Solving data placement as an integer linear program (ILP) is precise but slow. CATER combines ILP for global optimality and heuristics for real-time adaptability, reducing computation time by 90% while keeping constraint violations at 0%. 🌟 Follow us on LinkedIn!…
Traditional distributed storage systems ignore edge-specific constraints—like data sovereignty, hardware proximity, and privacy. CATER changes that, using integer linear programming and heuristics to place data optimally. Tested on Apache Ozone, it cuts active nodes by 23% while meeting all constraints.…
RDMA’s memory registration overhead can negate its performance benefits. Our optimized GVirtuS RDMA communicator uses pre-registered buffers and polling-based work completion, cutting context switches by 98% and boosting Matrix Multiplication speed by 5.5x over TCP. 🌟 Follow us on LinkedIn!…
Expectation-Maximization (EM) isn’t just for statistics—it’s a powerful tool for AP clustering in D-mMIMO. By iteratively refining channel strength indicators, our method ensures optimal UE-AP associations with minimal overhead. 🌟 Follow us on LinkedIn! Check the updates from the…
Not all channel gains are equal. Our partial and total channel strength indicators ensure optimal AP-UE pairing, balancing SE and EE. Data-driven decisions for smarter 6G networks. 🌟 Follow us on LinkedIn! Check the updates from the website: www.cleverproject.eu…
How do you maintain high SE while reducing processing overhead? Our user-centric D-mMIMO system dynamically adjusts AP clusters, ensuring real-time adaptability without sacrificing performance. Simulation results show <9% SE degradation even with 96% lower CC. Efficiency meets intelligence in 6G.…
More packets (N) mean more Multiply-Accumulate (MAC) operations. Our analysis shows N=50 increases MACs to 30M+, but N=10-20 balances accuracy and efficiency. Optimizing AI for real-world 6G deployment. 🌟 Follow us on LinkedIn! Check the updates from the website:…
Higher N (packet flow length) improves detection but increases computational overhead. Our experiments show N=10-20 offers the best trade-off, achieving 97% F1-scores without overloading base stations. Efficiency meets accuracy in 6G security. 🌟 Follow us on LinkedIn! Check the…
We address a key limitation: effective user space and kernel space management in network scanners, optimizing resource utilization and improving overall scanning speed. 🌟 Follow us on LinkedIn! https://www.linkedin.com/company/clever-project/?viewAsMember=true 🌟 Check the updates from the website: www.cleverproject.eu 🌟 You can read…
Our framework balances aggregated EU welfare, system cost reduction, and fair reward allocation. A simulation tool for exhaustive “what-if” scenario evaluation. 🌟 Follow us on LinkedIn! https://www.linkedin.com/company/clever-project/?viewAsMember=true 🌟 Check the updates from the website: www.cleverproject.eu 🌟 You can read the post…
