Predictive Load Balancing for Lower-Carbon Application Scaling
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Solution Overview
Problem
Conventional systems allocate memory statically for software applications, leading to waste and increased carbon footprints due to unused memory, while dynamic allocation increases computation time and energy consumption.
Innovation Solution
Implement a predictive load balancing and adaptive process scaling method using a delegation model trained on historical event processing information to dynamically allocate resources based on predicted loads, utilizing artificial intelligence to identify thread requirements and optimize memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static memory allocation is used for software applications, then memory is always available for applications, but memory waste occurs and carbon footprint increases due to unused memory
Solution Approach 1:
The patent implements dynamic memory allocation that adjusts memory resources based on actual application needs and predicted workload. The system continuously monitors application performance and reallocates memory dynamically, transitioning from static allocation to adaptive dynamic allocation that responds to changing demands, thereby reducing wasted memory while maintaining availability when needed.
Solution Approach 2:
The system changes memory allocation parameters based on workload conditions. It uses predictive analytics to forecast memory requirements and adjusts allocation parameters dynamically, changing the state of memory resources from fixed to variable based on predicted and actual demand, thus optimizing the balance between availability and energy consumption.
2Loss of energy
If dynamic memory allocation is used to reduce memory waste, then memory efficiency improves, but computation time increases and energy consumption rises
Solution Approach 1:
The patent employs predictive analytics that analyze historical workload patterns and forecast future memory requirements in advance. By performing preliminary analysis of workload trends, the system pre-determines optimal memory allocation before actual demand occurs, avoiding the need for time-consuming real-time allocation decisions while still achieving dynamic optimization.
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors actual memory usage and performance metrics, then uses this feedback to refine predictive models and adjust allocation strategies. This closed-loop approach optimizes memory allocation over time, reducing both waste and computation time by learning from past performance patterns.
3Productivity
If reactive load balancing is used to distribute resources, then resource distribution occurs, but waste is not prevented from initially occurring
Solution Approach 1:
The patent shifts from reactive to predictive load balancing by analyzing historical workload data and forecasting future resource requirements. The system proactively allocates resources based on predicted demand before tasks are actually executed, preventing waste from occurring in the first place rather than redistributing wasted resources afterward.
Solution Approach 2:
The system enables applications to self-report their memory requirements and performance status, allowing the load balancer to make informed predictive decisions. Applications essentially serve their own allocation needs by providing accurate demand signals, enabling more precise and efficient resource distribution without excessive provisioning.
Data Source
AI summary
Aspects related to reducing application carbon footprints using predictive load balancing and adaptive process scaling are provided. An adaptive scaling platform may train a delegation model to output event pools based on input of event processing traffic information. The platform may receive event processing traffic information. The platform may generate an event pool using the delegation model. The event pool may comprise tasks required to fulfill event processing requests corresponding to the event processing traffic information and indicators of applications corresponding to the plurality of tasks. The platform may receive event processing information from a server. The platform may cause, based on the event processing information, an application to identify thread requirements. The computing platform may allocate resources based on the thread requirements. The computing platform may delegate tasks to the threads based on the allocating. The platform may update the delegation model based on monitoring the threads.