Dynamic Service Redeployment for Network Response Time
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Solution Overview
Problem
Current web service deployment methods are inefficient as they require static deployment of presentation logic at the network edge, leading to increased administration complexity and suboptimal response times, especially when usage patterns change or require software updates, and do not effectively distribute dynamic content closer to users.
Innovation Solution
A method for dynamically redeploying network-accessible services by receiving a redeployment trigger, determining new network locations, programmatically removing and replacing services, and using usage metrics to deploy services based on demand, allowing for automated updates and undeployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If services are statically deployed at the network edge, then response time is improved, but administration complexity increases and adaptability to usage pattern changes deteriorates
Solution Approach 1:
The patent implements dynamic service deployment by allowing services to be automatically redeployed from the origin server to edge servers based on runtime usage metrics. This transforms the static deployment model into a dynamic one where service locations can change automatically without manual administrative intervention, thus improving response time while avoiding administration complexity.
Solution Approach 2:
The system enables self-service through automated monitoring of usage metrics and automatic triggering of redeployment operations. The origin server and edge servers work autonomously to detect when services need redeployment and execute the redeployment process without requiring manual administrative actions, resolving the contradiction between fast response and complex administration.
2Speed
If services are statically deployed at the network edge, then response time is improved, but adaptability to usage pattern changes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring usage metrics at edge servers and using this information to trigger service redeployment decisions. When usage patterns change, the system receives feedback about the new patterns and automatically adapts by redeploying services to appropriate locations, thus maintaining both fast response time and adaptability to changing conditions.
Solution Approach 2:
The system transitions from static to dynamic deployment by enabling services to be automatically relocated based on runtime conditions. This dynamic approach allows the network to adapt to changing usage patterns while maintaining the performance benefits of edge deployment, resolving the contradiction between response time and adaptability.
3Adaptability or versatility
If services are dynamically redeployed based on usage metrics, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent reduces system complexity by implementing self-service automation where the system monitors its own usage metrics and automatically triggers redeployment operations without requiring complex external management systems. This self-managing approach improves adaptability while keeping the system relatively simple by eliminating the need for manual intervention and complex orchestration infrastructure.
4Device complexity
If manual deployment methods are used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent achieves automated self-service deployment where the system monitors usage metrics and automatically triggers service redeployment operations. This eliminates manual deployment steps while keeping the underlying system relatively simple, thus improving deployment productivity without significantly increasing device complexity.
Solution Approach 2:
The system performs preliminary actions by pre-configuring the deployment infrastructure and establishing automated monitoring of usage metrics. This preliminary setup enables rapid automatic deployment responses without requiring complex real-time decision-making systems, improving productivity while maintaining manageable system complexity.
Data Source
AI summary
Methods, systems, and computer program products for improving network operations by dynamically redeploying services (such as web services or other network-accessible services) in a computing network. A programmatic replication or redeployment process is defined, whereby system upgrades may be implemented by redeploying services dynamically, without human intervention, enabling the complexity of upgrading previously-deployed software to be reduced significantly.