Automated update management for cloud services

Patent metadata

Field	Value
Publication number	US-2024303062-A1
Application number	US-202318191031-A
Country	US
Kind code	A1
Filing date	Mar 28, 2023
Priority date	Mar 28, 2023
Publication date	Sep 12, 2024
Grant date	—

Abstract

An orchestration system implements a rollout service that deploys a series of updates to a cloud service while minimizing an impact of a regression caused in the cloud service by one of the updates. The system includes an orchestrator host computer hosting the rollout service; a network interface with a network on which the cloud service is provided; and a database of deployment policy information and records of previous updates to the cloud service. The rollout service automatically determines a deployment policy for an update using the database, implements a deployment of the update according to the deployment policy, monitors for evidence of a regression caused by the update, and identifies occurrence of the regression caused by the update to the cloud service to enable mitigation of an impact of the regression.

First claim

What is claimed is:

1 . An orchestration system for implementing a rollout service that deploys a series of updates to a cloud service while minimizing an impact of a regression caused in the cloud service by one of the updates, the system comprising:
an orchestrator host computer hosting the rollout service; a network interface with a network on which the cloud service is provided; and a database of deployment policy information and records of previous updates to the cloud service; wherein the rollout service automatically determines a deployment policy for an update using the database, implements a deployment of the update according to the deployment policy, monitors for evidence of a regression caused by the update, and identifies occurrence of the regression caused by the update to the cloud service to enable mitigation of an impact of the regression.

2 . The orchestration system of claim 1 , further comprising a service health engine to automatically detect a regression based on Quality of Service (QoS) signals from components supporting the cloud service.

3 . The orchestration system of claim 2 , wherein the rollout service receives a health signal from the service health engine and, in response to the health signal indicating a regression with a magnitude exceeding a threshold, halts rollout of the update.

4 . The orchestration system of claim 2 , wherein the service health engine focuses on different QoS signals based on a type of update being deployed, where past regressions caused by different types of updates have been correlated to different QoS signals.

5 . The orchestration system of claim 2 , wherein the service health engine compares current QoS signals while rolling out a current build with QoS signals recorded during rollout of a previous build, the rollout service detecting a regression based on a change in the QoS signals corresponding to the current build as compared with the QoS signals corresponding to the previous build.

6 . The orchestration system of claim 1 , wherein the rollout service quantifies the impact of the regression and, when the impact exceeds a threshold, takes action to mitigate the regression.

7 . The orchestration system of claim 6 , further comprising:
a state machine that stores a succession of states of the cloud service, wherein the rollout service tags a number of states in the succession of states as “good” based on telemetry received, the rollout service to return the cloud service to a last known good state in response to a regression by implementing the last known good state from data in the state machine.

8 . The orchestration system of claim 7 , wherein the rollout service increases a rate at which states are checked and tagged as “good” in response to an increasing level of risk of a current update.

9 . The orchestration system of claim 1 , further comprising a policy service storing indications of which segments of a userbase are more or less sensitive to a regression;
wherein the rollout service places userbase segments that are more sensitive to a regression in later stages of an update and userbase segments that are less sensitive to a regression in earlier stages of an update.

10 . The orchestration system of claim 1 , further comprising a data-driven temperature monitor for different segments of a userbase, the rollout service to implement a data-driven temperature-based rollout by determining a staged deployment policy for the update based on a temperature of different segments of a userbase.

11 . The orchestration system of claim 1 , wherein, in response to detection of a regression, the rollout service is to provide information about the regression and a matching change in the update that caused the regression to a number of administrators of client systems of the cloud service.

12 . The orchestration system of claim 1 , wherein a planned fault that will cause a regression is periodically placed in an update being implemented by the rollout service to test response of the service to a resulting planned regression.

13 . A method of rolling out updates to a cloud service to minimize impact of a regression caused in the cloud service by a corresponding update, the method comprising:
detecting the regression caused by the corresponding update in the cloud service; measuring an impact of the regression on the cloud service; and when the impact exceeds a threshold, automatically mitigating the regression.

14 . The method of claim 13 , further comprising mitigating the regression by halting the regression.

15 . The method of claim 13 , further comprising mitigating the regression by rolling back a state of the cloud service to a last known good (LKG) state.

16 . The method of claim 15 , further comprising:
with a state machine, storing a succession of states of the cloud service; tagging a number of states in the succession of states as “good” based on telemetry received; and returning the cloud service to a state most recently tagged as good to mitigate the regression.

17 . The method of claim 16 , further comprising increasing a rate at which states are checked and tagged as “good” in response to an increasing level of risk of a current update.

18 . The method of claim 13 , further comprising periodically inserting a planned fault that will cause a regression in an update being implemented to test response of a rollout service to a resulting planned regression.

19 . A method of rolling out updates to a cloud service to minimize impact of a regression caused in the cloud service by a corresponding update, the method comprising:
periodically inserting a planned fault that will cause a regression in an update being implemented to test response of a rollout service to a resulting planned regression; detecting the planned regression caused by the update in the cloud service; verifying proper operation of a regression detection system; and mitigating the planned regression.

20 . The method of claim 19 , further comprising automatically detecting the planned regression based on Quality of Service (QoS) signals from components supporting the cloud service.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Verma Nidhi
• Nigam Rahul

Classifications

• G06F11/0751

  Error or fault detection not based on redundancy (power supply failures G06F1/30; network fault management H04L41/06) · CPC title

• H04W28/0268

  using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR] (negotiating SLA or negotiating QoS H04W28/24) · CPC title

• G06F11/1433

  during software upgrading · CPC title

• G06F8/656

  while running · CPC title

• G06F8/65 Primary

  Updates (security arrangements therefor G06F21/57) · CPC title