, for example, or determining that preconditions correspond to different branches of a conditional programming statement. A satisfiability engine may also provide a satisfiability determination. Co-located tasks may also be batched, for improved execution performance. Co-location based on mutual exclusivity may result in fewer operations to save and restore task state, fewer cache misses, greater co-allocation of computational resources by the tasks, and easier debugging.

Precondition exclusivity mapping of tasks to computational locations

Methods, systems, apparatuses, and computer program products are provided for developing workflows. An input control may be rendered in association with the rendered representation of a workflow step corresponding to an input parameter. A developer is enabled to interact with the input control to select a parameter value for the input parameter. A workflow is enabled to be developed that includes a push notification workflow step configured to issue a push notification when encountered in the work flow and to wait for a response before enabling the workflow to continue. A workflow saved in a user folder is enabled to be selected for insertion into further workflows. A subscription workflow step may be selected for inclusion in a second workflow that is configured to wait for output information from a subscribed-to workflow before enabling the second workflow to continue.

Intelligent flow designer

A client device includes a processor and memory. An operating system and a client application are executed by the processor and memory. The client application is configured to determine first and second coordinated universal time (UTC) time intervals for a first non-UTC time interval associated with a first event type and a second non-UTC time interval associated with the first event type; generate a single request for the remote activity feed server to aggregate corresponding events during the first UTC time interval and the second UTC time interval; and send the single request via a network interface to the remote activity feed server.

Systems and methods for event aggregation to reduce network bandwidth and improve network and activity feed server efficiency

Aspects of the present invention relate to guaranteeing delivery of replication messages in distributed storage systems. A request to perform an operation may be received. A first replication message corresponding to the request may be created, where the first replication message comprises instructions to replicate the result of the operation to one or more target storages. The replication message may be inserted in a message queue with a delayed visibility. The operation may be performed, where a result of the operation is persisted in a source storage in a data store. Upon determining that the result was successfully persisted in the source storage, a second replication message may be created, where the second replication message comprises instructions to replicate the result of the operation to the one or more target storages. The second replication message may be inserted in the message queue with an immediate visibility.

Guaranteed delivery of replication message

Embodiments are directed to replicating data in distributed storage. A replication message may be retrieved from a message queue associated with a source table. The replication message may include a row identifier. One or more target storages within a same replication group as the source table may be identified. A row from each of the one or more target storages may be obtained corresponding to the row identifier. A winning row may be determined from the obtained rows based on a latest timestamp of the row. A replication operation may be created based on the winning row. The replication operation may be performed on the obtained rows from each of the target storages.

Flow for multi-master replication in distributed storage

Embodiments are directed to determining an optimal number of concurrently running cloud resource instances and to providing an interactive interface that shows projected operational metric measurements. In one scenario, a computer system accesses metric information which identifies operational metric measurements, and further accesses a second portion of metric information that identifies operational metric measurements for the cloud resource instances over a second period of time. The computer system then calculates projected operational metric measurements based on the identified operational metric measurements over the first period of time (e.g. for reactive tuning) and further based on the identified operational metric measurements over the second period of time (e.g. for predictive tuning). The computer system then determines, based on the projected operational metric measurements, a number of cloud resource instances that are to be concurrently running at a specified future point in time.

Predictive load scaling for services

Embodiments are directed to preventing flapping when auto-scaling cloud resources. In one scenario, a computer system accesses information specifying a target operational metric that is to be maintained on a plurality of cloud resources. The computer system determines a current measured value for the target operational metric for at least some of the cloud resources. The computer system further calculates a scaling factor based on the target operational metric and the current measured value, where the scaling factor represents an amount of variance between the target operational metric and the current measured value. The computer system also calculates a delta value representing a modified quantity of cloud resources modified by the calculated scaling factor and determines whether a scaling action is to occur based on the calculated delta value.

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Kirkland, WA, United States of America

Ilya Grebnov