WORKLOAD OPTIMIZATION, SCHEDULING, AND PLACEMENT FOR RACK-SCALE ARCHITECTURE COMPUTING SYSTEMS

• US 20160359683A1
• Filed: 02/24/2015
• Published: 12/08/2016
• Est. Priority Date: 02/27/2014
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: BARTFAI-WALCOTT, Katalin K., WOODS, Chris, ESTRADA, Giovani, KENNEDY, John, BUTLER, Joseph, LECKEY, Alexander, BAYON-MOLINO, Victor M., UPTON, Connor, METSCH, Thijs, PUTYRSKI, Slawomir
• Granted Patent: US 10,404,547 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 41/0883 Semiautomatic configuration, e.g. proposals from system
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/149 for prediction of maintenance
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• 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 41/5054 Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
• CPC Class Codes: H04L 43/0817 by checking functioning
• CPC Class Codes: H04L 43/20 the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV

Abstract

Technologies for datacenter management include one or more computing racks each including a rack controller. The rack controller may receive system, performance, or health metrics for the components of the computing rack. The rack controller generates regression models to predict component lifespan and may predict logical machine lifespans based on the lifespan of the included hardware components. The rack controller may generate notifications or schedule maintenance sessions based on remaining component or logical machine lifespans. The rack controller may compose logical machines using components having similar remaining lifespans. In some embodiments the rack controller may validate a service level agreement prior to executing an application based on the probability of component failure. A management interface may generate an interactive visualization of the system state and optimize the datacenter schedule based on optimization rules derived from human input in response to the visualization. Other embodiments are described and claimed.

Claims

1-25. -25. (canceled)

26. A rack controller of a computing rack, the rack controller comprising:

a performance monitoring module to receive a metric associated with a hardware component of the computing rack, wherein the metric comprises a system metric, a performance metric, or a health metric; and

an analytics module to;

determine a regression model for the hardware component based on the metric associated with the hardware component;

determine a mean-time-to-failure value for the hardware component based on the regression model for the hardware component; and

determine a mean-time-to-failure value for a logical machine based on the mean-time-to-failure value for the hardware component, wherein the logical machine is associated with the hardware component.

27. The rack controller of claim 26, wherein to receive the metric comprises to receive the metric from a metric component of the hardware component.

28. The rack controller of claim 26, wherein the hardware component comprises a compute resource, a memory resource, a storage resource, or a network resource.

29. The rack controller of claim 26, wherein to determine the regression model comprises to determine a linear regression model.

30. The rack controller of claim 26, wherein to determine the regression model comprises to determine a non-linear regression model.

31. The rack controller of claim 26, wherein to determine the mean-time-to-failure value for the hardware component comprises to:

determine a predicted metric associated with the hardware component based on the regression model; and

compare the predicted metric to a predefined threshold metric.

32. The rack controller of claim 26, wherein to determine the mean-time-to-failure value for the hardware component comprises to:

determine a predicted metric associated with the hardware component based on the regression model;

determine a service level metric of a service level agreement associated with the hardware component; and

compare the predicted metric to the service level metric.

33. The rack controller of claim 26, further comprising a datacenter management module to notify a user of the mean-time-to-failure value for the hardware component.

34. The rack controller of claim 26, further comprising a datacenter management module to determine a future time for a maintenance session associated with the logical machine based on the mean-time-to-failure value for the logical machine.

35. The rack controller of claim 34, wherein:

the performance monitoring module is further to receive a performance indicator associated with a computing application assigned to the logical machine; and

to determine the future time further comprises to determine the future time based on the performance indicator.

36. The rack controller of claim 26, further comprising a datacenter management module to:

identify a plurality of hardware components of the computing rack, wherein the plurality of hardware components comprises the hardware component and wherein a mean-time-to-failure value associated with each of the hardware components is similar to the mean-time-to-failure value of the hardware component; and

compose the logical machine to include the plurality of hardware components.

37. A method for datacenter management, the method comprising:

receiving, by a rack controller of a computing rack, a metric associated with a hardware component of the computing rack, wherein the metric comprises a system metric, a performance metric, or a health metric;

determining, by the rack controller, a regression model for the hardware component based on the metric associated with the hardware component;

determining, by the rack controller, a mean-time-to-failure value for the hardware component based on the regression model for the hardware component; and

determining, by the rack controller, a mean-time-to-failure value for a logical machine based on the mean-time-to-failure value for the hardware component, wherein the logical machine is associated with the hardware component.

38. The method of claim 37, wherein receiving the metric comprises receiving the metric from a metric component of the hardware component.

39. The method of claim 37, wherein determining the mean-time-to-failure value for the hardware component comprises:

determining a predicted metric associated with the hardware component based on the regression model; and

comparing the predicted metric to a predefined threshold metric.

40. The method of claim 37, wherein determining the mean-time-to-failure value for the hardware component comprises:

determining a predicted metric associated with the hardware component based on the regression model;

determining a service level metric of a service level agreement associated with the hardware component; and

comparing the predicted metric to the service level metric.

41. The method of claim 37, further comprising determining, by the rack controller, a future time for a maintenance session associated with the logical machine based on the mean-time-to-failure value for the logical machine.

42. The method of claim 41, further comprising:

receiving, by the rack controller, a performance indicator associated with a computing application assigned to the logical machine;

wherein determining the future time further comprises determining the future time based on the performance indicator.

43. The method of claim 37, further comprising:

identifying, by the rack controller, a plurality of hardware components of the computing rack, wherein the plurality of hardware components comprises the hardware component and wherein a mean-time-to-failure value associated with each of the hardware components is similar to the mean-time-to-failure value of the hardware component; and

composing, by the rack controller, the logical machine to include the plurality of hardware components.

44. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a rack controller of a computing rack to:

receive a metric associated with a hardware component of the computing rack, wherein the metric comprises a system metric, a performance metric, or a health metric;

determine a regression model for the hardware component based on the metric associated with the hardware component;

determine a mean-time-to-failure value for the hardware component based on the regression model for the hardware component; and

determine a mean-time-to-failure value for a logical machine based on the mean-time-to-failure value for the hardware component, wherein the logical machine is associated with the hardware component.

45. The one or more computer-readable storage media of claim 44, wherein to receive the metric comprises to receive the metric from a metric component of the hardware component.

46. The one or more computer-readable storage media of claim 44, wherein to determine the mean-time-to-failure value for the hardware component comprises to:

determine a predicted metric associated with the hardware component based on the regression model; and

compare the predicted metric to a predefined threshold metric.

47. The one or more computer-readable storage media of claim 44, wherein to determine the mean-time-to-failure value for the hardware component comprises to:

determine a predicted metric associated with the hardware component based on the regression model;

determine a service level metric of a service level agreement associated with the hardware component; and

compare the predicted metric to the service level metric.

48. The one or more computer-readable storage media of claim 44, further comprising a plurality of instructions that in response to being executed cause the rack controller to determine a future time for a maintenance session associated with the logical machine based on the mean-time-to-failure value for the logical machine.

49. The one or more computer-readable storage media of claim 48, further comprising a plurality of instructions that in response to being executed cause the rack controller to:

receive a performance indicator associated with a computing application assigned to the logical machine;

wherein to determine the future time further comprises to determine the future time based on the performance indicator.

50. The one or more computer-readable storage media of claim 44, further comprising a plurality of instructions that in response to being executed cause the rack controller to:

identify a plurality of hardware components of the computing rack, wherein the plurality of hardware components comprises the hardware component and wherein a mean-time-to-failure value associated with each of the hardware components is similar to the mean-time-to-failure value of the hardware component; and

compose the logical machine to include the plurality of hardware components.