Microsoft Technology Licensing, LLC

Ashwin Narasimha Murthy

Parveen Kumar Patel

Deepak Bansal

Marios Zikos

Aastha Jain

Parag Agrawal

Yafei Wang

Yan Wang

Ankan Saha

Eric M Lawrence

Aastha Nigam

Albert Lingfeng Cui

Naveen Reddy Karri

Bohong Zhao

David Sung

HongYu Wu

Yagya Narayanan Sethuraman

Abdulla Mohammad Al-Qawasmeh

In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Recommending edges via importance aware machine learned model

Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

Intent based second pass ranker for ranking aggregates

The obtaining of a lease on a resource in a circumstance in which multiple lease servers are capable of granting a lease to the resource. A computing entity attempts to obtain the lease on the resource by causing a lease request to be sent to each of at least most (and perhaps all) of the lease servers. In response, the computing entity receives one or more responses to the lease requests. If the computing entity receives grants of a lease from a majority of the lease servers that are capable of granting a lease to the resource, then it is determined that the computing entity acquired a lease on the resource. On the other hand, if the computing entity receives grants of a lease from less than a majority of the lease servers, it is determined that the computing entity failed to acquire the lease on the resource.

Acquiring resource lease using multiple lease servers

When a load balancer detects that a virtual address is associated with a single destination address, the load balancer sets a flag to distinguish the virtual address from virtual addresses that are associated with a plurality of destination addresses. The load balancer instructs the router to bypass the load balancer for network packets that are addressed to the virtual address, and refrains from storing subsequent flow state for the virtual address. When the virtual address is to be scaled up with an additional destination address, the load balancer sets a flag to distinguish the virtual address from virtual addresses that are associated with a single destination addresses. The load balancer instructs the router to route network packets that are addressed to the virtual address through the load balancer, instead of bypassing the load balancer, and starts storing flow state for the virtual address.

Load balancing for single-address tenants

A load balancer that is able to detect and mitigate a Denial of Service (DOS) attack. The load balancer is placed in the flow path of network data packets that are destined for one or more tenant addresses. The load balancer analyzes performance parameters regarding the network data packets that are destined for the one or more tenant addresses and are received at the load balancer. The performance parameters describe network data packet flow to the tenant addresses. The load balancer detects, based on the analysis of the performance parameters, that one or more of the tenant addresses are being subjected to a DOS attack. The load balancer performs a mitigation operation to isolate the one or more tenant addresses being subjected to the DOS attack.

DOS detection and mitigation in a load balancer

A central NAT manager efficiently allocates ports to endpoints of a tenant application in a hosting environment for NAT purposes. In some instances, the central NAT manager may pre-allocate ports to endpoints as part of the initial configuration of the tenant application. The pre-allocation process may include persisting pre-allocated port state information in storage and configuring endpoints and a NAT device with the pre-allocated port state information. Additional ports may be dynamically allocated to endpoints during runtime using dynamically determined port allocation sizes. In other instances, the central NAT manager may reserve ports for endpoints during the initial configuration of the tenant application by assigning ports to endpoints and persisting port assignment information to storage. During runtime, endpoints may issue port allocation requests to the central NAT manager, which may allocate ports to the endpoints from the range of ports initially reserved for each endpoint.

Efficient port management for a distributed network address translation

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Sunnyvale, CA, United States of America

Ashwin Narasimha Murthy