Workload scheduling across heterogeneous resource environments

• US 10,447,806 B1
• Filed: 06/09/2017
• Issued: 10/15/2019
• Est. Priority Date: 06/09/2017
• Status: Active Grant

• Current Assignee: Nutanix Incorporated
• Original Assignee: Nutanix Incorporated
• Inventors: Sahay, Manjul, Chandra, Ramesh U.
• Primary Examiner(s): Meky, Moustafa M
• Application Number:
  US15/619,146
• Time in Patent Office: 858 Days
• CPC Class Codes: H04L 41/085 Retrieval of network configuration; Tracking network configuration history
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• 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 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 67/1012 based on compliance of requirements or conditions with available server resources
• CPC Class Codes: H04L 67/1036 Load balancing of requests to servers for services different from user content provisioning, e.g. load balancing across domain name servers
• CPC Class Codes: H04L 67/14 Session management for real-time applications in data packet communications networks H04L65/1066
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70

Abstract

Systems for distributed resource management. A method for deploying two or more computing workloads across heterogeneous computing systems commences upon establishing network communications between an on-premises computing system and a cloud computing system. Resource data corresponding to the on-premises resources and the cloud resources are collected continuously and saved as a time series history of observations. Upon a user request or other event, workload placement planning commences. A set of workload placement plans are generated, which workload placement plans are then evaluated in accordance with one or more quantitative objectives. Scheduling commands to carry out the workload placement plans are generated. A first portion of the scheduling commands is executed at the cloud computing system, and another portion of the scheduling commands is executed at the on-premises computing system.

Claims

1. A method for deploying computing workloads across heterogeneous computing systems, the method comprising:

establishing network communications between an on-premises computing system associated with an on-premises resource and a cloud computing system associated with a cloud-based resource;

collecting resource data corresponding to the on-premises resource and the cloud-based resource;

receiving a workload placement request, the workload placement request being described by workload placement request attribute;

generating a workload placement plan;

evaluating the workload placement plan, wherein the evaluating of the workload placement plan considers at least a portion of the resource data corresponding to the on-premises resource and considers at least a portion of the resource data corresponding to the cloud-based resource;

generating a scheduling command to carry out the workload placement plan; and

deploying computing workloads by executing the scheduling command on the on-premises resource or the cloud-based resource.

2. The method of claim 1, further comprising performing a re-analysis of at least some portion of the workload placement plan with respect to updated resource data.

3. The method of claim 2, wherein retrieval of the updated resource data employs at least one API that is used to access at least one of, the on-premises computing system, or the cloud computing system.

4. The method of claim 1, further comprising receiving a resource rule, wherein determining the workload placement plan is subject to the resource rule.

5. The method of claim 1, wherein determining the workload placement plan is based at least in part on an objective.

6. The method of claim 1, wherein the workload placement plan is determined at least in part by at least one of, a multi-objective optimization algorithm, or a logistic regression algorithm.

7. The method of claim 1, wherein the on-premises resource or the cloud-based resource comprises at least one of, a virtualized entity, a virtual machine, an application, and application extension, an app, or a container.

8. The method of claim 1, wherein the workload placement plan comprises a set of workload attribute translation instructions.

9. The method of claim 8, wherein the set of workload attribute translation instructions comprises at least one of, a binary image of a virtual machine or container, or installation specifications pertaining to the virtual machine or container, or configuration specifications pertaining to the virtual machine or container.

10. The method of claim 9, wherein the set of workload attribute translation instructions comprises at least one of, instructions to use a particular hypervisor type or container daemon, or instructions to create the virtual machine or container, or instructions to register a migrated workload with the virtual machine or container.

11. A computer readable medium, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes a set of acts for deploying computing workloads across heterogeneous computing systems, the set of acts comprising:

establishing network communications between an on-premises computing system associated with an on-premises resource and a cloud computing system associated with a cloud-based resource;

collecting resource data corresponding to the on-premises resource and the cloud-based resource;

receiving a workload placement request, the workload placement request being described by a workload placement request attribute;

generating a workload placement plan;

evaluating the workload placement plan, wherein the evaluating of the workload placement plan considers at least a portion of the resource data corresponding to the on-premises resource and considers at least a portion of the resource data corresponding to the cloud-based resource;

generating a scheduling command to carry out the workload placement plan; and

deploying computing workloads by executing the scheduling command on the on-premises resource or the cloud-based resource.

12. The computer readable medium of claim 11, wherein at least one API is used to access at least one of, the on-premises computing system, or the cloud computing system.

13. The computer readable medium of claim 11, wherein the resource data comprises at least one of, a set of resource environment data, or a set of resource usage data.

14. The computer readable medium of claim 11, the set of facts further comprising instructions which, when stored in memory and executed by the processor causes the act of receiving a resource rule, wherein determining the workload placement plan is subject to at least one of the resource rules.

15. The computer readable medium of claim 11, wherein determining the workload placement plan is based at least in part on an objective.

16. The computer readable medium of claim 11, wherein the workload placement plan is determined at least in part by at least one of, a multi-objective optimization algorithm, or a logistic regression algorithm.

17. The computer readable medium of claim 11, wherein the on-premises resource or the cloud-based resource comprises at least one of, a virtualized entity, a virtual machine, an application, and application extension, an app, or a container.

18. The computer readable medium of claim 11, wherein the workload placement plan comprises a set of workload attribute translation instructions.

19. A system for deploying computing workloads across heterogeneous computing systems, the system comprising:

a storage medium having stored thereon a sequence of instructions; and

a processor that execute the sequence of instructions to cause a set of acts, the set of acts comprising,

establishing network communications between an on-premises computing system associated with an on-premises resource and a cloud computing system associated with a cloud-based resource;

collecting resource data corresponding to the on-premises resource and the cloud-based resource;

receiving a workload placement request, the workload placement request being described by a workload placement request attribute;

generating a workload placement plan;

evaluating the workload placement plan, wherein the evaluating of the workload placement plan considers at least a portion of the resource data corresponding to the on-premises resource and considers at least a portion of the resource data corresponding to the cloud-based resource;

generating a scheduling command to carry out the workload placement plan; and

deploying computing workloads by executing the scheduling command on the on-premises resource or the cloud-based resource.

20. The system of claim 19, wherein at least one API is used to access at least one of, the on-premises computing system, or the cloud computing system.