Systems, apparatus and methods for management of software containers

• US 10,191,778 B1
• Filed: 11/14/2016
• Issued: 01/29/2019
• Est. Priority Date: 11/16/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: Turbonomic, Inc. (International Business Machines Corporation)
• Inventors: Yang, Dongyi, Sara, Endre, Xu, Enlin
• Primary Examiner(s): Eskandarnia, Arvin
• Application Number:
  US15/351,342
• Time in Patent Office: 806 Days
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• CPC Class Codes: G06F 11/301 where the computing system is a virtual computing platform, e.g. logically partitioned systems virtual machines G06F9/45533; logical partitioning of resources G06F9/5077
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• 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 2009/45595 Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 9/455 Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: G06F 9/50 Allocation of resources, e.g. of the central processing unit [CPU]
• CPC Class Codes: G06Q 20/065 using e-cash
• CPC Class Codes: G06Q 20/145 Payments according to the detected use or quantity
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• 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 41/5054 Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications
• 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 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/1023 : based on a hash applied to IP addresses or costs

Abstract

Methods, systems, and apparatus, including computer program products, are disclosed for managing resources in container systems, including multi-cloud systems. The use of supply chain economics alone and in combination with other techniques offers a unified platform to integrate, optimize or improve, and automate resource management in a container system. These techniques may be used to auto-scale or place container or pod entities. They may also be used to monitor and control contention of computing resources in a container system, and to place, clone, resize, suspend or terminate computing resources.

Claims

1. A computer-implemented method, comprising:

determining, by a pod manager running on a data processor in a container system, a computer resource bundle to be purchased for a pod in the container system using virtual currency units, wherein the pod is a cluster of two or more containers in the computer system sharing at least one resource;

identifying multiple resource providers in the container system offering the computer resource bundle;

determining a purchase price for the computer resource bundle, in virtual currency units, for each of the multiple resource providers;

automatically selecting, by the pod manager, a first one of the multiple resource providers based at least in part on the purchase price for the computer resource bundle for each of the multiple resource providers;

allocating the computer resource bundle from the selected first one of the multiple resource providers to the pod; and

determining, following an increase in the purchase price for the computer resource bundle offered by the selected first one of the multiple resource providers, that the pod is to be moved from the selected first one of the multiple resource providers to a second one of the multiple resource providers based at least in part on a lower purchase price for the computer resource bundle offered by the second one of the multiple resource providers.

2. The computer-implemented method of claim 1, further comprising:

monitoring usage or performance metrics of at least one of the cluster of containers in the pod; and

determining that at least one of the cluster of containers in the pod is to be moved, cloned or suspended.

3. The computer-implemented method of claim 2, wherein the monitoring step comprises comparing actual and target quality of service metrics associated with the at least one of the cluster of containers in the pod.

4. The computer-implemented method of claim 1, further comprising:

dividing the container system into subcomponents including at least one subcomponent that manages individual nodes in the container system and at least one subcomponent that forms part of a control plane; and

selecting, by a scheduler in the container system, a node that the pod is to run on, wherein the selection is based at least in part on resource availability of the selected node.

5. The computer-implemented method of claim 1, wherein the selected node is a physical machine.

6. The computer-implemented method of claim 5, wherein the selected node is a virtual machine.

7. The computer-implemented method of claim 1, wherein each of the cluster of containers in the pod share the same IP address.

8. The computer-implemented method of claim 1, wherein the container system is a Kubernetes container system.

9. The computer-implemented method of claim 1, wherein the cluster of containers is a cluster of Docker containers.

10. The computer-implemented method of claim 1, wherein the multiple resource providers are multiple computer servers in the container system, and wherein the determination that the pod is to be moved results in movement of the pod from a first computer server to a second computer server in the container system.

11. The computer-implemented method of claim 1, further comprising determining that the pod is to be horizontally scaled by provisioning additional pod components.

12. The computer-implemented method of claim 11, further comprising allocating additional computer resources to the pod from a selected third one of the multiple resource providers.

13. The computer-implemented method of claim 1, further comprising automatically allocating additional computer resources to the pod based on a determination that the pod is below a predetermined service level.

14. A computer-implemented method, comprising:

determining, by a pod manager running on a data processor in a Kubernetes system, a computer resource bundle to be purchased for a pod in the Kubernetes system using virtual currency units, wherein the pod is a cluster of two or more containers;

determining a performance level of one or more containers in the pod;

determining a purchase price, in virtual currency units, for additional computer resources to be purchased for the one or more containers in the pod; and

automatically allocating, based at least in part on the determined performance level and determined purchase price, the additional computer resources to the one or more containers in the pod.

15. The computer-implemented method of claim 14, wherein the automatically allocating step comprises comparing the determined performance level with a target performance level.

16. The computer-implemented method of claim 14, further comprising:

dividing the Kubernetes system into subcomponents including at least one subcomponent that manages individual nodes in the Kubernetes system; and

selecting, by a scheduler in the container system, a node that the pod is to run on, wherein the selection is based at least in part on resource availability of the selected node.

17. The computer-implemented method of claim 16, wherein the selected node is a virtual machine.

18. The computer-implemented method of claim 14, wherein each of the cluster of containers in the pod share the same IP address.

19. The computer-implemented method of claim 14, wherein the cluster of containers is a cluster of Docker containers.

20. A container-based computer system for managing resources, comprising:

instructions stored on a non-transitory computer readable medium in the container-based computer system and executable by a data processing apparatus to cause the data processing apparatus to perform operations comprising;

determining, by a pod manager running on a data processor in the container-based computer system, a computer resource bundle to be purchased for a pod in the system using virtual currency units, wherein the pod is a cluster of two or more containers in the system sharing at least one resource;

identifying multiple resource providers in the system offering the computer resource bundle;

determining a purchase price for the computer resource bundle, in virtual currency units, for each of the multiple resource providers;

automatically selecting, by the pod manager, a first one of the multiple resource providers based at least in part on the purchase price for the computer resource bundle for each of the multiple resource providers;

allocating the computer resource bundle from the selected first one of the multiple resource providers to the pod; and

determining, following an increase in the purchase price for the computer resource bundle offered by the selected first one of the multiple resource providers, that the pod is to be moved from the selected first one of the multiple resource providers to a second one of the multiple resource providers based at least in part on a lower purchase price for the computer resource bundle offered by the second one of the multiple resource providers.