Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments

Patent metadata

Field	Value
Publication number	US-9576240-B2
Application number	US-201314020711-A
Country	US
Kind code	B2
Filing date	Sep 6, 2013
Priority date	Jul 2, 2009
Publication date	Feb 21, 2017
Grant date	Feb 21, 2017

Abstract

Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

First claim

What is claimed is:

1. A system, comprising:
at least one processor; and
a non-transitory processor-readable memory device storing instructions that when executed by said at least one processor causes said at least one processor to perform operations including:
generating a customized cost model for an electronic device based on one or more cost preferences corresponding to said electronic device; and
based on said customized cost model, reducing a power consumption cost associated with execution of computer executable code on said electronic device by generating a decision relating to allocation of one or more individual executable components of said computer executable code between said electronic device and one or more cloud computing resources for execution.

2. The system of claim 1 , wherein the operations further comprise:
obtaining sensor information associated with said electronic device;
obtaining sensor information associated with said one or more cloud computing resources; and
distinguishing sensor information associated with said electronic device from sensor information associated with said one or more cloud computing resources;
wherein said customized cost model is further based on sensor information associated with said electronic device and sensor information associated with said one or more cloud computing resources.

3. The system of claim 1 , wherein:
said customized cost model identifies a plurality of parameters to monitor, and said decision is further based on said plurality of parameters; and
said decision minimizes said power consumption cost.

4. The system of claim 3 , wherein:
at least one of said plurality of parameters is monitored by said electronic device; and
at least one of said plurality of parameters is monitored by said one or more cloud computing resources.

5. The system of claim 3 , wherein said plurality of parameters includes at least one of the following:
for each individual executable component allocated to said electronic device, an execution power cost associated with said individual executable component;
a transportation power cost associated with communicating data between said electronic device and said one or more cloud computing resources; and
size of data communicated between at least one individual executable component allocated to said electronic device and at least one individual executable component allocated to said one or more cloud computing resources.

6. The system of claim 5 , wherein said transportation power cost is based on at least one of the following: signal strength between said electronic device and said one or more cloud computing resources, status of network traffic between said electronic device and said one or more cloud computing resources, and one or more network interfaces on said electronic device.

7. The system of claim 1 , wherein said decision comprises a decision to offload, during runtime of said one or more individual executable components, at least one of said one or more individual executable components to said one or more cloud computing resources.

8. The system of claim 1 , wherein said decision comprises a decision to onload, during runtime of said one or more individual executable components, at least one of said one or more individual executable components on said electronic device.

9. The system of claim 1 , wherein the operations further comprise:
generating another customized cost model for multiple electronic devices having at least one of similar cost preferences and similar device configuration data.

10. A method, comprising:
generating a customized cost model for an electronic device based on one or more cost preferences corresponding to said electronic device; and
based on said customized cost model, reducing a power consumption cost associated with execution of computer executable code on said electronic device by generating a decision relating to allocation of one or more individual executable components of said computer executable code between said electronic device and one or more cloud computing resources for execution.

11. The method of claim 10 , further comprising:
obtaining sensor information associated with said electronic device;
obtaining sensor information associated with said one or more cloud computing resources; and
distinguishing sensor information associated with said electronic device from sensor information associated with said one or more cloud computing resources;
wherein said customized cost model is further based on sensor information associated with said electronic device and sensor information associated with said one or more cloud computing resources.

12. The method of claim 10 , further comprising:
monitoring a plurality of parameters identified by said customized cost model, and said decision is further based on said plurality of parameters;
wherein said decision minimizes said power consumption cost.

13. The method of claim 12 , wherein:
at least one of said plurality of parameters is monitored by said electronic device; and
at least one of said plurality of parameters is monitored by said one or more cloud computing resources.

14. The method of claim 12 , wherein said plurality of parameters includes at least one of the following:
for each individual executable component allocated to said electronic device, an execution power cost associated with said individual executable component;
a transportation power cost associated with communicating data between said electronic device and said one or more cloud computing resources; and
size of data communicated between at least one individual executable component allocated to said electronic device and at least one individual executable component allocated to said one or more cloud computing resources.

15. The method of claim 14 , wherein said transportation power cost is based on at least one of the following: signal strength between said electronic device and said one or more cloud computing resources, status of network traffic between said electronic device and said one or more cloud computing resources, and one or more network interfaces on said electronic device.

16. The method of claim 10 , wherein said decision comprises a decision to offload, during runtime of said one or more individual executable components, at least one of said one or more individual executable components to said one or more cloud computing resources.

17. The method of claim 10 , wherein said decision comprises a decision to onload, during runtime of said one or more individual executable components, at least one of said one or more individual executable components on said electronic device.

18. The method of claim 10 , further comprising:
generating another customized cost model for multiple electronic devices having at least one of similar cost preferences and similar device configuration data.

19. An electronic device, comprising:
at least one processor; and
a non-transitory processor-readable memory device storing instructions that when executed by said at least one processor causes said at least one processor to perform operations including:
receiving a customized cost model for said electronic device based on one or more cost preferences corresponding to said electronic device; and
based on said customized cost model, reducing a power consumption cost associated with execution of computer executable code on said electronic device by generating a decision relating to allocation of one or more individual executable components of said computer

Assignees

• Samsung Electronics Co Ltd

Inventors

• Jeong Sangoh
• Gibbs Simon J
• Zhang Xinwen
• Kunjithapatham Anugeetha

Classifications

• G06F9/5066 Primary

  Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs (mappping at compile time, see G06F8/451) · CPC title

• G06N5/02 Primary

  Knowledge representation; Symbolic representation · CPC title

• G06F9/4494

  data driven · CPC title

• G06F9/5094

  where the allocation takes into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206) · CPC title

• G06F9/5044

  considering hardware capabilities · CPC title