Content Moderation with Highly Available Azure Load Balancing.

Abstract

Abstract:
Virtualisation technology is used by cloud service providers to separate real infrastructure. This lets them create resources that can be used by anyone and can be scaled up or down as needed. Because of this, public cloud services like Microsoft Azure are necessary to meet changing customer needs. As part of Azure's cloud load balancing, workloads and computing resources are spread across various servers to ensure the best speed and availability. However, allocating resources is still very hard because user needs change and are hard to predict. This is on top of the fact that costs need to be kept in check and resources need to be used well. In this study, we suggest an Efficient Scheduling Mechanism that will help spread out Azure resources more efficiently while improving Quality of Service (QoS). The suggested model uses resource weight-based scheduling to make sure that resources are used efficiently to finish tasks and spread out work, which improves cloud speed as a whole. The way resources are used is improved so that real-time weather data streams with information can be processed more efficiently. This balances the needs for predictions while reducing the stress on the cloud system. The model provides a prediction-based view of how resources can be partitioned that is more advantageous for managing energy and time to schedule functions in the distributed cloud environment. Beyond that, it goes further to even look at how to diminish power as a speed factor in any cloud system based on Azure. To ensure that the suggested method is effective, the aspects of performance that include execution time, resource usage, energy consumption are considered. The results reveal that the proposed approach is effective in managing existing and available resources during VM migration and reduces the time taken to execute scripts and power consumption with minimal disruption of the system.