Auto-scaling Web Content Management Service
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Solution Overview
Problem
Current distributed computing resource systems face challenges in dynamically adjusting hardware resources to meet performance thresholds for serving electronic content, leading to suboptimal response times and resource utilization.
Innovation Solution
A management service measures performance metrics such as response time, load time, and resource utilization to determine whether to auto-scale hardware resources up or down, transmitting requests to the distributed computing resource system to instantiate or modify resources accordingly, ensuring optimal performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware resources are statically allocated to computing system instances, then device complexity is reduced and ease of operation is improved, but performance thresholds cannot be dynamically met and response times become suboptimal
Solution Approach 1:
The system implements self-service through automatic auto-scaling mechanisms that monitor performance metrics and dynamically adjust hardware resources without manual intervention. The management service automatically detects when performance thresholds are not met and provisions or de-provisions resources accordingly, enabling the system to serve itself and eliminate the need for complex manual resource management while maintaining optimal response times
Solution Approach 2:
The patent applies dynamics by transitioning from static hardware resource allocation to dynamic auto-scaling. The system continuously monitors performance metrics such as CPU utilization, memory usage, and response time, and automatically adjusts the number and configuration of computing system instances based on real-time conditions. This dynamic adaptation allows the system to meet performance thresholds while managing complexity through automation
2Speed
If more hardware resources are provisioned to meet performance demands, then response time improves, but resource utilization becomes suboptimal and power consumption increases
Solution Approach 1:
The system implements periodic action through continuous monitoring of performance metrics at defined intervals and periodic auto-scaling adjustments. The management service regularly assesses whether performance thresholds are met and triggers resource provisioning or de-provisioning actions only when necessary. This periodic evaluation ensures that resources are consumed only when needed to maintain response time performance, avoiding continuous power consumption for idle resources
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting hardware resource parameters (CPU, memory, storage) based on monitored performance metrics. When response time degrades below thresholds, the system increases resource parameters by provisioning additional instances. When performance exceeds thresholds, the system decreases parameters by de-provisioning resources. This dynamic parameter adjustment optimizes the balance between response time and power consumption
3Adaptability or versatility
If manual resource management is used, then device complexity is reduced, but adaptability to changing performance requirements deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the management service continuously monitors performance metrics and uses this feedback to automatically adjust hardware resources. The feedback loop compares actual performance against defined thresholds and triggers auto-scaling actions when deviations occur. This feedback-driven approach enables high adaptability to changing performance requirements while managing complexity through automated decision-making algorithms
Solution Approach 2:
The patent introduces an intermediary management service that acts as a mediator between performance requirements and hardware resource provisioning. This intermediary layer monitors performance metrics, makes intelligent decisions about resource allocation, and automatically provisions or de-provisions computing system instances. The intermediary absorbs the complexity of resource management logic, allowing the system to adapt to performance demands without requiring complex manual management procedures
4Reliability
If hardware resources are scaled up continuously, then performance thresholds are consistently met, but costs increase and efficiency decreases
Solution Approach 1:
The system applies partial action by provisioning hardware resources only to the extent necessary to meet performance thresholds, rather than continuously maintaining maximum capacity. The auto-scaling mechanism scales resources up when performance degradation is detected and scales down when thresholds are comfortably met. This partial provisioning approach maintains reliability by ensuring sufficient resources are available when needed while improving productivity by eliminating excess resource consumption during low-demand periods
Data Source
AI summary
One exemplary embodiment involves transmitting a request to a distributed resource system to provide, from a server computer device associated with the distributed computing resource system, network content to a requesting device, the server computer device being located at a geographic location that corresponds to a location of the requesting device. The embodiment further involves measuring at least one of a plurality of performance metrics associated with providing the network content and determining whether to auto scale a plurality of resources associated with the server computer device based at least in part on the at least one of the performance metrics. Additionally, the embodiment involves transmitting a request to the distributed computing resource system to auto scale the resources, responsive to the determination to auto scale the resources associated with the server device.