Job distribution within a grid environment using mega-host groupings of execution hosts

• US 10,664,308 B2
• Filed: 10/02/2018
• Issued: 05/26/2020
• Est. Priority Date: 06/20/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chen, Chong, Liu, Fang, Wang, Qi, Yuan, Shutao
• Primary Examiner(s): Ghaffari, Abu Zar
• Application Number:
  US16/150,163
• Publication Number:
  US 20190042309A1
• Time in Patent Office: 602 Days
• CPC Class Codes: G06F 2209/5012 Processor sets
• CPC Class Codes: G06F 2209/505 Clust
• CPC Class Codes: G06F 9/4881 Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
• CPC Class Codes: G06F 9/5038 considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration scheduling strategies G06F9/4881 and subgroups
• CPC Class Codes: G06F 9/5044 considering hardware capabilities
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: G06F 9/5061 Partitioning or combining of resources
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers

Abstract

A technique for job distribution within a grid environment includes receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. An optimum execution cluster is selected for receiving the job based on a weighting factor applied to select resources of the respective execution clusters.

Claims

1. A method for job distribution within a grid environment, comprising:

receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts;

determining resource attributes corresponding to each execution host of the execution clusters;

grouping, for each execution cluster, execution hosts based on the resource attributes of the respective execution hosts;

defining, for each grouping of execution hosts, a mega-host for the respective execution cluster, the mega-host for a respective execution cluster based on combining select resource attributes of the respective grouped execution hosts;

determining resource requirements for the job;

selecting an optimum execution cluster for receiving the job based on a weighting factor applied to select resources of the respective execution clusters;

identifying candidate mega-hosts within the optimum execution cluster for the job based on the resource attributes of the respective mega-hosts and the resource requirements of the job; and

selecting at least one of the candidate mega-hosts within the optimum execution cluster for allocating the job thereto for execution of the job.

2. The method of claim 1, further comprising grouping the execution hosts for a respective execution cluster based on resource slots and memory capacity attributes for the respective execution hosts.

3. The method of claim 1, further comprising sorting and ordering the candidate mega-hosts based on available resources of the respective mega-hosts.

4. The method of claim 3, further comprising sorting and ordering the candidate mega-hosts based on slot availability.

5. A system for job distribution within a grid environment, comprising:

a submission cluster, having a processor, for distributing a job to at least one of a plurality of execution clusters, wherein each execution cluster includes one or more execution hosts, and wherein the submission cluster comprises logic executable by a processor unit to;

determine resource attributes corresponding to each execution host of the execution clusters;

group, for each execution cluster, execution hosts based on the resource attributes of the respective execution hosts;

define, for each grouping of execution hosts, a mega-host for the respective execution cluster, the mega-host for a respective execution cluster based on combining select resource attributes of the respective grouped execution hosts;

determine resource requirements for the job;

select an optimum execution cluster for receiving the job based on a weighting factor applied to select resources of the respective execution clusters;

identify candidate mega-hosts within the optimum execution cluster for the job based on the resource attributes of the respective mega-hosts and the resource requirements of the job; and

select at least one of the candidate mega-hosts within the optimum execution cluster for allocating the job thereto for execution of the job.

6. The system of claim 5, wherein the logic is executable to group the execution hosts for a respective execution cluster based on resource slots and memory capacity attributes for the respective execution hosts.

7. The system of claim 5, wherein the logic is executable to sort and order the candidate mega-hosts based on available resources of the respective mega-hosts.

8. The system of claim 7, wherein the logic is executable to sort and order the candidate mega-hosts based on slot availability.

9. A computer program product for job distribution within a grid environment, the computer program product comprising:

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

receive a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts;

determine resource attributes corresponding to each execution host of the execution clusters;

group, for each execution cluster, execution hosts based on the resource attributes of the respective execution hosts;

define, for each grouping of execution hosts, a mega-host for the respective execution cluster, the mega-host for a respective execution cluster based on combining select resource attributes of the respective grouped execution hosts;

determine resource requirements for the job;

select an optimum execution cluster for receiving the job based on a weighting factor applied to select resources of the respective execution clusters;

identify candidate mega-hosts within the optimum execution cluster for the job based on the resource attributes of the respective mega-hosts and the resource requirements of the job; and

select at least one of the candidate mega-hosts within the optimum execution cluster for allocating the job thereto for execution of the job.

10. The computer program product of claim 9, wherein the computer readable program code is configured to group the execution hosts for a respective execution cluster based on resource slots and memory capacity attributes for the respective execution hosts.

11. The computer program product of claim 9, wherein the computer readable program code is configured to sort and order the candidate mega-hosts based on available resources of the respective mega-hosts.

12. The computer program product of claim 11, wherein the computer readable program code is configured to sort and order the candidate mega-hosts based on slot availability.