METHOD FOR EXECUTING PROCESSES ON A WORKER MACHINE OF A DISTRIBUTED COMPUTING SYSTEM AND A DISTRIBUTED COMPUTING SYSTEM

• US 20150113539A1
• Filed: 05/25/2012
• Published: 04/23/2015
• Est. Priority Date: 05/25/2012
• Status: Abandoned Application

• Current Assignee: NEC Europe Ltd (NEC Corporation)
• Original Assignee: NEC Europe Ltd (NEC Corporation)
• Inventors: Agarwal, Sachin, Sembiring, Krisantus, Beyer, Andreas
• Application Number:
  US14/402,409
• Publication Number:
  US 20150113539A1
• Time in Patent Office: Days
• US Class Current: 718/104
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 2209/504 Resource capping
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: G06F 9/5088 involving task migration
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention relates to a method for executing processes, preferably media processes on a worker machine of a distributed computing system, with a plurality of worker machines, comprising the steps of a) Selecting one of the worker machines out of the plurality of worker machines for execution of a process to be executed in the distributed computing system and transferring said process to the selected worker machine, b) Executing the transferred process on the selected worker machine, and c) Removing the executed process from the selected worker machine after finishing of the execution of the process, wherein statistical information of resource usage of the process to be executed on one of the worker machines is collected and that the selection of the worker machine is based on a probability resource usage qualifier, wherein the probability resource usage qualifier is extracted from combined statistical information of the process to be executed and already executed and/or executing processes on the worker machine. The invention relates also to a system and a use.

Claims

1. Method for executing processes preferably media processes (102) on a worker machine (111) of a distributed computing system (1), with a plurality of worker machines (111, 112), comprising the steps of

a) Selecting one of the worker machines (111) out of the plurality of worker machines (111, 112) for execution of a process (102) to be executed in the distributed computing system (1) and transferring said process (102) to the selected worker machine (111),

b) Executing the transferred process (102) on the selected worker machine (111), and

c) Removing the executed process (102) from the selected worker machine (111) after finishing of the execution of the process (102),

characterized in that

statistical information of resource usage of the process (102) to be executed on one of the worker machines (111) is collected and that the selection of the one worker machine (111) is based on a probability resource usage qualifier, wherein the probability resource usage qualifier is extracted from combined statistical information of the process (102) to be executed and already executed and/or executing processes (102) on the worker machine (111).

2. The method according to claim 1, characterized in that the process (102) is classified into a process class according to statistical information of resource usage of a process (102) to be executed, wherein processes (102) in the same process class have similar resource usage characteristics.

3. The method according to claim 2, characterized in that processes (102) in the same process class have the same type of content data, preferably media content data, to be processed.

4. The method according to claim 1, characterized in that working machines (111, 112) having no processes (102) to execute are either shutdown or assigned to be a slack machine (112), wherein a slack machine (112) is kept idle for execution of future processes (102).

5. The method according to claim 1, characterized in that a process on a first worker machine (111) having a first probability resource usage qualifier is moved to a second worker machine (111) having a second probability resource usage qualifier when the second probability resource usage qualifier is higher than the first probability resource usage qualifier.

6. The method according to claim 5, characterized in that a moving of a process to another worker machine (111) is checked when another process (102) on a worker machine (111) is terminated on that worker machine (111).

7. The method according to claim 5, characterized in that a moving of a process to another worker machine (111) is only performed if the number of worker machines (111) having an inefficient resource usage is below a fragment threshold.

8. The method according to claim 4, characterized in that the number of slack machines (112) is determined according to a parameter, wherein the parameter is dependant on process classes, statistical information of processes (102) to be executed and/or executing and/or the rate of processes to be executed and/or terminated on the distributed computing system (1).

9. The method according to claim 1, characterized in that process classes are determined by K-means clustering, hierarchical clustering, using of neural networks and/or using of a support vector machine.

10. The method according to claim 1, characterized in that resource usage of processes executed on the distributed computing system (1) are collected periodically.

11. The method according to claim 1, characterized in that the probability resource usage qualifier represents a probability of a process (102) exceeding a predefined maximum resource usage on a worker machine (111).

12. The method according to claim 1, characterized in that the combined statistical information of the process (102) to be executed and already executed and/or executing processes (102) on the worker machine (111) are weighed for extraction of the probability resource usage qualifier.

13. The method according to claim 1, characterized in that the probability resource usage qualifier for media processes (102) following a Gaussian distribution for resource usage is based on the corresponding Q-function.

14. The method according to claim 1, characterized in that the worker machine (111) having the highest probability resource usage qualifier below a certain threshold is selected for execution of the process (102).

15. Distributed computing system (1) for executing processes (102), preferably media processes and preferably for execution with the method according to claim 1, comprising

a plurality of worker machines (111, 112) for execution of processes (102) in the distributed computing system (1) and

an inserter (105) for selecting one of the worker machines (111, 112) out of the plurality of worker machines (111, 112) for execution of a process (102) to be executed and transferring said process (102) to the selected worker machine (111) for execution,

characterized by

the inserter (105) being operable to select one worker machine (111) based on a probability resource usage qualifier, wherein the probability resource usage qualifier is extracted from combined statistical resource usage information of the process (102) to be executed and already executed and/or executing processes.

16. The distributed computing system according to claim 15, characterized by a classifier (106) for classifying the process (102) to be executed into a process class, wherein processes (102) in the same process class have similar resource usages.

17. The distributed computing system according to claim 15, characterized by a model generator (107) for generating and/or updating a model for different process classes, preferably based on data from executed and/or executing processes (102).

18. Use of the method according to claim 1 and/or the distributed computing system according to one of the claims 15-17 for executing media processes.