Virtual and physical address translation mechanism of interconnect network

Most of users are accustomed to utilizing virtual address in their parallel programs running at the scalable high-performance parallel computing systems.Therefore a virtual and physical address translation mechanism is necessary and crucial to bridge the hardware interface and software application.In this paper,a new virtual and physical translation mechanism is proposed,which includes an address validity checker,an address translation cache（ATC）,a complete refresh scheme and many reliability designs.The ATC employs a large capacity embedded dynamic random access memory（eDRAM）to meet the high hit ratio requirement.It also can switch the cache and buffer mode to avoid the high latency of accessing the main memory outside.Many tests have been conducted on the real chip,which implements the address translation mechanism.The results show that the ATC has a high hit ratio while running the well-known benchmarks,and additionally demonstrates that the new high-performance mechanism is well designed.

Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.