Microsoft Technology Licensing, LLC

Fortinet, Inc.

Saurabh Agarwal

Karthikeyan Subramanian

Shandan Zhou

Thomas Moscibroda

Yong Xu

Ajay Mani

Qingwei Lin

Si Qin

Nisarg Tarakkumar Sheth

Sorin Iftimie

Gowtham Natarajan

Sanjay Ramanujan

Wanghai Gu

Paul Naveen Selvaraj

Sandeep Ramji

Ajay Malik

Marwan E Jubran

Aleksandr Mikhailovich Gershaft

Aastha Jain

Amar Phanishayee

Asad Yaqoob

Lakshmi Narayana Dronadula

Parag Agrawal

Avinash Bhagtani

Nuwas Ponnambathayil

Ambrose Thomas Treacy

Luke Rafael Rodriguez

Koon Hui Geoffrey Goh

Vipin Kumar

Usha Seetharaman

Maitreyee Ramprasad Joshi

Saravanan Arumugam

Vinayak Ramnath Karnataki

Neha Keshari

Yash Purohit

Muhammad Khizar Qazi

The description relates to deep learning cluster scheduler modular toolkits. One example can include generating a deep learning cluster scheduler modular toolkit that includes multiple DL scheduler abstraction modules and interactions between the multiple DL scheduler abstraction modules and allows user composition of the multiple DL scheduler abstraction modules to realize a deep learning scheduler.

Deep learning scheduler toolkit

The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Predicting expansion failures and defragmenting cluster resources

To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

Node health prediction based on failure issues experienced prior to deployment in a cloud computing system

A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

Capacity management in a cloud computing system using virtual machine series modeling

A cohort service is configured to address the technical problem of providing recommendations to a member of an online connection network system in a manner that alleviates potentially excessive cognitive load associated with presenting recommended entities indiscriminately as a scrollable list. The cohort service is configured to visually surface recommended relevant entities already grouped as cohorts. A cohort is a grouping of entities based on one or more common attributes, such as, e.g., same school, same company, etc. The cohort service is designed to group recommendation results into cohorts at the server side, which increases the liquidity and the relevance of the recommended entities so that the already grouped recommendations can be sent to the client computer system for presentation to a viewer.

Cohort service

Embodiments relate to determining whether to take a resource distribution unit (RDU) of a datacenter offline when the RDU becomes faulty. RDUs in a cloud or datacenter supply a resource such as power, network connectivity, and the like to respective sets of hosts that provide computing resources to tenant units such as virtual machines (VMs). When an RDU becomes faulty some of the hosts that it supplies may continue to function and others may become unavailable for various reasons. This can make a decision of whether to take the RDU offline for repair difficult, since in some situations countervailing requirements of the datacenter may be at odds. To decide whether to take an RDU offline, the potential impact on availability of tenant VMs, unused capacity of the datacenter, a number or ratio of unavailable hosts on the RDU, and other factors may be considered to make a balanced decision.

Opportunistic offlining for faulty devices in datacenters

Wi-Fi flows are intelligently bridged in a software-defined network (SDN) controller of a wireless communication network that centrally coordinates data plane behavior. A default mode tunnels packets received at an access point to the SDN controller for layer 2 routing decisions. A bridging policy concerning bridging of specific types of traffic flows for the wireless communication network is received at the SDN. Data plane traffic flow for each of a plurality of access points distributed around the wireless communication network is centrally monitored. New data streams tunneled to the SDN controller are matched to bridging policies with deep packet inspection. Responsive to matching, the tunnel mode is converted to a bridge mode by sending a rule concerning the new data stream to the access point. As a result, subsequent packets of the new data stream are transferred at the access point without tunneling additional packets to the SDN controller).

Intelligent bridging of Wi-Fi flows in a software defined network (SDN)

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Redmond, WA, United States of America

Saurabh Agarwal