Cloud Service Component Placement Optimization
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Solution Overview
Problem
Current solutions for optimizing software component placement in cloud computing networks are limited, as they often focus on single services, rely on specific optimization scenarios, and fail to consider a comprehensive set of parameters, leading to suboptimal placement and fragmentation over time, especially with the increasing complexity of microservices and heterogeneous infrastructure.
Innovation Solution
A computer-implemented method and system that receives service placement requests, obtains optimization criteria and static/dynamic information, creates a placement optimization description, and computes the optimal placement of software components across multiple data centers, considering resource availability, metrics, and constraints to generate a service deployment model that balances utilization, cost, and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software components are deployed to multiple data centers using existing solutions, then service availability is improved, but placement optimization is insufficient leading to suboptimal resource utilization
Solution Approach 1:
The placement optimization system is designed to handle multiple cloud services and software components simultaneously through a universal optimization criterion framework. The system can optimize placement for different service types (microservices, monoliths, event-driven architectures) using the same core optimization engine, which considers multiple factors including resource utilization, cost, performance, and service-level agreements across heterogeneous data center infrastructures.
Solution Approach 2:
The system dynamically adjusts placement decisions by changing multiple parameters simultaneously - not just resource availability but also cost metrics, performance requirements, service-level agreements, and optimization criteria. This multi-parameter optimization approach allows the system to transition from simple availability-based placement to comprehensive optimization that simultaneously improves reliability and resource utilization efficiency.
2Ease of operation
If existing placement solutions are used, then deployment is simplified, but they only optimize single services leading to fragmentation over time
Solution Approach 1:
The system performs preliminary global optimization analysis before actual deployment by evaluating multiple services and components together. It pre-calculates optimal placement configurations considering all services in the cloud computing network, preventing fragmentation before it occurs. This advance planning ensures consistent placement decisions across all services while maintaining deployment simplicity through automated decision-making.
Solution Approach 2:
The system implements continuous feedback mechanisms that monitor placement decisions across multiple services. It tracks placement consistency, resource utilization patterns, and service performance over time, using this feedback to refine future placement decisions. This feedback loop ensures that the system maintains stable and consistent placement configurations while adapting to changing conditions, preventing fragmentation without complicating deployment operations.
3Manufacturing precision
If comprehensive optimization criteria are applied, then placement quality is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex optimization problem into manageable components by processing services and components in organized groups. It divides the cloud computing network into logical segments for optimization analysis, evaluating placement decisions in a structured manner that reduces computational overhead while maintaining comprehensive optimization quality across all services.
Solution Approach 2:
The system applies optimization criteria selectively based on service priorities and requirements. For critical services requiring high placement quality, it applies comprehensive optimization with all criteria. For less critical services, it uses simplified optimization approaches. This partial application of full optimization complexity achieves high placement quality where needed while managing overall computational complexity through differentiated optimization strategies.
Data Source
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AI summary
A method and system for optimizing placement of a plurality of software components of cloud services in a cloud computing network, generates a service deployment model with a placement optimization description described as constrained optimization problem, by matching service constraints to infrastructure capacity in combination with optimization criterium, thus enable the method and system to deploy new services and/or redeploy existing services in a profitable, efficient and flexible way.