Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil Optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.

5G and beyond will occupy a big position in wireless communication future, because of the high end-user data rate over the Internet demand. Millimeter wave (mmWave) plays the major role as a 5G networks enabler. To overcome mmWave band propagation losses and increase its coverage area, beamforming training (BT) is typically used in conjunction with mmWave transmissions. IEEE 802.11ad standard defines an exhaustive search BT to find out the best transmit (TX)/receive (RX) beam identifications (IDs). Narrow, i.e., pencil, beams guarantee a high gain, i.e., a wide coverage area, but at the expense of very high setup time due to the exhaustive searching process. Localization provides a promising solution for finding out the best mmWave beams within a small setup time. However, localization techniques having high localization errors compared with the coverage area of the sharp beams (e.g., 5 degrees) are not suitable for these scenarios. Motivated by the high publicity of light emitting diode (LED) lambs and their high accurate positioning (e.g., around 10 cm), a mmWave BT technique based on light fidelity (Li-Fi) multilateral positioning is proposed in this paper to work efficiently in mmWave sharp beam scenarios. Via mathematical and simulation analysis, the proposed mmWave BT achieves a few number of beam switchings with a comparable performance with the exhaustive search BT. Besides, it achieves a lower outage probability than that comes from using Wi-Fi Localization.

In this paper, we design a positioning information (PI) based codebook (PI-CB) for reconfigurable intelligent surface (RIS) passive beamforming (PBF), in online stage. In which, corresponding to each reflection pattern (RP), the current user equipment (UEs) PI, which are widely and easily available in communication networks, are stored. Moreover, for further overhead reduction, and based on the stored PI, we propose a partial PI-CB scheme for PBF of the RIS, where a group of candidate RPs, that are highly probable to serve the UEs, is determined depending on a distance metric. Consequently, the system can select the best RP by training only this group of RPs instead of searching the entire PI-CB. The proposed full and partial PI-CB schemes highly reduce the overhead and complexity, especially in large RIS. Also, they outperform channel estimation and alternating optimization (CE&AO) based and randomly generated CB based schemes in terms of effective achievable rate, particularly in rapidly changing channels. The proposed schemes can obtain about 35% increase in the effective achievable rate compared to CE&AO based scheme in 4 users’ scenario with extremely low complexity. In addition, using the partial PI-CB, further reduction in overhead and complexity can be achieved with a slight decrease in performance due to the positioning error.