摘 要
随着云计算技术的快速发展，大数据处理面临着新的机遇与挑战。本研究旨在探索云计算环境下高效、可靠的大数据处理技术，以应对海量数据存储、计算和分析的需求。研究采用分布式计算框架与云平台相结合的方法，重点解决了数据分区、任务调度和容错机制等关键技术问题。通过构建基于Hadoop和Spark的混合计算模型，实现了对大规模数据的并行处理与实时分析。实验结果表明，所提出的优化算法在数据处理效率上较传统方法提升了35%，同时资源利用率提高了28%。创新性地提出了基于深度学习的自适应任务调度策略，有效降低了系统延迟并提高了吞吐量。研究还设计了多层次的数据安全保护机制，确保了云环境下数据处理的隐私性和完整性。主要贡献在于开发了一套完整的云计算大数据处理解决方案，为相关领域的研究提供了理论依据和实践参考。研究成果可广泛应用于智慧城市、金融科技和医疗健康等领域，具有重要的学术价值和现实意义。
关键词：云计算 大数据处理 分布式计算框架 任务调度优化
Abstract
With the rapid development of cloud computing technology, big data processing is facing new opportunities and challenges. This study aims to explore efficient and reliable big data processing technologies in cloud computing environments to meet the needs of massive data storage, computing and analysis. The research adopts the combination of distributed computing fr amework and cloud platform, and focuses on solving the key technical problems such as data partition, task scheduling and fault tolerance mechanism. The allel processing and real-time analysis of large-scale data are realized by constructing a hybrid computational model based on Hadoop and Spark. The experimental results show that the proposed optimization algorithm improves the data processing efficiency by 35% compared with the traditional method, and improves the resource utilization rate by 28%. We innovatively propose a deep learning-based adaptive task scheduling strategy, which effectively reduces the system latency and improves the throughput. The study also designed a multi-level data security protection mechanism to ensure the privacy and integrity of data processing in the cloud environment. The main contribution lies in the development of a complete set of cloud computing big data processing solutions, which provides theoretical basis and practical reference for research in related fields. The research results can be widely used in smart city, fintech and health care, which has important academic value and practical significance.
Keyword:Cloud computing Big data processing Distributed computing fr amework Task scheduling optimization
目 录
1 引言 1
2 云计算环境下大数据处理的关键技术 1
2.1 分布式存储技术在云计算环境下的应用 1
2.2 并行计算框架在云计算环境下的优化 2
2.3 数据安全与隐私保护机制研究 3
3 云计算环境下大数据处理的性能优化 3
3.1 资源调度算法的改进与实现 3
3.2 数据处理效率的提升策略 4
3.3 能耗优化与绿色计算研究 5
4 云计算环境下大数据处理的应用实践 5
4.1 典型行业应用案例分析 5
4.2 系统架构设计与实现方案 6
4.3 应用效果评估与优化建议 7
5 结论 7
参考文献 9
致谢 10
