INTELLIGENT MANAGEMENT OF VIRTUALIZED RESOURCES FOR CLOUD DATABASE SYSTEMS

• US 20120022910A1
• Filed: 01/05/2011
• Published: 01/26/2012
• Est. Priority Date: 07/20/2010
• Status: Active Grant

• Current Assignee: Zoom Video Communications, Inc.
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Chi, Yun, Moon, Hyun Jin, Hacigumus, Vahit Hakan, Xiong, Pengcheng, Zhu, Shenghuo
• Granted Patent: US 8,359,223 B2
• Time in Patent Office: Days
• US Class Current: 705/7.22
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling

Abstract

Systems and methods are disclosed to manage resources in a cloud-based computing system by generating a model of a relationship between cloud database resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances, wherein the model comprises a two level optimization/control problem, wherein model receives system metrics, number of replicas, and arrival rate as the multiple input; and dynamically adjusting resource allocation among different customers based on current customer workload and the expected profit to maximize the expected profit for a cloud computing service provider.

Claims

1. A method to manage resources in a cloud-based computing system, comprising:

a. generating a model of a relationship between cloud database resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances, wherein the model comprises a two level optimization and control problem and wherein model receives system metrics, number of replicas, and arrival rate as inputs; and

b. dynamically adjusting a system resource allocation among different customers based on current customer workload and the expected profit to maximize the expected profit for a cloud computing service provider.

2. The method of claim 1, comprising predicting the profit under different system resource allocations.

3. The method of claim 1, comprising building a predictive model to determine a potential profit gain under different system resource allocations.

4. The method of claim 1, comprising obtaining an initial performance predictive model that predicts the expected profit under a predetermined resource allocation.

5. The method of claim 1, comprising updating the predictive model with feedbacks learned from a real-time system resource allocation and a corresponding cost.

6. The method of claim 1, comprising generating a decision strategy that considers the expected profit and an expense for making a change in the system resource allocation.

7. The method of claim 1, comprising changing the system resource allocation in each allowed time-interval.

8. The method of claim 1, comprising collecting new observations on performance under a new setting of system resource allocation.

9. The method of claim 1, comprising:

determining a weighted SLA penalty cost function as;

P_w(q,i)=P(q,i)×

w(i)

where within the k-th interval, an average SLA penalty cost for the i-th class of clients is a total SLA penalty cost over the total number of queries L(i,k) during the interval;

determining an average cost function AC as;

10. The method of claim 1, comprising:

a. predicting the profit under different system resource allocations;

b. building a predictive model to determine potential profit gain under different system resource allocations;

c. obtaining an initial performance predictive model that predicts the expected profit under a resource allocation;

d. updating the predictive model with feedbacks learned from a real-time system resource allocation and corresponding cost;

e. generating a decision strategy that considers the expected profit and expense for making a change in the system resource allocation;

f. changing the system resource allocation in each allowed time-interval; and

g. collecting new observations on performance under a new setting of system resource allocation.

11. A system, comprising

a. a model generator to build a model of relationships between allocated resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances;

and

b. a dynamic control module coupled to the model generator, the dynamic control module adjusting resource allocations among different customers based on current customer workload and the expected profit.

12. The system of claim 11, wherein the system parameter comprises CPU share, memory share, database replicas.

13. The system of claim 11, wherein the control module considers a response smoothness to avoid jitter experienced by customers and expense for re-configuring the system resource allocation.

14. The system of claim 11, comprising means for predicting the profit under different system resource allocations.

15. The system of claim 11, comprising means for building a predictive model to determine potential profit gain under different system resource allocations.

16. The system of claim 11, comprising means for obtaining an initial performance predictive model that predicts the expected profit under a resource allocation.

17. The system of claim 11, comprising means for updating the predictive model with feedbacks learned from a real-time system resource allocation and corresponding cost.

18. The system of claim 11, comprising means for generating a decision strategy that considers the expected profit and expense for making a change in system resource allocation.

19. The system of claim 11, comprising:

means for determining a weighted SLA penalty cost function as;

P_w(q,i)=P(q,i)×

w(i)

where within the k-th interval, an average SLA penalty cost for the i-th class of clients is a total SLA penalty cost over the total number of queries L(i,k) during the interval;

means for determining an average cost function AC as;

20. A system, comprising:

a. a model generator to build a model of relationships between allocated resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances; and

b. a dynamic control module coupled to the model generator, the dynamic control module adjusting resource allocation among different customers based on current customer workload and the expected profit

c. means for predicting the profit under different system resource allocations;

d. means for building a predictive model to determine potential profit gain under different system resource allocations;

e. means for obtaining an initial performance predictive model that predicts the expected profit under a resource allocation;

f. means for updating the predictive model with feedbacks learned from a real-time system resource allocation and corresponding cost;

g. means for generating a decision strategy that considers the expected profit and expense for making a change in the system resource allocation;

h. means for changing the system resource allocation in each allowed time-interval; and

i. means for collecting new observations on performance under a new setting of system resource allocation.