Yahoo! Inc.

Yahoo Assets LLC

Nathaniel David Roberts

Peter Cnudde

Jeanie Zhiling Zheng

Kihwal Lee

John Vijoe George

Chung Hae Sohn

Jason Lowe

The present teaching relates to managing computing resources. In one example, information about resource utilization on a computing node is received from the computing node. Available resource on the computing node is determined based on the information. A model generated in accordance with reinforcement learning based on simulated training data is obtained. An adjusted available resource is generated based on the available resource and the model with respect to the computing node. The adjusted available resource is sent to a scheduler for scheduling one or more jobs to be executed on the computing node based on the adjusted available resource.

Method and system for managing resource utilization based on reinforcement learning

Methods and apparatus are described for a horizontally scalable high performance object storage architecture. Metadata are completely decoupled from object storage. Instead of file names, users are given a locator when the object is uploaded and committed. Users can store the locator along with their own metadata or embed it directly in the static content. Clients can choose which storage nodes to store data on based on dynamic measures of node performance. Since there is no coupling among storage servers, performance can scale horizontally by adding more nodes. The decoupling also allows the front end services and storage to scale independently. High service availability is achieved by object-level synchronous replication and having no single point of failure. Failed nodes are rebuilt using copies of data in other nodes without taking the cluster offline. In addition to the replication, the ability to add or remove nodes on-line reduces maintenance-related service downtime.

Method for efficient storage node replacement

Growing community of inventors

Ogden, IL, United States of America

Nathaniel David Roberts