Cloud Cost Attribution via Distributed CTS Agents
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Solution Overview
Problem
Current systems fail to provide an adequate solution for measuring the runtime cost-to-serve of applications distributed across multiple platforms, services, and customers in a public cloud environment, lacking automated cloud-agnostic methods to collect service usage statistics and attribute costs effectively.
Innovation Solution
A Cost-To-Serve (CTS) service framework is implemented, which includes CTS agents executed on public cloud hardware to monitor and collect service usage statistics across multiple customers and applications, providing real-time cost attribution and analytics through a multi-tenant architecture, enabling automated resource provisioning and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated cost-to-serve agents are deployed to collect service usage statistics across multiple platforms and customers, then measurement precision and automation extent improve, but device complexity and system resource requirements increase
Solution Approach 1:
The system segments the cost-to-serve measurement function into distributed agents deployed across multiple cloud platforms (AWS, Azure, GCP). Each agent independently collects and processes usage statistics for its specific platform, eliminating the need for a single complex centralized system while achieving comprehensive multi-platform measurement precision.
Solution Approach 2:
The patent introduces an intermediary cost-to-serve agent that bridges the gap between cloud infrastructure and billing systems. This agent acts as a mediator that collects usage data, calculates costs, and provides attribution information, simplifying the overall system architecture while enabling precise cost measurement across diverse platforms.
2Adaptability or versatility
If cloud-agnostic automated tools are implemented for cost attribution across multiple customers and applications, then adaptability and productivity improve, but device complexity increases
Solution Approach 1:
The cost-to-serve agent is designed with universal functionality to operate across multiple cloud platforms (AWS, Azure, GCP) using platform-specific SDKs. This multi-functional design enables the same agent architecture to adapt to different cloud environments, achieving cloud-agnostic cost attribution without requiring separate specialized systems for each platform.
Solution Approach 2:
The system achieves cloud platform adaptability by dynamically changing configuration parameters such as SDK selections, endpoint URLs, and pricing models based on the target cloud platform. This parameter-based adaptation allows the agent to function universally across different clouds without altering the core agent architecture, maintaining simplicity while achieving versatility.
3Loss of information
If real-time cost metrics and usage statistics are collected and analyzed, then information availability and productivity improve, but use of energy and computational resources increase
Solution Approach 1:
The agent implements partial monitoring by selectively collecting cost-to-serve metrics for specific services, customers, and usage patterns based on configuration priorities. This selective data collection approach provides sufficient cost information for decision-making while avoiding the computational overhead of capturing and processing every possible metric, thereby reducing energy consumption while maintaining information availability.
Solution Approach 2:
The system maintains continuous cost tracking and metric collection operations to ensure real-time information availability for cost attribution. By implementing persistent, background operations that continuously gather and update cost data, the system ensures information is always available without requiring intensive batch processing or periodic heavy computational bursts.
Data Source
AI summary
Systems and methods for a cost-to-serve (CTS) service to measure total cost-to-serve and cost attributions by a plurality of CTS agents spawned across dynamic resources to capture information from a set of instances associated with the plurality of resources; a CTS agent transaction module to publish a set of metrics established by the CTS agent for each instance and usage type; a CTS service collector module to aggregate from each CTS agent, one or more instances from the set of instances to generate transaction metrics; a CTS measurement service module for measuring a total cost for each selected transaction stored at the CTS store based on at least a cost per unit; a CTS metrics processor module for aggregating metrics related to the transactions to determine total cost and set of cost attributions for a selected cloud; and a CTS metrics analytic module to provide cost attribution analytics in the selected cloud in an analytics display.