In modern cloud-native applications, microservices are commonly deployed in containerized environments to ensure scalability and flexibility. However, inter-process communication (IPC) between co-located microservices often suffers from significant overhead, especially when traditional networking protocols are employed within containers. This paper introduces a novel approach, MPKLink, leveraging Intel Memory Protection Keys (MPK) to enhance intra-container communication efficiency while ensuring security. By utilizing shared memory with MPK-based access control, we eliminate unnecessary networking latencies, leading to reduced resource consumption and faster response times. We present a comprehensive evaluation of MPKLink, demonstrating its superior performance over conventional methods such as REST and gRPC within microservice architectures. Furthermore, we explore the integration of this approach with existing container orchestration platforms, showcasing its seamless adoption in real-world deployment scenarios. This work provides a transformative solution for developers looking to optimize communication in microservices while maintaining the integrity and security of containerized applications.

This paper presents "Cloudlab," a comprehensive, cloud-native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for "Security as Code," and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures.

Automatic text summarization has recently become popular in natural language processing because of its ability to minimize an overwhelming quantity of information into a summarization. This research aims at analyzing extractive and abstractive approaches to the automatic text summarization through the help of deep learning models. The work mainly deals with the analysis of the performance of models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) networks and Transformer-based models including BERT and GPT. Pertaining to the evaluation of these models, the study employs objective metrics such as ROUGE, BLEU, in addition to subjective human evaluation of coherence, relevance and fluency. Studies show that Transformer-based models: BERT and GPT perform better than the extractive model in every aspect in the ability to produce summaries with high fluency and context relevancy. However, there are still problems associated with growing the scope of higher-order n-gram recall and preserving summary relevance to the text information. The author of the study also supports the fact that deep learning-based summarization methods demonstrate high potential but require additional studies to improve the quality of the output summary. The work offers an understanding of the elements of the existing models and offers a foundation for future development in automatic summarization.

The security of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learning algorithms, including deep Q Networks and proximal policy optimization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity and Access Management (IAM) policies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results demonstrate that our adaptive RL based framework significantly outperforms static policies, achieving higher intrusion detection rates (92% compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In addition, it maintains high conformity with security requirements and efficient resource usage. These findings validate the effectiveness of adaptive reinforcement learning approaches in improving cloud security policy management.

Sustainable development of agriculture along with food safety and precise resource handling depends on correct crop yield prediction. Traditionally yielded forecast systems find it challenging to handle agricultural data of large scale and multi-source nature thus resulting in inaccurate prediction outcomes. This study develops data-connected crop yield prediction through deep learning framework implementation with TensorFlow and Keras which creates highly precise instant forecast mechanisms. Identifying historical climate data, remote sensing imagery as well as soil characteristics, this approach implements Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for analysis. Through its dynamic learning mechanisms the model detects sophisticated patterns found in agricultural datasets better than both established statistical methodologies and machine learning models at execution speed and adaptability rates. The prediction accuracy through deep learning reached 93% while yield estimate errors declined by 65% and continuous forecasting became 90% faster. The research shows AI predictive analytics can enhance farming choices by improving crop management techniques which benefits agricultural sustainability on a global scale through data science and smart decision systems.