Fabian Mastenbroek , Tiziano De Matteis , Vincent van Beek , Alexandru Iosup
{"title":"RADiCe:数据中心的风险分析框架","authors":"Fabian Mastenbroek , Tiziano De Matteis , Vincent van Beek , Alexandru Iosup","doi":"10.1016/j.future.2024.107702","DOIUrl":null,"url":null,"abstract":"<div><div>Datacenter service providers face engineering and operational challenges involving numerous risk aspects. Bad decisions can result in financial penalties, competitive disadvantage, and unsustainable environmental impact. Risk management is an integral aspect of the design and operation of modern datacenters, but frameworks that allow users to consider various risk trade-offs conveniently are missing. We propose <span>RADiCe</span>, an open-source framework that enables data-driven analysis of IT-related operational risks in sustainable datacenters. <span>RADiCe</span> uses monitoring and environmental data and, via discrete event simulation, assists datacenter experts through systematic evaluation of risk scenarios, visualization, and optimization of risks. Our analyses highlight the increasing risk datacenter operators face due to price surges in electricity and sustainability and demonstrate how <span>RADiCe</span> can evaluate and control such risks by optimizing the topology and operational settings of the datacenter. Eventually, <span>RADiCe</span> can evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107702"},"PeriodicalIF":6.2000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RADiCe: A Risk Analysis Framework for Data Centers\",\"authors\":\"Fabian Mastenbroek , Tiziano De Matteis , Vincent van Beek , Alexandru Iosup\",\"doi\":\"10.1016/j.future.2024.107702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Datacenter service providers face engineering and operational challenges involving numerous risk aspects. Bad decisions can result in financial penalties, competitive disadvantage, and unsustainable environmental impact. Risk management is an integral aspect of the design and operation of modern datacenters, but frameworks that allow users to consider various risk trade-offs conveniently are missing. We propose <span>RADiCe</span>, an open-source framework that enables data-driven analysis of IT-related operational risks in sustainable datacenters. <span>RADiCe</span> uses monitoring and environmental data and, via discrete event simulation, assists datacenter experts through systematic evaluation of risk scenarios, visualization, and optimization of risks. Our analyses highlight the increasing risk datacenter operators face due to price surges in electricity and sustainability and demonstrate how <span>RADiCe</span> can evaluate and control such risks by optimizing the topology and operational settings of the datacenter. Eventually, <span>RADiCe</span> can evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"166 \",\"pages\":\"Article 107702\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X24006666\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24006666","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}
RADiCe: A Risk Analysis Framework for Data Centers
Datacenter service providers face engineering and operational challenges involving numerous risk aspects. Bad decisions can result in financial penalties, competitive disadvantage, and unsustainable environmental impact. Risk management is an integral aspect of the design and operation of modern datacenters, but frameworks that allow users to consider various risk trade-offs conveniently are missing. We propose RADiCe, an open-source framework that enables data-driven analysis of IT-related operational risks in sustainable datacenters. RADiCe uses monitoring and environmental data and, via discrete event simulation, assists datacenter experts through systematic evaluation of risk scenarios, visualization, and optimization of risks. Our analyses highlight the increasing risk datacenter operators face due to price surges in electricity and sustainability and demonstrate how RADiCe can evaluate and control such risks by optimizing the topology and operational settings of the datacenter. Eventually, RADiCe can evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.