Context aware dynamic composition of migration plans to cloud

Patent metadata

Field	Value
Publication number	US-9612767-B2
Application number	US-201514747558-A
Country	US
Kind code	B2
Filing date	Jun 23, 2015
Priority date	Nov 19, 2014
Publication date	Apr 4, 2017
Grant date	Apr 4, 2017

Abstract

Context aware dynamic composition of migration plans may be provided. A request for application or image migration may be received. Target machines and associated configuration may be identified. Resources and a schedule may be allocated. An appropriate tooling for each migration action may be selected. An artificial intelligence aspect of the migration planning process may continuously replan migration based on monitored changes in the context of source or target environment.

First claim

We claim:

1. A method of providing context aware dynamic composition of migration plans, comprising:
receiving a request to migrate at least one of application and computer system image from a source environment;
identifying a target environment and associated configuration;
defining one or more contextual actions comprising a sequence of migration steps;
allocating resources associated with the source environment and a schedule for triggering the one or more contextual actions;
selecting automatically one or more migration techniques to use to implement the one or more contextual actions based on current context of the source environment and the target environment;
monitoring an execution of migration performed according to the allocating and the selecting;
responsive to detecting one or more exceptions in the execution of migration, regenerating a migration plan, the one or more exceptions comprising a component defined in the target definition missing from the target environment, and the regenerating the migration plan to include a contextual action comprising installing the missing component in the target environment; and
automatically generating a migration template comprising learned patterns associated with a particular context learned based on semi-supervised machine learning observing historical data and user input;
wherein the template captures one or more steps that apply in a given context, which can be reused in future;
wherein the semi-supervised machine learning involves interactively querying a user to obtain outputs at new data points.

2. The method of claim 1 , wherein the regenerating a migration plan comprises repeating the allocating and the selecting.

3. The method of claim 1 , wherein the regenerating a migration plan comprises repeating the defining, the allocating and the selecting.

4. The method of claim 1 , wherein the one or more exceptions comprises a change in one or more of the source environment and the target environment during the migration.

5. The method of claim 1 , further comprising continuing to monitor one or more of the source environment and the target environment for changes, after completing of the migration.

6. The method of claim 5 , further comprising responsive to detecting one or more of the changes, performing the regenerating of the migration plan based on detected one or more changes.

7. The method of claim 1 , wherein the regenerating of the migration plan comprises selecting a different technical tool to implement the one or more contextual actions.

8. The method of claim 1 , wherein responsive to detecting one or more exceptions in the execution of migration, the regenerating a migration plan comprises changing a migration technique selected from at least one of physical migration, image migration, application/workload migration, and data migration.

Assignees

• IBM

Inventors

• Huang Yunwu
• Hwang Jinho
• Perpetua Jr Dennis A
• Vukovic Maja
• Young Christopher C

Classifications

• G06F9/5011

  the resources being hardware resources other than CPUs, Servers and Terminals · CPC title

• G06F9/5072

  Grid computing · CPC title

• H04L41/0816

  the condition being an adaptation, e.g. in response to network events · CPC title

• H04L41/0886

  Fully automatic configuration · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title