Predictive Scaling for 5G Service Chain Latency
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Solution Overview
Problem
Current auto-scaling mechanisms in 5G service chains, particularly those using Kubernetes, are reactive and can cause latency issues or dropped requests due to inadequate resource allocation for latency-sensitive workloads.
Innovation Solution
A method that pre-emptively scales resources by estimating traffic distribution across service chains using scaling factors computed from real-time traffic measurements and direct path coefficients, deploying additional resources before increased demand is met, thereby preventing processing delays and dropped requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive auto-scaling mechanisms are used in Kubernetes, then resource allocation responds to actual demand, but latency-sensitive workloads experience processing delays and dropped requests
Solution Approach 1:
The patent applies preliminary action by proactively scaling resources before traffic bursts occur. The system monitors traffic patterns and predicts future demand, then pre-allocates computing resources in advance. This prevents latency and request drops by ensuring capacity is already available when traffic increases, rather than waiting for reactive triggers.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring traffic metrics, resource utilization, and service chain performance. This feedback loop enables the predictive scaling algorithm to adjust resource allocation dynamically, learning from past traffic patterns and optimizing resource distribution across the service chain to maintain low latency while preventing overload.
2Reliability
If resources are pre-allocated to all services in the service chain, then latency-sensitive workloads are protected from processing delays, but resource waste increases when traffic is low
Solution Approach 1:
The patent applies dynamics by implementing flexible, adaptive resource allocation that continuously adjusts to actual traffic conditions. Instead of static pre-allocation, the system dynamically scales resources based on real-time traffic patterns and predictive analytics. This ensures reliability during traffic bursts while minimizing resource waste during low-demand periods through automated scaling decisions.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on traffic conditions and predictive models. It adjusts computing resources, memory allocation, and service chain capacity according to actual demand patterns, transforming the rigid parameter allocation into adaptive parameters that optimize both reliability and resource efficiency across varying traffic conditions.
3Measurement precision
If scaling factors are computed for all services in the service chain, then traffic distribution is accurately predicted, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the service chain into independent units with individual scaling factors. Each service is analyzed separately based on its specific traffic patterns and resource requirements. This modular approach maintains high prediction accuracy for each service while simplifying overall system management, as each service's scaling can be computed and adjusted independently rather than as a monolithic system.
Data Source
AI summary
Some embodiments provide a method that identifies a first number of requests received at a first application. Based on the first number of requests received at the first application, the method determines that a second application that processes requests after processing by the first application requires additional resources to handle a second number of requests that will be received at the second application. The method increases the amount of resources available to the second application prior to the second application receiving the second number of requests.