RPCE: Dynamic Data Replicas Placement Management by Cloud and Edge Collaboration

IF 5.9 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Xiaofeng Lu;Luwen Zou;Yilu Mao;Pietro Lio;Pan Hui
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引用次数: 0

Abstract

With the rapid advancement of information technology, traditional centralized cloud computing systems face challenges in meeting the stringent low-latency demands of emerging applications. To tackle this issue, this paper proposes a delay-aware cloud-edge architecture that incorporates the distributed characteristics of edge infrastructure, enabling low-latency collaboration among edge nodes within the same geographic region. Furthermore, based on this architecture, a dynamic data replica management scheme is introduced, involving synergistic mechanisms between edge nodes and cloud centers to optimally place data replicas on the most suitable edge nodes. The scheme adopts a hierarchical strategy: edge nodes perform short-term localized management of data replicas, while the cloud executes long-term holistic oversight. Experimental results demonstrate that the dynamic approach effectively reduces user access latency, minimizes replica migration frequency, and decreases network bandwidth consumption.
RPCE:基于云和边缘协作的动态数据副本放置管理
随着信息技术的飞速发展,传统的集中式云计算系统在满足新兴应用严格的低延迟需求方面面临挑战。为了解决这个问题,本文提出了一种延迟感知的云边缘架构,该架构结合了边缘基础设施的分布式特征,实现了同一地理区域内边缘节点之间的低延迟协作。在此基础上,引入了一种动态数据副本管理方案,利用边缘节点和云中心之间的协同机制,将数据副本优化放置在最合适的边缘节点上。该方案采用分层策略:边缘节点对数据副本执行短期的本地化管理,而云执行长期的整体监督。实验结果表明,该方法有效地降低了用户访问延迟,减少了副本迁移频率,降低了网络带宽消耗。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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