High Availability Ultra-Large Cloud-Oriented Storage Systems: A Review, Mind Mapping and Open Research Directions

Optimizing Cloud Storage Using Intelligent Caching Algorithms

Cloud computing has become the new foundation of the current digital network, which provides the possibility to store data with scaling and on-demand access to huge amounts of data. Nevertheless, the fast development of the cloud-based applications has posed the significant threats in terms of latency, bandwidth usage as well as storage efficiency. Conventional cloud storage platforms are very much dependent on the concept of traditional or heuristic based caching whereby caching is not always adequate to adjust to the dynamism and heterogeneity of workloads. The given paper is a complete research on the optimization of cloud storage systems by using intelligent caching algorithms. Through machine learning, predictive analytics, and adaptive replacement technologies, intelligent caching will be used to enhance the speed and efficiency of data access, minimizing network congestions, and increasing the efficiency of a system as a whole. The suggested algorithm is a combination of workload-sensitive placement of caches, predicting access pattern, and managing the cache in real time to dynamically optimize storage throughput. The high performance of the cloud of isolated simulated cloud workloads has been extensively tested and results in high improvements in cache hit ratio, response time and bandwidth use as compared to traditional caching methods. The findings support the fact that smart caching schemes offer a solid and scalable remedy to optimization of next-generation cloud storage.

Edge Computing Architectures for Ultra-Low Latency Applications

The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.

Intelligent Portable Edge-Cloud Computing Ar-chitecture for Secure Data Analysis and Adaptive Resource Optimization Using AI-Driven Resource Scheduling

This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.

Scalable and Interactive Data Deduplication in Cloud Environment with Enhanced Quality of Service

Scalable Microservices Architecture for Data-Intensive Applications

The modern software architectures have never had higher requirements than they do currently as the result of the rapid growth of data-intensive applications in the fields of cloud computing, Internet of Things (IoT), artificial intelligence, and large-scale web services. Amongst the problems that traditional monolithic systems cannot cope with are scalability, fault tolerance and on-going deployment demands using extensive data workloads. The consequence of this is that microservice architecture has become an architecture with potential to make systems that are scalable, resilient, and maintainable. The paper provides a detailed research of scalable microservices architecture specifically to the data intensive applications. The suggested architecture breaks the complex applications in loosely coupled, independently deployable services, which are specialized in the processing of specific data. Such fundamental architectural values as service granularity, data decentralization, containerization, orchestration, and asynchronous communication are discussed in detail. An overlay-based architectural design is presented to help scalability horizontally and provide high availability as well as efficient data management. Also, the analysis of current architectural methods is done in this study based on the comprehensive literature review that finds out the limitations in scalability, consistency, and operational complexity. An end-to-end process is suggested, where distributed data stores are combined with event-driven communication and automatic scaling processes. The analysis of performance has shown that the proposed solution is much better in terms of throughput, latency and fault isolation than the conventional architectures. These findings prove that microservices architecture with proper design presents a solid base of scalable data-intensive systems. The paper is useful to the researchers and practitioners who need to develop high-performance, cloud-native applications using micro services.

Scalable Load Balancing Algorithms for Cloud Infrastructures

The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms.