Cloud Service Provisioning via Automated Broker and Node Coordination
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Solution Overview
Problem
In traditional platform-as-a-service (PaaS) environments, infrastructure provisioning and scaling are typically static and require human intervention, making it difficult to dynamically adjust to changing demands and maintain high performance and availability.
Innovation Solution
A cloud computing system with an automatic service provisioning manager and service broker that uses infrastructure configuration management tools like Chef to dynamically provision and scale resources, perform failover, and manage service instances with minimal human intervention, enabling dynamic horizontal scaling and concurrent request handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators manage and control the underlying cloud infrastructure, then the provisioning and configuration can be precisely controlled, but the system becomes static and requires human intervention to scale infrastructure and services to meet changing demand
Solution Approach 1:
The system enables self-service through automated service brokers and provisioner nodes that independently handle infrastructure provisioning, service instantiation, and scaling operations without human intervention. The service broker automatically receives service requests, determines resource requirements, and coordinates with provisioner nodes to provision and configure services dynamically based on changing demand
Solution Approach 2:
The system transitions from static human-managed provisioning to dynamic automated provisioning. The service broker and provisioner nodes continuously monitor demand and automatically adjust infrastructure resources in real-time, enabling the system to adapt dynamically to changing service requirements and load conditions
2Manufacturing precision
If human intervention is used for infrastructure provisioning, then configuration accuracy is maintained, but provisioning speed and responsiveness to demand changes are reduced
Solution Approach 1:
The system replaces manual human operations with automated software-based service brokers and provisioner nodes. These automated components use standardized APIs and configuration management tools to provision and configure services with both high speed and precision, eliminating the bottleneck of human intervention while maintaining configuration accuracy through automated validation and templating
3Device complexity
If static infrastructure provisioning is used, then system simplicity is maintained, but the ability to meet demand fluctuations and maintain high performance is compromised
Solution Approach 1:
The system segments the provisioning function into separate, independent components: service brokers that handle service requests and logic, provisioner nodes that execute provisioning tasks, and infrastructure resources that provide computing capacity. This modular architecture maintains simplicity through clear separation of concerns while enabling dynamic scaling and high availability through independent operation of each segment
Data Source
AI summary
Systems and methods for automatically provisioning a service instance in a cloud computing system are presented. In an example embodiment, a message is retrieved by one of multiple provisioner nodes from a message queue shared by the provisioner nodes, the message including a service request to provision a service instance on the computing infrastructure. The provisioner node that retrieved the message may determine whether a resource of the computing infrastructure capable of hosting the service instance is available, and if not, may cause a creating on the computing infrastructure of a resource capable of hosting the service instance. The provisioner node may also cause deployment and configuration of software for providing the service instance on the resource.