Cloud Resource Routing via Business Value Graphs
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Solution Overview
Problem
Current Application Resource Management (ARM) and Application Performance Management (APM) systems in cloud-based environments do not effectively combine data for service call prioritization, leading to inefficient resource allocation and potential performance issues.
Innovation Solution
A method is introduced to calculate the business value of service calls by combining runtime and remote data, generating a business flow dependency graph, and using this information to dynamically route and provision resources for each service call, ensuring high-value calls are prioritized and executed on high-performant resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud resources are allocated without prioritization, then resource allocation is simple and fast, but service call performance deteriorates due to inefficient resource distribution
Solution Approach 1:
The system performs preliminary actions by calculating business value and generating dependency graphs before service calls are executed. Runtime data and remote data are collected in advance, business value is computed for each service call, and a business flow dependency graph is generated to predict execution paths. This preliminary analysis enables informed routing and provisioning decisions that optimize service call performance without adding complexity during actual execution.
Solution Approach 2:
The resource allocation system becomes dynamic by continuously adapting to changing conditions. The system dynamically routes service calls to optimal resources based on real-time business value calculations and predicted execution paths. Resource provisioning is adjusted on-demand based on the calculated business value, allowing the system to respond flexibly to varying service requirements and optimize performance without fixed rigid allocation rules.
2Measurement precision
If runtime and remote data are combined for business value calculation, then service call prioritization improves, but data processing complexity increases
Solution Approach 1:
The data processing system is segmented into distinct modules that handle different data types separately before integration. Runtime data is collected from executing services, remote data is gathered from external sources, and these segmented data streams are then processed independently before being combined to calculate business value. This segmentation reduces the complexity of handling complex data structures and enables more accurate prioritization through systematic data processing.
Solution Approach 2:
An intermediary processing layer is introduced between data collection and business value calculation. This intermediary layer aggregates and preprocesses both runtime and remote data, transforming raw data into structured business value metrics. The intermediary handles the complexity of data integration and transformation, enabling accurate business value calculation while isolating the complexity from the core prioritization logic.
3Productivity
If service calls are routed based on predicted paths, then resource optimization improves, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically generating business flow dependency graphs and predicting execution paths without manual intervention. The routing decision-making process is automated, with the system independently analyzing service call requirements, calculating business values, and selecting optimal resource paths. This self-service approach optimizes resource allocation efficiency while containing complexity within automated processes rather than requiring complex manual control systems.
Data Source
AI summary
A method, computer system, and a computer program product are provided for optimizing just in time routing of resources for performing a plurality of service calls in a distributed cloud-based computer system environment. Runtime data and remote data associated with resource needs associated with the plurality of service calls to be performed is obtained. A business value associated with each of the plurality of service calls is calculated. The business value has a calculated tangible and intangible element. A business flow dependency graph is generated based on the business value calculated. A service call prediction path for each of the plurality of service calls is generated based on said business flow dependency graph. Using the service call prediction path and the business value calculated, routing and provisioning of the plurality of resources for each of the plurality of service calls is provided.