Microsoft Technology Licensing, LLC

Zhengping Qian

Jingren Zhou

Peter Zabback

Wei Lin

Xiuwei Chen

Fan Yang

Thomas Moscibroda

Zheng Zhang

Feng Zhao

Mark Eugene Russinovich

Guobin Shen

Lidong Zhou

Yuan Yu

Jiaxing Zhang

Li Zhuang

Xiangyao Yu

Ivan Beschastnikh

A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Distributed stream processing in the cloud

Distributed stream processing in the Cloud

The techniques and arrangements described herein provide for updating services, host operating systems and other applications while satisfying update domain constraints. In some examples, one or more controller modules may maintain a data structure including a plurality of server update domains, each server update domain including a set of machines of a plurality of machines of a distributed computing system which may be concurrently updated. The one or more controller modules may allocate the plurality of instances to the plurality of machines such that a number of server update domains is minimized.

Service allocation in a distributed computing platform

Described herein are technologies pertaining to matrix computation. A computer-executable algorithm that is configured to execute perform a sequence of computations over a matrix tile is received and translated into a global directed acyclic graph that includes vertices that perform a sequence of matrix computations and edges that represent data dependencies amongst vertices. A vertex in the global directed acyclic graph is represented by a local directed acyclic graph that includes vertices that perform a sequence of matrix computations at the block level, thereby facilitating pipelined, data-driven matrix computation.

Matrix computation framework

Data that is collected and disseminated by mobile devices typically has to be processed, correlated with other data, aggregated, and then transmitted back to the mobile device users before the information becomes stale or otherwise irrelevant. These operations may be performed in a cloud-based solution that manages dataflow. The cloud-based solutions may be scalable and implemented in a fault-tolerant distributed system to support user-facing continuous sensing and processing services in the cloud-computing system. A system may monitor execution of data and shift workloads (i.e., balancing) in response to spatial and temporal load imbalances that occur in a continuous computing environment. A failure recovery protocol may be implemented that uses a checkpoint-based partial rollback recovery mechanism with selective re-execution, which may allow recovery of the continuous processing after an error while avoiding large amounts of downtime and re-execution.

Platform for continuous mobile-cloud services

Growing community of inventors

Shanghai, China

Zhengping Qian