Progressive deployment and termination of canary instances for software analysis

• US 9,712,411 B2
• Filed: 12/28/2015
• Issued: 07/18/2017
• Est. Priority Date: 06/25/2013
• Status: Active Grant

• Current Assignee: Netflix, Inc.
• Original Assignee: Netflix, Inc.
• Inventors: Sondow, Joseph, Tseitlin, Ariel
• Primary Examiner(s): Lim, Krisna
• Application Number:
  US14/981,629
• Publication Number:
  US 20160119207A1
• Time in Patent Office: 568 Days
• CPC Class Codes: G06F 11/368 for test version control, e.g. updating test cases to a new software version
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/5025 by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1004 Server selection for load balancing
• CPC Class Codes: H04L 67/1031 Controlling of the operation of servers by a load balancer, e.g. adding or removing servers that serve requests

Abstract

A data processing method, comprising: using computing apparatus, causing instantiating a plurality of baseline application instances that are running a first version of software, and one or more canary application instances that are running a second version of the software; using computing apparatus including a load balancer, causing selectively routing a first proportion of incoming requests to the baseline instances, and routing a second proportion of the incoming requests to the canary instances; monitoring the plurality of canary instances to collect performance data for performance metrics; determining that the performance data indicates a negative performance issue, and in response thereto: using computing apparatus, automatically updating the first proportion to be larger and updating the second proportion to be smaller, and then reconfiguring the load balancer based upon the first proportion and the second proportion; terminating one or more of the canary application instances.

Claims

1. A computer-implemented method, comprising:

deploying one or more baseline application instances that run a first version of software application code and one or more canary application instances that run a second version of the software application code;

routing a first proportion of incoming requests to the one or more canary application instances;

monitoring the one or more canary application instances relative to one or more performance metrics; and

modifying the first proportion of incoming requests according to the one or more performance metrics.

2. The computer-implemented method of claim 1, wherein the one or more performance metrics do not indicate a negative performance issue, and modifying the first proportion of incoming requests comprises increasing a size of the first proportion.

3. The computer-implemented method of claim 2, further comprising deploying one or more additional canary application instances.

4. The computer-implemented method of claim 3, further comprising terminating at least one of the baseline application instances included in the one or more baseline application instances.

5. The computer-implemented method of claim 1, wherein the one or more performance metrics indicate a negative performance issue, and modifying the first proportion of incoming requests comprises decreasing a size of the first proportion.

6. The computer-implemented method of claim 5, further comprising terminating at least one of the one or more canary application instances.

7. The computer-implemented method of claim 1, further comprising determining whether the one or more performance metrics indicates a negative performance issue by evaluating at least one of (i) a threshold amount of variance, (ii) one or more error messages associated with the one or more performance metrics, and (iii) how much data has been collected for the one or more performance metrics.

8. The computer-implemented method of claim 1, wherein the one or more baseline application instances are instantiated in a first cluster, and the one or more canary application instances are instantiated in a second cluster.

9. The computer-implemented method of claim 8, wherein a first cloud service provider instantiates the one or more baseline application instances, and a second cloud service provider different than the first cloud service provider instantiates the one or more canary application instances.

10. A non-transitory computer-readable medium including instructions that, when executed by a processor, cause the processor to perform the steps of:

deploying one or more baseline application instances that run a first version of software application code and one or more canary application instances that run a second version of the software application code;

monitoring the one or more canary application instances relative to one or more performance metrics; and

deploying one or more additional canary application instances when the one or more performance metrics do not indicate a negative performance issue.

11. The non-transitory computer-readable medium of claim 10, wherein the one or more performance metrics do not indicate a negative performance issue, and further comprising increasing a size of a first proportion of incoming requests routed to the one or more canary application instances.

12. The non-transitory computer-readable medium of claim 11, further comprising deploying two or more additional canary application instances.

13. The non-transitory computer-readable medium of claim 12, further comprising terminating at least one of the baseline application instances included in the one or more baseline application instances.

14. The non-transitory computer-readable medium of claim 10, wherein the one or more performance metrics indicate a negative performance issue, and further comprising decreasing a size of a first proportion of incoming requests routed to the one or more canary application instances.

15. The non-transitory computer-readable medium of claim 14, further comprising terminating at least one of the one or more canary application instances.

16. The non-transitory computer-readable medium of claim 10, further comprising determining whether the one or more performance metrics indicate a negative performance issue by evaluating a threshold amount of variance, one or more error messages associated with the one or more performance metrics, or how much data has been collected for the one or more performance metrics.

17. The non-transitory computer-readable medium of claim 10, wherein the one or more baseline application instances are instantiated in a first cluster, and the one or more canary application instances are instantiated in a second cluster.

18. A computer system, comprising:

a memory that stores a canary analysis engine; and

a processor that is coupled to the memory and, when executing the canary analysis engine, is configured to;

deploy one or more baseline application instances that run a first version of software application code and one or more canary application instances that run a second version of the software application code;

monitor the one or more canary application instances relative to one or more performance metrics; and

cause a first proportion of incoming requests routed to the one or more canary application instances to be modified according to the one or more performance metrics.

19. The computer system of claim 18, wherein the one or more performance metrics do not indicate a negative performance issue, and causing comprises causing a size of the first proportion to be increased.

20. The computer system of claim 18, wherein the one or more performance metrics indicate a negative performance issue, and causing comprises causing a size of the first proportion to be decreased.