公开(公告)号：US20250086023A1

公开(公告)日：2025-03-13

申请号：US18463005

申请日：2023-09-07

Applicant: c/o Microsoft Technology Licensing, LLC

Inventor： Ali KANSO ,  Arijit TARAFDAR ,  Sumeet KHUSHALANI

IPC: G06F9/50

Abstract: Described are examples for distributing stateful workloads to clusters including obtaining, for a cluster, a previous configuration from a shared database, wherein the previous configuration includes a previous indication of a previous owner of a stateful workload, obtaining, for the cluster and from the shared database, a configuration including an indication of a current owner of the stateful workload. Based on determining that the cluster is the current owner, the cluster can continue processing a next state of the stateful workload, which may be based on one or more other considerations.

公开(公告)号：US20240231927A1

公开(公告)日：2024-07-11

申请号：US18152391

申请日：2023-01-10

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Alex YEO ,  Harsha Nihanth NAGULAPALLI ,  Sumeet KHUSHALANI ,  Arijit TARAFDAR ,  Subramaniam VENKATRAMAN KRISHNAN ,  Deepak RAVIKUMAR ,  Andrew Francis FOGARTY ,  Steve D. SUH ,  Yoonjae PARK ,  Niharika DUTTA ,  Santhosh Kumar RAVINDRAN

IPC: G06F9/50 ,  G06F9/48 ,  G06N3/08

CPC classification number: G06F9/5038 ,  G06F9/4843 ,  G06N3/08

Abstract: The present application relates to a network, apparatus, and method for allocating clusters of computing nodes for programming jobs. A network includes a plurality of datacenters including computing resources configurable to instantiate nodes for executing programming jobs on a cluster. The computing resources at one of the datacenters are configured to: provision a live pool including a number of clusters, each cluster in the live pool including a plurality of nodes imaged with a configuration for executing the programming jobs in parallel on the cluster; receive a request from a user to execute a programming job; allocate a cluster from the live pool to the user for the programming job when the cluster is available; evict the cluster from the live pool; and provision a new cluster within the live pool to meet the number of clusters. The number of clusters may be optimized based on linear programming and machine-learning.