Providing energy consumption analytics of cloud based service

• US 9,645,629 B2
• Filed: 09/25/2014
• Issued: 05/09/2017
• Est. Priority Date: 09/25/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Mitchell, Kent, Strader, Dan, Moon, Carroll, McCurdy, Hollie, Nikodym, Lou
• Primary Examiner(s): Rehman, Mohammed
• Assistant Examiner(s): PANDEY, KESHAB R
• Application Number:
  US14/497,053
• Publication Number:
  US 20160091948A1
• Time in Patent Office: 957 Days
• Field of Search: 713300, 713310, 713320, 713321, 713322, 713323, 713324, 713330, 713340, 713375
• CPC Class Codes: G06F 1/32 Means for saving power
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• CPC Class Codes: G06F 1/3206 Monitoring of events, devices or parameters that trigger a change in power modality
• CPC Class Codes: G06F 11/3031 where the computing system component is a motherboard or an expansion card
• CPC Class Codes: G06F 11/3055 Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction by redundancy in operation G06F11/14; error detection or correction by redundancy in hardware G06F11/16
• CPC Class Codes: G06F 11/3062 where the monitored property is the power consumption power management in a computing system G06F1/3203
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Energy consumption analytics of a cloud based service is provided. An application such as a power analytics application monitors power consumption data of a server of the cloud based service. The power consumption data of the server is captured in a data file, a data store, a temporary storage, etc. The power consumption data is analyzed to detect power consumption metrics for the server. The power consumption metrics are presented in a business intelligence data structure.

Claims

1. A method executed on a computing device to provide energy consumption analytics of a cloud based service, the method comprising:

monitoring power consumption data of servers in an operational group of servers of the cloud based service, wherein the operational group of servers share a common functionality based role and a common usage pattern, wherein the common functionality based role includes one or more of processing and data storage management, and the common usage pattern includes a service executed by the operational group of servers;

capturing the power consumption data;

analyzing the power consumption data to detect;

power consumption metrics of the servers in the operational group, wherein the power consumption metrics includes a total consumed power by the servers in the operational group and by individual sub-components of the servers in the operational group,

an upper threshold for an optimal power consumption of the operational group, and

a lower threshold for the optimal power consumption of the operational group;

presenting the power consumption metrics in a business intelligence data structure;

and

allowing a stakeholder to

configure a new asset to be added to the operational group to conform to the optimal power consumption of the operational group, wherein the new asset includes one of a server and a server component, and

configure an application that executes on one or more of the servers in the operational group to increase or decrease operational activities to stay within the optimal power consumption of the operational group.

2. The method of claim 1, wherein

the operational group is created based on a configuration of the servers in the operational group and one or more peers.

3. The method of claim 2, further comprising:

capturing and analyzing the power consumption data of the one or more peers to detect power consumption metrics for the one or more peers; and

presenting the power consumption metrics of the one or more peers with the power consumption metrics of the servers in the operational group in the business intelligence data structure.

4. The method of claim 2, further comprising:

computing an average power consumption of the operational group; and

comparing the power consumption metrics of the servers in the operational group to the average power consumption.

5. The method of claim 4, further comprising:

detecting the power consumption metrics of the servers in the operational group to exceed the average power consumption;

labeling the servers in the operational group as an outlier in the business intelligence data structure; and

generating an alert to inform a stakeholder of the cloud based service that a status of the servers in the operational group is one or more from a set of;

incorrectly configured and malfunctioning.

6. The method of claim 1, further comprising:

detecting the power consumption metrics of the servers in the operational group to exceed the upper threshold; and

generating an alert to inform a stakeholder of the cloud based service that a status of the servers in the operational group is one or more from a set of;

overused, incorrectly configured, and malfunctioning.

7. The method of claim 1, further comprising:

detecting the power consumption metrics of the servers in the operational group to fall below the lower threshold; and

generating an alert to inform a stakeholder of the cloud based service that a status of the servers in the operational group is one or more from a set of;

underused, incorrectly configured, and malfunctioning.

