2023· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
The findings support the fact that smart caching schemes offer a solid and scalable remedy to optimization of next-generation cloud storage.
Abstract
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.
This study aims to compare and analyze the different aspects of ultra-large data storage systems in cloud computing, with the help of a mind-mapping diagram of cloud-oriented data storage (CODS) elements.
Ajay Kumar, S. Bawa, Neeraj Kumar et al.· ACM Computing Surveys· 0 citations
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.
Farah Al-Farsi· International Journal of App...· 0 citations
The built-in caching capability of Named Data Networking (NDN) is one of the most transformative proposals of next-generation network architecture, as it simultaneously realizes network traffic reduction, resilience to node failures, and prompt data retrieval. However, existing caching policies either make too many caches across the network by naively copying content everywhere or incur excess communication overhead for efficient caching through cache state exchange. Hence, we propose a machine learning-based caching policy along with a communication-efficient method to share the cache state. As the intelligent caching policy is capable of learning request patterns and estimating cache states of other nodes, our proposal realizes a more efficient utilization of the cache capacity by virtually considering the cache spaces of neighboring NDN nodes as an aggregated, larger cache space. The extensive evaluation experiments demonstrate the effectiveness of our proposal in increasing the cache hit ratio and reducing the average distance that each data travels to reach a requesting user. The effectiveness of the proposed cache state sharing method is empirically verified through an interpretable machine learning technique.
Deep Pradipbhai Shah, Sai Sameer Yanamandra, Siva Girish Ramesh et al.· International Conference on...· 0 citations
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
Seshagiri N· International Journal of Dat...· 0 citations
With the rapid development of the big data industry, data volume across various industries has exploded, and large-scale datasets at PB and EB levels have become mainstream objects for data processing. Relying on core theories of distributed storage and query, this paper constructs an integrated collaborative optimization system covering storage, query and caching. Storage efficiency is improved by designing an adaptive dynamic sharding strategy and intelligent multi-replica placement policy, building a tiered storage architecture for hot and cold data, and optimizing storage encoding and compression mechanisms. Query overhead is reduced by reconstructing query execution plans based on cost models, pushing down operators, and optimizing cross-node transmission. A collaborative global-local indexing framework and a multi-level caching linkage mechanism are established to further boost query response performance. A standardized distributed experimental cluster is deployed, and multi-dimensional comparative experiments are conducted to verify the performance of the proposed optimization scheme. Experimental results demonstrate that the proposed optimization solution can effectively cut storage redundancy overhead, improve cluster resource utilization, drastically reduce query latency for large-scale data, and raise concurrent throughput. It can provide theoretical support and engineering practice references for distributed system optimization in industrial big data, internet massive data processing and other scenarios.
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
