The rapid growth of mobile data and computing demands has strained resource-constrained edge devices, particularly in supporting IoT applications. Mobile Edge Computing (MEC) offloading alleviates these challenges by shifting complex tasks to edge-cloud servers, reducing computational burdens and enhancing efficiency. The integration of 5G and 6G technologies further enhances MEC by providing ultra-low latency and high-bandwidth connections. Despite the use of deep learning methods in task offloading, current approaches struggle with slow learning and adaptability issues. To address these challenges, we introduce a Deep Meta Reinforcement Learning based Offloading (Deep Meta-RL) Framework. Formulating the task offloading problem as a Markov Decision Process (MDP) enables us to leverage the Deep Meta-RL algorithm for precise offloading decisions, reinforcement learning’s decision-making, and meta-learning’s adaptability. Simulation results demonstrate that Deep Meta-RL significantly outperforms traditional DQN algorithms, achieving a 16.75% improvement in rewards and reducing latency by 20%.

This research work introduces a new strategy to enhance computation offloading in wireless-powered Edge-Enabled Mobile Computing (EEMC) networks by utilizing Dueling Double Deep Q-Networks with Attention (Dueling DDQN-A). EEMC facilitates the offloading of computational tasks from mobile devices to local edge servers, leading to decreased latency and improved energy efficiency. However, the dynamic and stochastic nature of wireless environments presents significant challenges for real-time task offloading decisions. To address this, we enhance the Dueling DDQN framework by integrating an attention mechanism to prioritize the most relevant features in the state space. The proposed Dueling DDQN-A model enhances decision-making by separating state value estimation from action advantage estimation. Additionally, the model uses attention to prioritize key state features, leading to improved adaptability in dynamic wireless channel and network environments. We frame the offloading decision problem as a Markov Decision Process (MDP) and apply deep reinforcement learning to optimize both the computation rate and energy efficiency. We compare and evaluate the model through comprehensive simulations using the latest techniques like Simple DQN, Double DQN, and Dueling DDQN. Our results demonstrate that Dueling DDQN-A achieves a 59.12% improvement in average computation rate and a 16.19% reduction in energy consumption over the baseline models. Additionally, it significantly outperforms the baselines in terms of training loss and convergence speed. These findings suggest that Dueling DDQN-A offers a robust and highly efficient solution for real-time computation offloading in wireless-powered EEMC networks, making it a strong candidate for deployment in real-world scenarios.

UAVs have the potential to enhance wireless systems by improving range and quality, and this can be achieved by utilising the Mobile Edge Computing provided by the unmanned aerial vehicle. In this system, the MEC server mounted on the unmanned aerial vehicle can offer offload services to all the User Equipment in a given space. By offloading some proportion of its tasks to unmanned aerial vehicle for computation, the UE can perform the remaining tasks locally. The objective of this study is to minimize the maximum processing delay in the whole process by optimizing on four parameters, scheduling of the user equipment, portion of the task that has to be offloaded, angle of flight for the UAV, and speed of flight of the UAV, taking into account discrete variables and power constraints. However, due to the nonconvexity, the state space’s high dimension, and action’s space continuous nature of this problem, we are proposing a Proximal Policy optimization algorithm which is based on boosting the policy gradient. Further we will do a comparative analysis of PPO algorithm with other popular Reinforcement Learning algorithms, particularly the Deep Deterministic Policy Gradient algorithm. PPO algorithm can quickly achieve the optimal policy for offloading computation in a dynamic environment. The results obtained implies that the both PPO and DDPG algorithm converges quickly, and the processing delay is minimizd. However, our proposed PPO algorithm has shown significant improvement in minimizing the processing delay. Our model also performs way better compared to basic algorithm such as Deep Q Network (DQN).

Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims to address the challenge of reducing carbon emissions in cloud computing by proposing a framework for workload shifting based on low carbon intensity periods in the power grid. The study focuses on four countries and their carbon production in the year 2022, calculating the carbon intensity for each country. Additionally, the paper identifies different cloud computing workloads and integrates constraints such as power, SLA, carbon emissions, and routing into the framework. The crucial factors considered during workload shifting include geo-distributed load balancing and right-sizing the data center.A simulation is developed to evaluate the proposed framework, simulating scenarios with shiftable workloads. The results are compared and analyzed, assessing the framework’s effectiveness in reducing carbon emissions while meeting the specified constraints. The findings highlight the potential benefits of workload shifting in reducing carbon emissions and improving environmental sustainability. Overall, this research contributes to the advancement of green computing and offers insights into the development of sustainable cloud computing practices.