8. A computing device to provide energy consumption analytics of a cloud based service, the computing device comprising:

a memory;

a processor coupled to the memory, the processor executing a power analytics application in conjunction with instructions stored in the memory, wherein the power analytics application is configured to;

monitor power consumption data of an operational group of servers of the cloud based service and one or more peers based on configuration of the servers in the operational group and the one or more peers, wherein the operational group of servers and the one or more peers share a common functionality based role and a common usage pattern, wherein the common functionality based role includes one or more of processing and data storage management, and the common usage pattern includes a service executed by the operational group of servers;

capture the power consumption data;

analyze the power consumption data to detect;

power consumption metrics for the operational group, wherein the power consumption metrics includes a total consumed power by the operational group and by individual sub-components of servers in the operational group within the operational group,

an upper threshold for an optimal power consumption of the operational group, and

a lower threshold for the optimal power consumption of the operational group; and

present the power consumption metrics in a business intelligence data structure;

and

allow a stakeholder to

configure a new asset to be added to the operational group to conform to the optimal power consumption of the operational group, and

configure an application that executes on one or more of the servers in the operational group and the one or more peers to increase or decrease operational activities to stay within the optimal power consumption of the operational group.

9. The computing device of claim 8, wherein the power analytics application is further configured to:

analyze processor energy consumption associated with one or more processors of members of the operational group from the power consumption data.

10. The computing device of claim 9, wherein the power analytics application is further configured to:

generate a graph of the processor energy consumption of the servers in the operational group and the power consumption metrics of the servers in the operational group to show a relationship between the processor energy consumption and the power consumption metrics; and

add the graph to the business intelligence data structure.

11. The computing device of claim 9, wherein the power analytics application is further configured to:

generate a graph of the processor energy consumption of the servers in the operational group, the power consumption metrics of the servers in the operational group, the processor energy consumption of the one or more peers, and the power consumption metrics of the one or more peers to illustrate a comparison relationship between the server and the one or more peers; and

add the graph to the business intelligence data structure.

12. The computing device of claim 8, wherein the power analytics application is further configured to:

analyze a hard disk drive (HDD) energy consumption associated with one or more hard disks of members of the operational group from the power consumption data.

13. The computing device of claim 12, wherein the power analytics application is further configured to:

generate a graph of the HDD energy consumption of the servers in the operational group, the power consumption metrics of the servers in the operational group, the HDD energy consumption of the one or more peers, and the power consumption metrics of the one or more peers to illustrate a comparison relationship between the servers in the operational group and the one or more peers; and

add the graph to the business intelligence data structure.

14. A method to provide energy consumption analytics of a cloud based service, the method comprising:

monitoring power consumption data of an operational group of servers of the cloud based service and one or more peers based on configuration of the servers in the operational group and the one or more peers, wherein the operational group of servers share a common functionality based role and a common usage pattern, wherein the common functionality based role includes one or more of processing and data storage management, and the common usage pattern includes a service executed by the operational group of servers;

capturing the power consumption data;

analyzing the power consumption data to detect;

power consumption metrics of the servers in the operational group,

wherein the power consumption metrics includes a total consumed power by the operational group and by individual sub-components of servers in the operational group within the operational group,

an upper threshold for an optimal power consumption of the operational group, and

a lower threshold for the optimal power consumption of the operational group; and

presenting the power consumption metrics to the in a business intelligence data structure; and

allowing a stakeholder to

configure a new asset to be added to the operational group to conform to the optimal power consumption of the operational group, and

configure an application that executes on one or more of the servers in the operational group and the one or more peers to increase or decrease operational activities to stay within the optimal power consumption of the operational group.

15. The method of claim 14, further comprising:

receiving the power consumption data from one or more client detectors measuring power drawn in one or more components of the servers in the operational group and the one or more peers.

16. The method of claim 14, further comprising:

analyzing processor energy consumption associated with one or more processors of members of the operational group from the power consumption data;

generating a graph of the processor energy consumption of the servers in the operational group, the power consumption metrics of the servers in the operational group, the processor energy consumption of the one or more peers, and the power consumption metrics of the one or more metrics to illustrate a comparison relationship between the servers in the operational group and the one or more peers; and

adding the graph to the business intelligence data structure.