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.

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.

The rapid adoption of pervasive and mobile computing has led to an unprecedented rate of data production and consumption by mobile applications at the network edge. These applications often require interactions such as data exchange, behavior coordination, and collaboration, which are typically mediated by cloud servers. While cloud computing has been effective for distributed systems, challenges like latency, cost, and intermittent connectivity persist. With the advent of 5G technology, features like location-awareness and device-to-device (D2D) communication enable a more distributed and adaptive architecture. This paper introduces Self-Organizing Interaction Spaces (SOIS), a novel framework for engineering pervasive applications. SOIS leverages the dynamic and heterogeneous nature of mobile nodes, allowing them to form adaptive organizational structures based on their individual and social contexts. The framework provides two key abstractions for modeling and programming pervasive applications using an organizational mindset and mechanisms for adapting dynamic organizational structures. Case examples and performance evaluations of a simulated mobile crowd-sensing application demonstrate the feasibility and benefits of SOIS. Results highlight its potential to enhance efficiency and reduce reliance on traditional cloud models, paving the way for innovative solutions in mobile and distributed environments.