Microsoft Technology Licensing, LLC

Krishnan Sundaram

Dharma Kiritkumar Shukla

Karthik Raman

Momin Mahmoud Al-Ghosien

Shireesh Kumar Thota

Madhan Gajendran

Pankaj Sharma

Ankur Savailal Shah

Hari Sudan Sundar

Samer Boshra

Liang Li

Mikhail Mikhailovich Koltachev

Adrian Ilcu Predescu

Brandon Chong

Aravind Ramachandran Krishna

Karan Vishwanath Popali

Atul Katiyar

Rajeev Sudhakar Bhopi

Jayanta Mondal

Oliver Drew Leonard Towers

Siddhesh Dilip Vethe

Vinod Sridharan

Orestis Kostakis

Balachandar Perumalswamy

Vivek Ravindran

Ashwini Singh

Artavazd Ginosian

Emily Lawton

Lalitha Manjapara Viswanathan

Data sets such as databases are often distributed over a number of servers, where each server stores a subset of records and an index that enables the server to locate the records in response to queries. However, tight coupling of indexing and storage may limit load-balancing, fault recovery, and distribution. Instead, the set of servers may be partitioned into a set of storage servers that store the records and a set of index servers of the index over the records. In a set that is decoupled in this manner, load-balancing may involve provisioning and locating index servers independently of the provisioning and locating of the storage servers in view of the particular sensitivities and tolerances of various applications. Additionally, the index servers may also utilize a data layout that is selected and adapted independent of the data layout of the storage servers and/or the schema of the data stored thereby.

Schema-agnostic indexing of distributed databases

Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.

Unified data organization for multi-model distributed 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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Krishnan Sundaram