TL;DR
Results demonstrate that the dynamic deployment technology of user preference-aware cache resource pre-selection method can effectively reduce the cache energy consumption, reduce the delivery delay and improve the cache hit rate.
The proposed MADDPG algorithm outperforms benchmark algorithms such as CO (Cloud-Only), GO (Greedy Offloading), SEP (Static Expert Partitioning), DDPG (Deep Deterministic Policy Gradient), COMA (Counterfactual Multi-Agent Policy Gradients), and MAPPO (Multi-Agent Proximal Policy Optimization) in terms of average iterat...
Experimental results demonstrate that the heuristic approach achieves better cost efficiency and improved resource utilization compared to the greedy method, highlighting that simple, rule-based allocation strategies can significantly enhance performance in cloud environments while maintaining low computational complex...
To address the decentralized and dynamic characteristics of adjustable resources on the power user side, as well as the high sensing delay and low collaborative efficiency of traditional centralized architectures, this paper proposes a dynamic sensing and collaborative optimization algorithm combining edge computing wi...
The increasing adoption of cloud computing (CC) produces a notable growth in data centres, resulting in severe financial, environmental, and operational problems. To mitigate these issues, energy-efficient resource allocation strategies have been developed as a key solution for enhancing energy utilization while ensuri...
This can be attributed to the fact that the fast growth of cloud computing services has caused a sharp rise in energy consumption in large-scale data centers increasing both the cost of operations and the environmental issue. Cloud infrastructure that is sustainable requires resource management techniques that reduce t...
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