AI-Based Autoscaler for Distributed Processing Load Balancing
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Solution Overview
Problem
In distributed cloud environments, existing load-balancing techniques often lead to over-allocation of processing resources, causing competition among applications and services for resources, which can result in inconsistent functionality and reduced performance.
Innovation Solution
The implementation of a dynamic autoscaler that uses historical resource allocation data and machine-learning models to determine when to increase processing resources by instantiating additional partitions, based on real-time load values and threshold processing loads, while adjusting the autoscaler configuration to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If processing resources are increased to handle larger application size and processing load, then application usability is maintained, but resource over-allocation occurs causing competition among applications and services
Solution Approach 1:
The patent implements dynamic resource allocation through an autoscaler that automatically adjusts the number of partitions based on real-time load monitoring. The system transitions from static resource allocation to dynamic scaling, where resources are adjusted up or down based on actual demand, preventing both over-allocation and under-allocation of processing resources.
Solution Approach 2:
The system employs feedback mechanisms where the autoscaler continuously monitors load metrics and uses this information to make informed decisions about resource allocation. The load monitoring and autoscaling decisions form a closed-loop control system that responds to actual system conditions, ensuring resources are allocated efficiently without causing competition among services.
2Adaptability or versatility
If additional processing resources are allocated to maintain consistent functionality as application size increases, then application complexity can be increased, but other applications and services may not receive needed processing resources
Solution Approach 1:
The patent enables dynamic resource allocation where the autoscaler adjusts partition counts based on real-time load conditions. This allows the system to adapt resource allocation to match actual application needs, supporting increased application complexity without permanently consuming excessive resources that would be unavailable to other services.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed to variable based on load conditions. The autoscaler modifies the number of partitions (a key system parameter) dynamically, allowing the distributed system to support complex applications when needed while freeing resources for other services when load decreases.
3Productivity
If applications over-allocate processing resources in distributed environments, then application performance is ensured, but resource competition occurs reducing overall system efficiency
Solution Approach 1:
The autoscaler implements feedback-driven resource management by continuously monitoring load metrics and adjusting partition allocation accordingly. This prevents over-allocation while ensuring sufficient resources are available to maintain application performance, thereby improving overall system efficiency and reducing wasted computational capacity.
Solution Approach 2:
The system enables self-service resource management where the autoscaler automatically makes scaling decisions based on monitored conditions without manual intervention. This self-adjusting mechanism ensures each application receives appropriate resources to maintain performance while preventing excessive allocation that would reduce system-wide efficiency.
Data Source
AI summary
Systems and method are provided for load balancing in distributed networks. A computing device uses historical resource allocation data associated with a service deployed within a distributed network to train a machine-learning model configured to generate a threshold processing load usable to determine when processing resources allocated to a service within a particular distributed network are to be increased. An autoscaler of the computing device may instantiate a first quantity of partitions within a processing node of the particular distributed network. The computing device may execute the machine-learning model using a load value associated with the first quantity of partitions to generate a threshold processing load. Upon receiving an updated load value that is greater than the threshold process load, the autoscaler may instantiate a second quantity of partitions and, in response, modify the autoscaler based on the updated load value.