Intelligent management of virtualized resources for cloud database systems
First Claim
1. A computer-implemented method to manage resources in a cloud-based computing system, comprising:
- a. generating by a processor a model of a relationship between cloud database resources and a predicted profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for alternate 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 by a processor a system resource allocation among alternate customers based on current customer workload and the predicted profit to maximize the expected profit for a cloud computing service provider while conforming to the SLA requirements.
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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.
61 Citations
19 Claims
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1. A computer-implemented method to manage resources in a cloud-based computing system, comprising:
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a. generating by a processor a model of a relationship between cloud database resources and a predicted profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for alternate 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 by a processor a system resource allocation among alternate customers based on current customer workload and the predicted profit to maximize the expected profit for a cloud computing service provider while conforming to the SLA requirements. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system, comprising:
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a. a model generator to build a model of relationships between allocated resources and a predicted profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for alternate system performances; and b. a dynamic control module coupled to the model generator, the dynamic control module adjusting resource allocations among alternate customers based on current customer workload and the predicted profit. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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a. a model generator to build a model of relationships between allocated resources and a predicted profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for alternate system performances; and b. a dynamic control module coupled to the model generator, the dynamic control module adjusting resource allocation among alternate customers based on current customer workload and the predicted profit c. means for predicting the profit under alternate system resource allocations; d. means for building a predictive model to determine potential profit gain under alternate system resource allocations; e. means for obtaining an initial performance predictive model that predicts the predicted 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 predicted 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.
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Specification