Efficient Resource Management in Edge Computing for Autonomous Systems with An Energy-aware Approach

With the rise of autonomous systems like self-driving cars and unmanned aerial vehicles (UAVs) becoming part of modern infrastructure, there is a huge use case for efficient resource management in edge computing environments. The ceremony contains approaches also leaves the requirement of low latency; high computation systems unaddressed. In this paper, we propose an energy aware resource management framework specifically designed for the autonomous system in edge computing environment. This helps to maximize resource utilization, allocating computational processing and energy consumption in a trade-off that will result on longer device operational life spans without degrading system performance. In this way, the proposed method combines real-time workload distribution algorithms with energy-aware scheduling techniques to allocate tasks at edge nodes dynamically. In this work, we construct a data analytical model to predict the resource demand of system set up and energy consumption using past historical data along with real time input information from auto scaling systems. And the predictive model is combined with machine learning-based optimization to improve decision making in task allocation. One of the key innovative approaches to this framework has been shifting computation loads dynamically between edge nodes based on energy availability, as well as system health in order to reduce node failures and downtime. Simulation results show that our energy-aware technique can increase the utilization of resources to 35% and decrease consumption by at least in comparison with traditional load balancing methods. Additionally, the framework provides substantial advancements in response time, a key factor for ensuring latency-sensitive autonomous systems maintain their operation. The model disclosed in our solution is scalable and can adapt for application on multiple autonomous platforms, that would help to make the future of Autonomous networks more reliable as well as sustainable. These results highlight the necessity of developing energy-aware solutions at the resource management level to guarantee higher operational efficiency for complex and wide-scale autonomous systems into edge computing environments.