GeoDeploy：利用基准测试进行地理分布式应用部署

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-09-27 DOI:10.1109/TPDS.2024.3470532

Devki Nandan Jha;Yinhao Li;Zhenyu Wen;Graham Morgan;Prem Prakash Jayaraman;Maciej Koutny;Omer F. Rana;Rajiv Ranjan

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引用次数: 0

摘要

地理分布式网络应用程序（GWA）可以部署在多个地理位置分离的数据中心，以减少用户访问的延迟。由于需要考虑许多不同的参数，例如联合基础设施中的主机配置，因此为 GWA 找到合适的部署方式具有挑战性。评估多种部署配置的能力可以确定有效的结果，在满足用户需求的同时平衡资源使用。我们提出的 GeoDeploy 是一个旨在为 GWA 寻找部署解决方案的框架。我们使用正式算法模型和基于云的实际部署对 GeoDeploy 进行了评估。我们还将我们的方法与其他现有技术进行了比较。

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GeoDeploy: Geo-Distributed Application Deployment Using Benchmarking

Geo-distributed web-applications (GWA) can be deployed across multiple geographically separated datacenters to reduce the latency of access for users. Finding a suitable deployment for a GWA is challenging due to the requirement to consider a number of different parameters, such as host configurations across a federated infrastructure. The ability to evaluate multiple deployment configurations enables an efficient outcome to be determined, balancing resource usage while satisfying user requirements. We propose GeoDeploy , a framework designed for finding a deployment solution for GWA. We evaluate GeoDeploy using both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.

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来源期刊

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.