Cloud Service Controller for Dynamic Resource Scaling
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Solution Overview
Problem
Current Cloud-based service deployment in telecommunications networks is complex, time-consuming, and costly due to inefficient resource allocation and scaling, often resulting in overprovisioning and manual intervention, which is error-prone and not dynamic enough to handle varying workloads effectively.
Innovation Solution
A controller that automatically models and adjusts Cloud resource usage based on specific and generalized workload models, selecting the most accurate model for operation and adapting resources to meet anticipated demands, thereby simplifying deployment and ensuring quality of service expectations are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overprovisioning is used to ensure service availability, then service reliability is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation through automated scaling that adjusts cloud resources in real-time based on actual service demand. The system monitors service metrics and automatically provisions or de-provisions resources, transitioning from static overprovisioning to dynamic adaptation. This resolves the contradiction by maintaining reliability through automated failover capabilities while improving resource efficiency by releasing unused resources.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring service metrics, performance thresholds, and resource utilization. The automated scaling controller receives feedback about service state and adjusts resource allocation accordingly. This feedback loop enables the system to maintain reliability while optimizing resource usage, avoiding both overprovisioning and underprovisioning.
2Ease of operation
If manual configuration and deployment processes are used, then service deployment control is improved, but deployment time and complexity deteriorate
Solution Approach 1:
The patent implements self-service automation where the system automatically performs configuration, integration, and deployment tasks without manual intervention. The automated scaling controller and service model orchestrator handle resource provisioning, threshold configuration, and service deployment autonomously based on predefined policies and service models. This maintains operational control through automated decision-making while dramatically reducing deployment time and complexity.
Solution Approach 2:
The system performs preliminary actions by pre-defining service models, deployment templates, and scaling policies before actual service deployment. These pre-configured models contain all necessary configuration parameters, resource requirements, and deployment instructions. When deployment is needed, the system simply instantiates these pre-prepared models, avoiding time-consuming manual configuration while maintaining control through the structured model definitions.
3Loss of energy
If reactive scaling techniques are used, then resource utilization is improved, but service responsiveness deteriorates
Solution Approach 1:
The patent implements proactive scaling by predicting future service demand based on historical data, service models, and current trends. The system scales resources in advance of actual demand spikes rather than waiting for thresholds to be exceeded. This preliminary action maintains high resource utilization by scaling down during low periods while preserving service responsiveness by having resources ready before demand increases.
Solution Approach 2:
The system uses feedback from service metrics, performance monitoring, and demand prediction models to make scaling decisions. The automated controller continuously receives feedback about service state and predicted future states, enabling it to proactively adjust resources before performance degradation occurs. This feedback-driven approach balances resource utilization with service responsiveness.
4Extent of automation
If cloud service orchestration is implemented, then service automation is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements a universal service model framework that can orchestrate multiple cloud services and resources through standardized models. The service model orchestrator uses generic model templates that can represent different service types, resource configurations, and deployment scenarios. This universality enables high-level automation while reducing complexity by providing a single orchestration interface rather than service-specific control mechanisms.
Solution Approach 2:
The system introduces service models as intermediary abstractions between the automated controller and underlying cloud infrastructure. These models serve as mediators that simplify complex orchestration tasks by providing standardized representations of services, resources, and relationships. The automated controller interacts with services through these model intermediaries, reducing system complexity while enabling comprehensive automation.
Data Source
AI summary
A controller is provided for a Cloud based service handling multiple types of traffic in a telecommunications network, the controller comprising: a first stage configured to automatically model use of Cloud resources allocated to the service for specific workloads of each of a plurality of traffic types, so as provide a variety of models for each traffic type; a second stage configured to automatically reduce the number of and generalise the models so as to provide generalised models for each traffic type applicable to other workloads than the specific loadings; a third stage configured to automatically evaluate accuracy of the generalised models for various combinations of given workloads, in each combination each of the given workloads being of a corresponding traffic type, and to select one of the generalised models dependent upon evaluated accuracy; and a fourth stage configured to control operation of the Cloud resources according to the selected model.