Resource provisioning planning for enterprise migration and automated application discovery

• US 9,565,129 B2
• Filed: 09/30/2014
• Issued: 02/07/2017
• Est. Priority Date: 09/30/2014
• Status: Expired due to Fees

• Current Assignee: AirBNB Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Bai, Kun, Hwang, Jinho, Jermyn, Jill L., Nidd, Michael E., Tacci, Michael, Vukovic, Maja
• Primary Examiner(s): Hussain, Tauqir
• Application Number:
  US14/501,311
• Publication Number:
  US 20160094477A1
• Time in Patent Office: 861 Days
• Field of Search: 709/226
• US Class Current: 1/1
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06F 9/5083 Techniques for rebalancing the load in a distributed system
• CPC Class Codes: H04L 47/786 Mapping reservation between domains

Abstract

A method and a system may automatically compute optimized target resources and identify configurations given discovered source properties and dependencies of machines, while prioritizing performance in the target environment. An algorithm and/or tools for programmatically identifying and locating business application instances in an infrastructure, based on weighted similarity metric may be also provided.

Claims

1. A method of resource provisioning planning, comprising:

receiving source site data comprising parameters associated with a source site, the source site comprising information technology infrastructure of an enterprise, the information technology infrastructure comprising at least a plurality of servers and a plurality of applications;

discovering network dependencies between the plurality of servers of the source site;

discovering application instances and dependencies on the plurality of servers, based on a weighted similarity metric;

identifying one or more server misconfigurations;

determining potential server consolidation;

determining a target platform for migrating the plurality of servers;

mapping each of the plurality of servers to a virtual machine of a plurality of virtual machines subject to resource constraints of the servers and the virtual machines;

clustering the virtual machines based on network dependencies of the servers mapped to the virtual machines;

determining whether an existing target server can fit a candidate cluster;

responsive to determining that the existing target server can fit the candidate cluster, placing the candidate cluster on the existing target server;

responsive to determining that the existing target server cannot fit the candidate cluster, selecting a new target server to fit the candidate cluster with minimum cost; and

repeating the determining of whether an existing target server can fit a candidate cluster, one or more of the placing and the selecting, for a next candidate cluster.

2. The method of claim 1, wherein the parameters comprise one or more of command line tool output, middleware dependency data associated with the plurality of servers discover using an automated discovery tool, utilization information associated with the information technology infrastructure, hardware specifications associated with the information technology infrastructure, application information associated with the information technology infrastructure, and log output associated with the information technology infrastructure.

3. The method of claim 1, wherein the discovering network dependencies comprises analyzing active sockets and middleware dependencies.

4. The method of claim 3, further comprising scoring weight for each of the network dependencies.

5. The method of claim 4, wherein the scoring is based on at least one of:

a level of socket activity;

a number of active sockets;

duration of socket length; and

a number of middleware dependencies.

6. The method of claim 1, wherein the clustering of the virtual machines is based on modularity technique.

7. The method of claim 1, wherein the identifying one or more server misconfigurations comprises analyzing sockets, dropped packets and incorrect checksums, system logs, or application logs, or combinations thereof.

8. The method of claim 1, wherein the determining potential server consolidation comprises identifying servers from the plurality of servers that are running a same operating system and version, and that do not have overlapping open ports and running applications.

9. The method of claim 8, further comprising consolidating the identified servers based on target cost and performance.

10. The method of claim 1, wherein the virtual machines is further grouped based on business application discovery results that identify servers in the plurality of servers that work together to deliver a single business application.

11. A system of resource provisioning planning, comprising:

a hardware processor,

a storage device storing source site data comprising parameters associated with a source site, the source site comprising information technology infrastructure of an enterprise, the information technology infrastructure comprising at least a plurality of servers and a plurality of applications;

the hardware processor discovering network dependencies between the plurality of servers of the source site,

the hardware processor identifying one or more server misconfigurations,

the hardware processor determining candidate server consolidation,

the hardware processor determining a target platform for migrating the plurality of servers,

the hardware processor mapping each of the plurality of servers to a virtual machine of a plurality of virtual machines subject to resource constraints of the servers and the virtual machines,

the hardware processor clustering the virtual machines based on network dependencies of the servers mapped to the virtual machines,

the hardware processor determining whether an existing target server can fit a candidate cluster,

responsive to determining that the existing target server can fit the candidate cluster, the hardware processor placing the candidate cluster on the existing target server,

responsive to determining that the existing target server cannot fit the candidate cluster, the hardware processor selecting a new target server to fit the candidate cluster with minimum cost, and

the hardware processor repeating the determining of whether an existing target server can fit a candidate cluster, one or more of the placing and the selecting, for a next candidate cluster.

12. The system of claim 11, wherein the parameters comprise one or more of command line tool output, middleware dependency data associated with the plurality of servers discover using an automated discovery tool, utilization information associated with the information technology infrastructure, hardware specifications associated with the information technology infrastructure, application information associated with the information technology infrastructure, and log output associated with the information technology infrastructure.

13. The system of claim 11, wherein the hardware processor discovers network dependencies by analyzing active sockets and middleware dependencies, wherein the processor scores a weight for each of the network dependencies, wherein the scores are based on at least one of:

a level of socket activity;

a number of active sockets;

duration of socket length; and

a number of middleware dependencies.

14. The system of claim 11, wherein the clustering of the virtual machines is based on modularity technique.

15. The system of claim 11, wherein the hardware processor identifies one or more server misconfigurations by analyzing at least one of sockets, dropped packets and incorrect checksums, system logs, and application logs.

16. The system of claim 11, wherein the hardware processor determines the candidate server consolidation by identifying servers from the plurality of servers that are running a same operating system and version, and that do not have overlapping open ports and running applications, and the hardware processor consolidates the identified servers based on target cost and performance.

17. A non-transitory computer readable storage medium storing a program of instructions executable by a machine to perform a method of automating business application discovery, the method comprising:

receiving a system description data associated with a server of a plurality of servers in an information technology infrastructure of an enterprise, for each of the plurality of servers;

determining a similarity measure between a pair of the plurality of servers comprising a first server and a second server, by analyzing the system description data associated with the first server and the second server,

the analyzing comprising at least;

assigning weights to sections of the system description data;

determining a number of common elements between the system description data associated with the first server and the second server; and

using the weights as multipliers for the common elements contained in the respective sections, wherein the similarity measure is determined based on the weighted common elements occurring in the system description data associated with the first server and the second server; and

grouping the plurality of servers into a plurality of groups based on the similarity measure and at least a greedy algorithm,

wherein migration of the plurality of servers are planned based at least on the plurality of groups.

18. The computer readable storage medium of claim 17, wherein the system description data is generated using an infrastructure discovery tool, and provided in an extensible markup language format.

19. The computer readable storage medium of claim 17, further comprising generating an application signature representing the common elements in the system description data.

20. The computer readable storage medium of claim 17, further comprising validating the plurality of groups using domain knowledge.