Cloud Cost-to-Serve Orchestration for Elastic Resource Provisioning
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Solution Overview
Problem
Current systems fail to provide adequate solutions for optimizing app servers, databases, storage, network, and additional services performance, particularly in load testing across multiple use-case scenarios with desired concurrency in a multi-tenant environment, lacking automation and integration of performance and cost analysis.
Innovation Solution
A framework for cross-platform and cross-device performance testing that virtualizes infrastructure topology, enabling automated load testing and cost analysis without manual intervention, using service elasticity and auto-scaling mechanisms to adapt resources based on performance metrics, and integrating performance monitoring APIs to track server requests and metrics across app servers, databases, and networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated resource provisioning and elastic services are implemented, then service response time is optimized and resource over-provisioning is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system comprising an orchestration module, cost-to-serve module, and performance metrics collection module that mediates between resource demands and provisioning decisions. This intermediary layer automates the complex tasks of resource allocation, cost calculation, and performance monitoring, thereby optimizing service response time while managing system complexity through modular design.
2Productivity
If manual intervention is used for performance testing and cost analysis, then system complexity is reduced, but productivity and automation level decrease
Solution Approach 1:
The system implements self-service capabilities where the automated performance testing module and cost-to-serve module autonomously execute performance tests, collect metrics, calculate costs, and generate reports without manual intervention. The system serves itself by automatically provisioning resources based on performance data and cost analysis, thereby increasing productivity and automation level while the modular architecture manages the inherent complexity.
3Reliability
If resource over-provisioning is implemented, then Quality of Service requirements are guaranteed, but energy consumption and infrastructure costs increase
Solution Approach 1:
The patent implements dynamic resource provisioning where the system continuously monitors performance metrics and automatically adjusts resource allocation based on actual demand. The orchestration module dynamically scales resources up or down to match workload requirements, ensuring Quality of Service is maintained during peak loads while reducing resource consumption during low-demand periods, thereby eliminating the need for static over-provisioning and reducing energy costs.
4Measurement precision
If comprehensive performance metrics collection is implemented across all server components, then measurement precision is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the performance metrics collection system into modular components, each responsible for collecting metrics from specific server elements (app servers, database servers, storage, network). The performance metrics collection module divides the complex task of comprehensive monitoring into manageable segments, collecting data from individual components and aggregating them centrally. This segmented approach improves measurement precision while managing system complexity through modular, distributed data collection.
Data Source
AI summary
Systems and methods for an elastic cost-to-serve system including a first module to orchestrate an elastic server set; a second module to orchestrate a load test and to apply one or more use-case scenarios for each orchestrated server set; a third module to generate a cost metrics model of the orchestrated server set for predictive cost modeling; a fourth module coupled to the third module to collect a plurality of performance metrics across the server resources and associated client devices; a fifth module to post-process the collected performance metrics across a load testing duration and to provide analytics of the server set performance; and a sixth module coupled to analyze the performance metrics adapting available resources and to apply a heuristic of the cost metrics model to predict a model of cost optimization of the server set.