Software-defined storage system monitoring tool

• US 9,798,474 B2
• Filed: 09/25/2015
• Issued: 10/24/2017
• Est. Priority Date: 09/25/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Barzik, Zah, Frank, Yaacov, Goldberg, Tomer, Margalit, Amit, Matosevich, Rivka M., Shasha, Osnat
• Primary Examiner(s): GIARDINO JR, MARK A
• Application Number:
  US14/865,539
• Publication Number:
  US 20170090779A1
• Time in Patent Office: 760 Days
• CPC Class Codes: G06F 11/3034 where the computing system component is a storage system, e.g. DASD based or network based digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 3/0605 by facilitating the interaction with a user or administrator
• CPC Class Codes: G06F 3/0608 Saving storage space on storage systems
• CPC Class Codes: G06F 3/0613 in relation to throughput
• CPC Class Codes: G06F 3/0619 in relation to data integrity, e.g. data losses, bit errors
• CPC Class Codes: G06F 3/0641 De-duplication techniques
• CPC Class Codes: G06F 3/065 Replication mechanisms
• CPC Class Codes: G06F 3/0653 Monitoring storage devices or systems
• CPC Class Codes: G06F 3/067 Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]

Abstract

Methods, computing systems and computer program products implement embodiments of the present invention that include collecting, from a software-defined storage system including one or more computing nodes that are configured to provide a storage service, performance metrics for each of the computing nodes, and detecting, based on the performance metrics, a performance bottleneck in the software-defined storage system. In embodiments of the present invention, each of the computing nodes includes one or more software modules and one or more hardware components, and the performance bottleneck is either a given software module or a given hardware component. In some embodiments, detecting the performance bottleneck includes predicting the performance bottleneck. Upon detecting the performance bottleneck, a solution for the performance bottleneck can be determined, and the performance bottleneck and the solution can be presented to a user on a display.

Claims

1. A method, comprising:

collecting, from a software-defined storage system comprising one or more computing nodes that are configured to provide a storage service, performance metrics for each of the computing nodes;

wherein the performance metrics collected include at least storage device latency, input/output (I/O) latency, transmission control protocol (TCP) connection performance, hypervisor processor and memory utilization, virtual machine (VM) processor and memory utilization, and network latency, packet loss and delay;

detecting, based on the performance metrics, a performance bottleneck in the software-defined storage system;

determining a solution for the performance bottleneck, the solution including at least one of allocating additional processors or memory to a given computing node and allocating additional network bandwidth to the software-defined storage system; and

presenting, to a user, the performance bottleneck and the solution.

2. The method according to claim 1, wherein detecting the performance bottleneck comprises predicting the performance bottleneck.

3. The method according to claim 1, wherein each of the computing nodes is selected from a group consisting of a bare metal system, a software container and a virtual machine.

4. The method according to claim 1, wherein a given computing node comprises one or more software modules executing on a computer system, and wherein detecting the performance bottleneck in the software-defined storage system comprises detecting the performance bottleneck in the one or more software modules.

5. The method according to claim 1, wherein a given computing node comprises multiple hardware components, and wherein detecting the performance bottleneck in the software-defined storage system comprises detecting the performance bottleneck in one or more of the hardware components.

6. The method according to claim 1, wherein presenting the performance bottleneck comprises calculating a score based on the performance metrics, and presenting the score to the user.

7. The method according to claim 6, and comprising receiving an input from the user identifying one or more of the performance metrics, and wherein calculating the score based on the performance metrics comprises calculating the score based on the one or more identified performance metrics.

8. A computing facility, comprising:

one or more computing nodes configured as a software-defined storage system that are arranged to provide a storage service; and

a computer coupled to the one or more computing nodes and configured;

to collect, from the software-defined storage system, performance metrics for each of the computing nodes;

wherein the performance metrics collected include at least storage device latency, input/output (I/O) latency, transmission control protocol (TCP) connection performance, hypervisor processor and memory utilization, virtual machine (VM) processor and memory utilization, and network latency, packet loss and delay,

to detect, based on the performance metrics, a performance bottleneck in the software-defined storage system,

to determine a solution for the performance bottleneck, the solution including at least one of allocating additional processors or memory to a given computing node and allocating additional network bandwidth to the software-defined storage system, and

to present, to a user, the performance bottleneck and the solution.

9. The computing facility according to claim 8, wherein the computer is configured to detect the performance bottleneck by predicting the performance bottleneck.

10. The computing facility according to claim 8, wherein each of the computing nodes is selected from a group consisting of a bare metal system, a software container and a virtual machine.

11. The computing facility according to claim 8, wherein a given computing node comprises one or more software modules executing on a computer system, and wherein the computer is configured to storage the performance bottleneck in the software-defined storage system by detecting the performance bottleneck in the one or more software modules.

12. The computing facility according to claim 8, wherein a given computing node comprises multiple hardware components, and wherein the computer is configured to detect the performance bottleneck in the software-defined storage system by detecting the performance bottleneck in one or more of the hardware components.

13. The computing facility according to claim 8, wherein the computer is configured to present the performance bottleneck by calculating a score based on the performance metrics, and presenting the score to the user.

14. The computing facility according to claim 13, wherein the computer is configured to receiving an input from the user identifying one or more of the performance metrics, and wherein the computer is configured to calculate the score based on the performance metrics by calculating the score based on the one or more identified performance metrics.

15. A computer program product, the computer program product comprising:

a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising;

computer readable program code configured to collect, from a software-defined storage system comprising one or more computing nodes that are configured to provide a storage service, performance metrics for each of the computing nodes;

wherein the performance metrics collected include at least storage device latency, input/output (I/O) latency, transmission control protocol (TCP) connection performance, hypervisor processor and memory utilization, virtual machine (VM) processor and memory utilization, and network latency, packet loss and delay;

computer readable program code configured to detect, based on the performance metrics, a performance bottleneck in the software-defined storage system;

computer readable program code configured to determine a solution for the performance bottleneck, the solution including at least one of allocating additional processors or memory to a given computing node and allocating additional network bandwidth to the software-defined storage system; and

computer readable program code configured to present, to a user, the performance bottleneck and the solution.

16. The computer program product according to claim 15, wherein the computer readable program code is configured to detect the performance bottleneck by predicting the performance bottleneck.

17. The computer program product according to claim 15, wherein each of the computing nodes is selected from a group consisting of a bare metal system, a software container and a virtual machine.

18. The computer program product according to claim 15, wherein a given computing node comprises one or more software modules executing on a computer system comprising multiple hardware components, and wherein the computer readable program code is configured to detect the performance bottleneck in the software-defined storage system by detecting the performance bottleneck in a location selected from a group consisting of a given software module and a given hardware component.

19. The computer program product according to claim 15, wherein the computer readable program code is configured to present the performance bottleneck by calculating a score based on the performance metrics, and presenting the score to the user.

20. The computer program product according to claim 19, and comprising computer readable program code configured to receive an input from the user identifying one or more of the performance metrics, and wherein the computer readable program code is configured to calculate the score based on the performance metrics by calculating the score based on the one or more identified performance metrics.