Cloud Database Resource Allocation via Profit Modeling
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Solution Overview
Problem
Current resource management strategies for cloud database systems lack an intelligent feedback process to dynamically adjust resource allocation based on changing workloads and service level agreements (SLAs), leading to inefficiencies in cost management and profit optimization.
Innovation Solution
A machine learning-based system, SmartSLA, that models the relationship between cloud database resources and expected profit, dynamically adjusts resource allocation to maximize profit by considering SLA costs, client workloads, and infrastructure costs, using a two-level optimization approach with a system modeling module and a resource allocation decision module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If multiple tenants are consolidated into a physical machine to improve cost efficiency, then infrastructure resource cost efficiency is improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent implements a feedback mechanism where system performance metrics (CPU utilization, memory usage, I/O operations, query response times) are continuously monitored and fed back to the resource allocation module. This feedback loop enables dynamic adjustment of resource allocation to maintain optimal performance while consolidating multiple tenants, thereby reducing management complexity despite increased consolidation.
Solution Approach 2:
The system employs dynamic resource allocation where resource partitions assigned to each tenant are not fixed but can be adjusted in real-time based on current workload demands. The resource allocation module dynamically modifies CPU shares, memory allocations, and I/O capacities for different tenants, enabling efficient consolidation while adapting to changing conditions to simplify management.
2Ease of operation
If resource allocation is manually managed to maintain simple system operation, then ease of operation is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system implements self-service automation where the resource allocation module autonomously monitors system metrics, evaluates tenant workload patterns, and adjusts resource allocations without human intervention. The automated feedback loop and machine learning-based predictions enable the system to self-optimize resource distribution, maintaining operational simplicity while dramatically improving allocation efficiency and scalability.
Solution Approach 2:
The patent replaces manual mechanical resource management processes with an automated electronic control system. The resource allocation module uses electronic monitoring of system metrics and automated decision-making algorithms to substitute human manual allocation, thereby maintaining ease of operation while achieving superior productivity and scalability through intelligent automation.
3Difficulty of detecting and measuring
If existing resource management strategies focusing on system-level metrics are used, then measurement simplicity is improved, but manufacturing precision of profit optimization deteriorates
Solution Approach 1:
The patent implements local quality monitoring by tracking specific tenant-level metrics in addition to system-level metrics. The system monitors CPU utilization, memory usage, I/O operations, and query response times for each individual tenant, enabling precise identification of which specific tenants or workloads are affecting overall system performance. This granular measurement approach improves profit optimization precision by enabling targeted resource allocation decisions.
Solution Approach 2:
The system adds another dimension to resource management by incorporating machine learning-based predictive analytics. Beyond traditional system-level metrics, the patent introduces predictive models that forecast future resource demands and performance outcomes, enabling the system to optimize profit not just based on current state but on anticipated future conditions, thereby achieving superior optimization precision.
4Reliability
If a private virtual machine is allocated to each tenant to improve reliability and SLA satisfaction, then service reliability is improved, but cost efficiency and resource utilization deteriorate
Solution Approach 1:
The patent merges multiple tenants onto shared physical infrastructure through virtualization, combining resource pools that can be dynamically allocated. Instead of dedicating separate physical hardware to each tenant, the system consolidates tenants on shared machines with virtualized resources, thereby reducing overall infrastructure costs while maintaining isolation and reliability through virtual machine boundaries and controlled resource partitioning.
Solution Approach 2:
The system employs dynamic resource allocation where the same physical infrastructure can adaptively serve different tenant workloads at different times. Resource partitions are dynamically adjusted based on current demands, allowing the system to maintain high reliability and SLA satisfaction for each tenant while achieving superior cost efficiency through shared infrastructure and flexible resource sharing.
Data Source
AI summary
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.