DeepPerfNet: AI-Powered Predictive Workload Forecasting and Autonomous Resource Scaling

Abstract

DeepPerfNet is a sophisticated deep learning architecture that can adapt to these changes in workloads & automatically modify resources in cloud-native environments that are always changing. DeepPerfNet combines the time-based prediction power of Long Short-Term Memory (LSTM) networks with the flexible decision-making power of reinforcement learning. This allows themselves to constantly check on how well their infrastructure is working and anticipate when demand can alter. This preventive approach makes sure that the operating system uses its computing, memory, storage, and network resources correctly, which avoids both excessive provisioning and operational problems. The framework uses historical information, contemporaneous communication data, and mechanisms for feedback to quickly enhance its capacity expansion techniques. This makes me confident that it can handle an extensive array of these workloads with the greatest speed and lowest latency. DeepPerfNet is not comparable to other rule-based auto scaling systems since it changes based on how applications work, how many other people are using the framework, and how the physical structure is built up. It does this by adopting closed-loop machinery that finds a suitable balance between expense and dependability. DeepPerfNet makes services more steady and lowers costs related to cloud computing by removing all of these unnecessary resources. This has been successfully shown by numerous experiments and modeling in the real world. DeepPerfNet is an advancement forward toward managing the cloud on its own. It uses comprehensive reinforcement instruction and mathematical modeling to create a cloud environment that really takes into account itself and performs more effectively.