Microsoft Technology Licensing, LLC

Rajeev Sudhakar Bhopi

Dharma Kiritkumar Shukla

Karthik Raman

Momin Mahmoud Al-Ghosien

Shireesh Kumar Thota

Pankaj Sharma

Atul Katiyar

Ankur Savailal Shah

Madhan Gajendran

Hari Sudan Sundar

Liang Li

Ji Huang

Arsalan Ahmad

Subramaniam Venkatraman Krishnan

Samer Boshra

Shreshth Singhal

Hemant Kumar

Sujit Vattathil Kuruvilla

Yiwen Zhu

Ovidiu Constantin Platon

Krishnan Sundaram

Padma Priya Aradhyula Bhavani

Venkata Sivaramakrishna Ramadugu

Artavazd Ginosian

Lalitha Manjapara Viswanathan

Jonah Karpman

Nicholas Kent Glaze

Irwin Hollar Mcneely, Iii

Junyan Guo

Matthew Joseph Gleeson

Helen Mary Serr

Example solutions provide an artificial intelligence (AI) agent for pre-build configuration of cloud services in order to enable the initial build of a computational resource (e.g., in a cloud service) to minimize the likelihood of excessive throttling or slack. Examples leverage prior-existing utilization data and project metadata to identify similar use cases. The utilization data includes capacity information and resource consumption information (e.g., throttling and slack) for prior-existing computational resources, and the project metadata includes information for hierarchically categorization, to identify similar resources. A pre-build configuration is generated for the customer's resource, which the customer may tune based upon the customer's preferences for a cost and performance balance point.

AI agent for pre-build configuration of cloud services

Distributed transactions are performed over a collection of servers operating as replicas of a data set, where a successful transaction involves meeting a quorum count of replicas that locally commit the transaction. However, performance constraints of data sets and consuming applications may vary (e.g., sensitivity to latency, scalability, and/or consistency), and the performance characteristics of the server set may be partly determined by the transactional commitment and quorum selection. The distributed transaction may be applied by designating the replicas as a set of followers and a leader that initiates the transaction and receives acknowledgments of local commits by each follower. On condition of the acknowledgments meeting a quorum count for the data set according to the performance characteristics of the application, the leader locally commits the transaction and delivers a result. The transaction may also be applied over collections of replica sets using a second-level quorum to achieve nested consensus.

Resource-governed protocol and runtime for distributed databases with consistency models

A data service may be distributed over a set of servers in order to provide a database with properties such as low latency, high availability, and support for various consistency levels. Presented herein is a particular architecture that promotes rapid provisioning to promote scalability and failover; adaptive load-balancing to accommodate fluctuations in demand; and resiliency in the event of various types of failure, such as network partitions or regional outages. For a service comprising a resource set, a container is provided that hosts a set of replicas of a partition, and that is assigned an allocation of computing capabilities of one or more servers. The resource set of the service may be distributed over the replicas of the container. Scalability is achieved by adding replicas to the container, and load-balancing may be provided by splitting, merging, or otherwise refactoring the partition to accommodate anticipated and unanticipated fluctuations in service demand.

Containerization for elastic and scalable databases

Workloads are often performed by a server set according to a service level agreement, and are often provisioned and load-balanced by dedicating selected computational resources (e.g., servers and bandwidth) for application to the workload. However, resource-based provisioning may not accurately reflect the computational resource expenditure of the workload, leading to overprovisioning or underprovisioning of servers for the workload. Instead, the workload may be evaluated according to a service unit as a measurement of a volume of computational resources consumed by a workload unit, including performance dimensions specified in the service level agreement. The service level agreement may indicate a service unit rate for the workload. The workload may therefore be allocated to a subset of servers in portions according to a service unit rate, where the sum of the service unit rates for the portions allocated to the servers satisfies the service unit rate specified in the service level agreement.

Data service provisioning, metering, and load-balancing via service units

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Mercer Island, WA, United States of America

Rajeev Sudhakar Bhopi