{"title":"GeoDeploy: Geo-Distributed Application Deployment Using Benchmarking","authors":"Devki Nandan Jha;Yinhao Li;Zhenyu Wen;Graham Morgan;Prem Prakash Jayaraman;Maciej Koutny;Omer F. Rana;Rajiv Ranjan","doi":"10.1109/TPDS.2024.3470532","DOIUrl":null,"url":null,"abstract":"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 \n<sc>GeoDeploy</small>\n, a framework designed for finding a deployment solution for GWA. We evaluate \n<sc>GeoDeploy</small>\n using both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2361-2374"},"PeriodicalIF":5.6000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10697467/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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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