Microsoft Technology Licensing, LLC

Srikanth Kandula

Albert Gordon Greenberg

Paramvir Bahl

Changhoon Kim

Deepak Bansal

Ming Zhang

Ratul Mahajan

Alireza Dabagh

Yousef A Khalidi

Ranveer Chandra

David A Maltz

Ishai Menache

Ganesh Ananthanarayanan

Sriram S Rao

Peter Bodik

Hongqiang Liu

Behnaz Arzani

Pooria Namyar

Jitendra Dattatraya Padhye

Daniel Sebastian Berger

Avrilia Floratou

Ashvin Agrawal

Joseph Naor

Christopher William Douglas

Tsu-wang Hsieh

Virajith Jalaparti

Xin Jin

Anand Padmanabha Iyer

Santiago Martin Segarra

Roy Schwartz

Qifan Pu

Srinivasa Aditya Akella

Mainak Ghosh

Daniel Stopol Crankshaw

Surajit J Chaudhuri

Sharad Agarwal

Yi Lu

Bongshin Lee

Nicolas Bruno

Jingren Zhou

Himanshu Raj

Ryan Andrew Beckett

Bolin Ding

Eric Boutin

Ming-Chuan Wu

Milan Vojnovic

Konstantinos Karanasos

Yao Lu

Jeffrey Thomas Rasley

Edward David Harris

Vijay Gill

Tsuwang Hsieh

Robert Grandl

Bikas Saha

Chi-Yao Hong

Beibin Li

Sathiya Kumaran Mani

Anil Atmanand Shanbhag

Aleksandar Vitorović

Matthaios Olma

Alan Shieh

Rodrigo Lopes Cancado Fonseca

Andrew D Ferguson

Sameer Agarwal

Zhicheng Liu

Mohan Nanduri

Roger Peter Wattenhofer

Patrick D Verkaik

Paramivir Bahl

Securing and optimizing communications for a cloud service provider includes collecting connection summary information at network interface devices associated with host computing devices for a group of resources allocated to a customer of the cloud computing environment. The connection summary information includes local address information, remote address information, and data information, each connection established via the network interface devices. At least one communication graph is generated for the group of resources using the connection summary information. The graph includes nodes that represent communication resources of the group of resources and edges extending between nodes that characterize communication between the nodes. At least one analytics process is performed on data from the graph to identify at least one of a micro-segmentation strategy, a communication pattern, and a flow prediction for the group of resources.

Communication visualization and analytics system for public clouds

A method of updating a trained cardinality estimation model includes receiving a cardinality estimation model with cardinality labels and detecting a drift in underlying data or predicates of the cardinality estimation model. The type of the detected drift is determined and new test queries that mimic test queries for the detected drift are synthesized. A portion of the synthesized test queries is selected to reduce annotation cost and used to update the cardinality estimation model.

Adapting learned cardinality estimators to data and workload drifts

A computing device is provided, including a processor that receives a network graph. The processor further receives a specification of a network traffic control heuristic for a network traffic routing problem over the network graph. The processor further constructs a gap maximization problem that has, as a maximization target, a difference between an exact solution to the network traffic routing problem and a heuristic solution generated using the network traffic control heuristic. The processor further generates a Lagrange multiplier formulation of the gap maximization problem. At a convex solver, the processor further computes an estimated maximum gap as an estimated solution to the Lagrange multiplier formulation of the gap maximization problem. The processor further performs a network traffic control action based at least in part on the estimated maximum gap.

Network traffic control using estimated maximum gap

A computing system identifies mitigation actions in response to failures within a computer network. A service level objective is obtained by the computing system for client-resource data flows traversing the computer network between client-side and resource-side nodes. Indication of a failure event at a network location of the computer network is obtained. For each mitigation action of a set of candidate mitigation actions, an estimated impact to a distribution of the service level objective is determined for the mitigation action by applying simulated client-resource data flows to a network topology model of the computer network in combination with the mitigation action and the failure event. One or more target mitigation actions are identified by the computing system from the set of candidate mitigation actions based on a comparison of the estimated impacts of the set of candidate mitigation actions.

Impact-aware mitigation for computer networks

Methods, systems, apparatuses, and computer program products are provided for prefetching data. A workload analyzer may identify job characteristics for a plurality of previously executed jobs in a workload executing on a cluster of one or more compute resources. For each job, identified job characteristics may include identification of an input dataset and an input bandwidth characteristic for the input dataset. A future workload predictor may identify future jobs expected to execute on the cluster based at least on the identified job characteristics. A cache assignment determiner may determine a cache assignment that identifies a prefetch dataset for at least one of the future jobs. A network bandwidth allocator may determine a network bandwidth assignment for the prefetch dataset. A plan instructor may instruct a compute resource of the cluster to load data to a cache local to the cluster according to the cache assignment and the network bandwidth assignment.

Cache and I/O management for analytics over disaggregated stores

Embodiments for efficient queue management for cluster scheduling and managing task queues for tasks which are to be executed in a distributed computing environment. Both centralized and distributed scheduling is provided. Task queues may be bound by length-based bounding or delay-based bounding. Tasks may be prioritized and task queues may be dynamically reordered based on task priorities. Job completion times and cluster resource utilization may both be improved.

Efficient queue management for cluster scheduling

The techniques and/or systems described herein are configured to determine a set of update operations to transition a network from an observed network state to a target network state and to generate an update dependency graph used to dynamically schedule the set of update operations based on constraint(s) defined to ensure reliability of the network during the transition. The techniques and/or systems dynamically schedule the set of update operations based on feedback. For example, the feedback may include an indication that a previously scheduled update operation has been delayed, has failed, or has been successfully completed.

Dynamic scheduling of network updates

Various technologies pertaining to scheduling network traffic in a network are described. A request to transfer data from a first computing device to a second computing device includes data that identifies a volume of the data to be transferred and a deadline, where the data is to be transferred prior to the deadline. A long-term schedule is computed based upon the request, wherein the long-term schedule defines flow of traffic through the network over a relatively long time horizon. A short-term schedule is computed based upon the long-term schedule, where devices in the network are configured based upon the short-term schedule.

Computing long-term schedules for data transfers over a wide area network

Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Latency reduction with pre-moving of distributed data and adaptive allocating of compute operations

One or more approximations of query output in a data analytics platform are controlled. The one or more approximations are controlled by generating values of error metrics associated with placements of samplers in one or more query execution plans associated with the query, and injecting a plurality of samplers into the query execution plans, using the determined values of the error metrics, in lieu of storing samples of input to the query prior to execution of the query.

Controlling approximations of queries

The ensuring of predictable and quantifiable networking performance includes adaptively throttling the rate of VM-to-VM traffic flow. A receiving hypervisor can detect congestion and communicate messages for throttling traffic flow to reduce congestion at the receiving hypervisor.

Ensuring predictable and quantifiable networking performance

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Redmond, WA, United States of America

Srikanth Kandula