Microsoft Technology Licensing, LLC

Virajith Jalaparti

Sriram S Rao

Christopher William Douglas

Srikanth Kandula

Ishai Menache

Avrilia Floratou

Ashvin Agrawal

Joseph Naor

Mainak Ghosh

Peng T Li

Antony Rowstron

Hitesh Ballani

Paolo Costa

Thomas Karagiannis

Baskar Sridharan

Karthick Krishnamoorthy

Raghunath Ramakrishnan

Sri Sai Kameswara Pavan Kumar Kasturi

Krishna Pradeep Reddy Tamma

Chakrapani Bhat Talapady

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

A system that maps files into multiple parts and initiates storage of each part within a respective storage provider. The mapping facilitates this by correlating each of the multiple parts to a corresponding storage provider which may be different than the storage provider that stores a different part of the same file. The system then initiates storage of the multiple parts of the file in each of their corresponding storage provider in accordance with the mapping, and using a storage-provider-facing API. Similarly, retrieval and/or editing of a partial file may be accomplished by referencing the mapping, and using the same API issued to the appropriate storage provider. Each storage provider is capable of interfacing with the system using this same API.

Mapping storage across storage providers

Methods of predicting datacenter performance to improve provisioning are described. In an embodiment, a resource manager element receives a request from a tenant which describes an application that the tenant wants executed by a multi-resource, multi-tenant datacenter. The request that has been received is mapped to a set of different candidate resource combinations within the datacenter, where each candidate resource combination can be used to execute the application in a manner which satisfies a high level constraint specified within the request. This mapping may, for example, be performed using a combination of benchmarking and an analytical model. In some examples, each resource combination may comprise a number of virtual machines and a bandwidth between those machines. Data relating to at least a subset (and in some examples, two or more) of the candidate resource combinations is then presented to the tenant.

Predicting datacenter performance to improve provisioning

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Milpitas, CA, United States of America

Virajith Jalaparti