Microservices Provider Selection via Blockchain QoS Monitoring
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Solution Overview
Problem
Current cloud-management applications struggle to predict and compare the quality of service (QoS) of microservices applications that comprise cloud-based microservices, especially when these services are hosted by multiple cloud providers, leading to unpredictable costs and service levels.
Innovation Solution
The implementation of a microservices-management system that uses blockchain technology to securely store and monitor QoS data from multiple cloud providers, combined with an artificially intelligent, self-learning cognitive selector module to infer and select the optimal provider based on historical and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cloud service providers are used to deliver microservices, then service availability and scalability are improved, but QoS predictability and selection optimization become more difficult
Solution Approach 1:
The patent introduces a microservices-management system as an intermediary between application owners and cloud service providers. This system includes a cognitive selector module that manages provider selection and a blockchain network that stores and verifies QoS data, thereby simplifying the complexity of selecting among multiple providers while maintaining service availability.
Solution Approach 2:
The system implements feedback mechanisms where QoS data from actual service deliveries is stored on the blockchain and used to train the cognitive selector module. This feedback loop enables the system to learn from past performance and continuously improve provider selection, making multi-provider management more predictable and less complex.
2Ease of operation
If QoS data is stored in a centralized database, then access and analysis are simplified, but data security and trustworthiness decrease when providers are untrusted
Solution Approach 1:
The blockchain network acts as a trusted intermediary that stores QoS data in a decentralized manner. While blockchain provides enhanced security and trustworthiness compared to centralized databases, the patent maintains ease of operation by implementing a cognitive selector module that automatically queries and analyzes blockchain-stored data, abstracting the complexity of decentralized data access from users.
Solution Approach 2:
The cognitive selector module autonomously queries the blockchain for QoS data, analyzes the information, and makes provider selections without requiring manual intervention. This self-service capability maintains operational simplicity despite the decentralized storage architecture, as the system automatically handles data retrieval and analysis.
3Device complexity
If manual provider selection is used, then system complexity is reduced, but QoS optimization and cost efficiency deteriorate
Solution Approach 1:
The cognitive selector module operates autonomously to query blockchain-stored QoS data, analyze provider performance, and make selection decisions. This self-service capability enables automated QoS optimization without requiring complex manual configuration or intervention, thereby improving optimization efficiency while keeping the system relatively simple to deploy.
Solution Approach 2:
The system uses feedback from actual service deliveries (stored on blockchain) to continuously train and improve the cognitive selector module. This automated feedback loop enables the system to progressively optimize QoS and cost efficiency without increasing operational complexity, as the optimization occurs automatically through machine learning.
4Reliability
If blockchain technology is used to store QoS data, then data security and immutability are improved, but system complexity and data access overhead increase
Solution Approach 1:
The microservices-management system acts as an intermediary that handles all interactions with the blockchain network. It manages data submission, querying, and analysis, thereby shielding application owners from blockchain complexity while maintaining data immutability and security benefits.
Solution Approach 2:
The cognitive selector module autonomously queries and analyzes blockchain-stored QoS data without requiring manual intervention. This automation compensates for the increased complexity of blockchain data access by making the process transparent and effortless for users, effectively hiding the overhead from end-users.
Data Source
AI summary
A microservices-management system intercepts a request for a cloud-based microservice sent by a microservices-architecture application. The system selects an optimal cloud-service provider from a group of candidate providers capable of delivering the microservice and then forwards the request to the optimal provider. The optimal provider is selected by drawing cognitive inferences from stored blockchain records that each describe a characteristic of a previous delivery of the requested service. Each record is generated by one of the candidate providers when delivering an instance of the microservice, regardless of whether the provider is in a trusted relationship with the application owner. The providers are barred by blockchain's intrinsic security features from altering or deleting previously stored blockchain records. Upon delivery of the service, the system compares the actual quality or cost of the delivery with predicted values in order to learn how to more effectively select optimal providers.