Container Deployment Optimization for Latency Reduction
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Solution Overview
Problem
Distributed systems face inefficiencies in hosting software services across multiple machines due to varying latency between machines, which affects overall system performance and customer experience.
Innovation Solution
A computer-implemented method and system that configures container deployment across multiple machines by obtaining network and inter-container communication information, determining a cost function for expected communication time, and processing it with an optimization algorithm to identify a deployment configuration that meets performance requirements, thereby minimizing latency and optimizing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If containers are deployed across multiple machines to distribute software services, then system scalability and fault tolerance are improved, but latency between machines increases and overall system performance deteriorates
Solution Approach 1:
The patent applies local quality by making the deployment configuration adaptive to specific network conditions between machine pairs. The system determines communication characteristics for each pair of machines and creates customized deployment configurations that optimize placement based on local network quality, rather than using a uniform deployment strategy across all machines.
Solution Approach 2:
The system changes parameters by dynamically adjusting the deployment configuration based on measured communication characteristics. The cost function incorporates communication time parameters that are determined through monitoring, and the optimization algorithm adjusts container placement parameters to minimize total communication time while maintaining scalability.
2Reliability
If containers are deployed across multiple machines to distribute software services, then system fault tolerance is improved, but overall system performance deteriorates due to varying latency
Solution Approach 1:
The patent implements feedback by monitoring communication characteristics between machines over time and using this information to optimize deployment configurations. The system determines communication characteristics for a predetermined time period, processes this feedback through optimization algorithms, and adjusts container placement to improve performance while maintaining fault tolerance.
Solution Approach 2:
The deployment configuration is made dynamic rather than static. The system continuously monitors communication characteristics and adjusts container placement based on changing network conditions, allowing the system to adapt to varying latency patterns while maintaining both fault tolerance and performance.
3Ease of manufacture
If traditional deployment methods are used without optimization, then deployment simplicity is maintained, but system performance and customer experience deteriorate
Solution Approach 1:
The system performs self-service by automatically determining communication characteristics, calculating optimal deployment configurations, and adjusting container placement without manual intervention. The automated optimization process maintains deployment simplicity from the user perspective while significantly improving system performance through data-driven decisions.
Solution Approach 2:
The system performs preliminary action by determining communication characteristics and optimizing deployment configurations before actual container deployment. This advance optimization ensures that containers are placed on machines with optimal communication characteristics, improving performance before the system even goes live.
Data Source
AI summary
A computer-implemented method for configuring deployment of a distributed system across a plurality of machines of a network. The method may include obtaining network information describing network communication characteristics between a plurality of machines of a network. The method may also include obtaining inter-container communication information describing at least one characteristic of communication between pairs of machines of the plurality of machines for a predetermined time period. The method may also include determining a cost function, the cost function mapping a potential container deployment configuration to an expected communication time, based on the network information and the inter-container communication. The method may also include processing the cost function with an optimization algorithm. The method may also include, in response to the processing, identifying a container deployment configuration having an associated cost that meets a cost requirement.