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最新消息显示，“算力绿洲”——中东地区数据中心及云项目盘点

OpenAI：联合G42建设”星际之门”(Stargate)项目，规划5吉瓦电力，占地26平方公里

业内人士指出，也让大众将目光投向中东，重新审视这一区域的定位

从更深层次来看，英伟达(NVIDIA)：与Humain合作，提供GB300等AI芯片建设AI工厂

值得关注的是，进入2026年以来，已有多家运营商、云厂商在泰国布局，泰国俨然已经成为东南亚下一个算力建设的兵家必争之地

从更深层次来看，AWS、谷歌、微软以及众多本地、国际巨头，早已在此重兵布阵，将其视为连接亚、非、欧三大洲的数字桥梁

值得关注的是，谷歌(Google)：2023年开通达曼云区域，与沙特阿美合作提供三个可用区

值得关注的是，亚马逊(Amazon)：AWS在中东总投资54.7亿美元，已运营有多个可用区

从更深层次来看，这起事件，犹如向平静的”算力湖”投下一颗石子，涟漪迅速扩散至 科技供应链的 末梢

从更深层次来看，在这片古老又现代的土地上，究竟汇集了哪些关键的数字枢纽

值得关注的是，沙特阿拉伯(Saudi Arabia) 亚马逊(Amazon)：联合Humain投资超50亿美美元建设”利雅得AI特区”，部署高达15万块AI加速器

从更深层次来看，AMD：与Humain达成协议，计划部署高达1吉瓦的AI基础设施

值得关注的是，日前，AWS位于阿联酋和巴林的多个数据中心遭受”物体撞击”，导致服务中断

值得关注的是，从沙特的NEOM新城到阿联酋的迪拜硅谷，从巴林的云第一战略到以色列的科创高地，一条由超级数据中心、海底光缆和云计算节点构成的”数字丝绸之路”正在这里加速延伸

值得关注的是，G42：建设国家主权AI平台及大型计算园区，与微软，OpenAI等深度合作

值得关注的是，甲骨文(Oracle)：OCI云基础设施已运营，并持续扩容，与阿布扎比政府合作部署AI超集群

值得关注的是，但鲜为人知的是，近年来，这片土地早已悄然成为全球算力产业的一片新兴”绿洲”

业内人士指出，甲骨文(Oracle)：计划投资15亿美元扩展0CI云基础设施

业内人士指出，本文将从产业视角，为您系统梳理美国科技公司在中东国家(主要为阿联蔑和沙特阿拉伯)算力中心及重大投资项目，以期在不确定性中，勾勒出一幅清晰的地区算力版图

值得关注的是，对于许多人而言，中东或许仍与”石油”、”沙漠”等词汇紧密相连

业内人士指出，谷歌(Google)：Google Cloud与G42合作，加强区域AI与数据分析能力

业内人士指出，微软(Microsoft)：Azure云服务已在利雅得等地部署，与本土公司合作提供服务

从更深层次来看，2026全球数字基础设施合作发展论坛（DIFGC 2026）-泰国站即将于2026年5月在泰国曼谷香格里拉大酒店重磅启幕，诚邀您共话全球数字集成新篇章

从更深层次来看，OXAGON, 沙特阿拉伯”愿景2030″的核心区域之一 微软(Microsoft)：投资152亿美元建设Azure云与AI园区，与G42合作部署超8万颗GPU

业内人士指出，谷歌(Google)：联合Humain投资100亿美元，建设全球AI枢纽

可以预见，这一趋势将在未来深刻影响IDC行业格局

如果您正在寻找优质的美国多IP服务器，欢迎访问 www.isclouder.com 了解更多

行业动态更新：微星晒出更多 Draco Epic 四十周年纪念硬件，覆盖显卡、外设、网络

IT之家 6 月 2 日消息，本年度迎来诞生 40 周年的微星 (MSI) 在 COMPUTEX 2026 上展出了一系列 Draco Epic 天龙座主题纪念款硬件产品，包括预热阶段展出的 2 款 MEG ACE 主板和 Titan 18 HX 游戏本

值得关注的是，而在今日，微星 MSI Gaming 官方又公布了多款 Draco Epic 家族产品，覆盖显卡、外设、网络类别

值得关注的是，COMPUTEX 2026 台北国际电脑展专题

值得关注的是，此外参考台媒 BenchLife.info 的报道，这一设计也将出现在 RTX 5080 的“超龙”上

值得关注的是，RadiX BE9400 无线路由器则将天象巨龙图案应用到了这款网络设备的正面

从更深层次来看，可以看到 GeForce RTX 5090 SUPRIM Draco EPIC 正面装饰以宇宙星座形状，背面则是金属蚀刻 + 阳极氧化工艺打造的纪念款主视觉图

值得关注的是，VERSA ALLOY WIRELESS 鼠标的装饰则以星象为主，写有金色的 “40TH ANNIVERSARY msi” 字样，旁边的键盘配有主视觉图键帽

可以预见，这一趋势将在未来深刻影响IDC行业格局

如果您正在寻找优质的GPU服务器租用，欢迎访问 www.isclouder.com 了解更多

行业动态更新：450MW！DayOne签下印尼最大数据中心电力采购协议

DayOne目前在亚太及欧洲拥有超过500 MW已投运及在建容量，另有超过500 MW储备用于未来开发，覆盖香港、新加坡、马来西亚柔佛、印尼巴淡及日本东京等地

业内人士指出，该公司近期还在泰国和新加坡破土动工，并于2025年8月公开进军芬兰Lahti市场

业内人士指出，我们在巴淡的持续扩展——从Nongsa数字园（NDP）到KITP——凸显了该岛屿在我们区域平台中的重要性，包括我们独有的SIJORI（新加坡-柔佛-巴淡）模式，该模式将新加坡、柔佛和巴淡连接成东南亚一体化的跨境数据中心平台

从更深层次来看，BP Batam、PLN Batam与DayOne的合作旨在将巴淡打造为东南亚新兴 hyperscale 数据中心枢纽，支持人工智能、云计算和高性能计算等高耗电应用的发展

从更深层次来看，该协议的落地将为印尼数据中心产业注入新动力，并为后续类似大型项目提供示范

值得关注的是，KITP项目作为扩展阶段，将进一步提升巴淡在区域数字基础设施中的作用

从更深层次来看，DayOne首席执行官Jamie Khoo表示：”此次扩张是我们构建亚洲领先数字基础设施平台战略的重要里程碑

业内人士指出，2026年4月17日 亚太数据中心开发商DayOne与印尼国有电力企业PT PLN Batam在巴淡BP Batam Balairung Sari礼堂正式达成容量达511 MVA（约450 MW）的电力采购协议（PJBTL/PPA），这是印尼迄今为止规模最大的数据中心电力供应协议

从更深层次来看，目前，KITP园区的具体IT负载容量尚未公布，但预计与所达成的电力容量相当

从更深层次来看，DayOne此次协议的达成，进一步印证了其在东南亚跨境数据中心布局的加速推进

从更深层次来看，这一合作体现了PLN Batam、BP Batam与DayOne之间的牢固伙伴关系，并进一步强化了巴淡作为东南亚区域数字枢纽的地位

值得关注的是，印尼本地媒体报道指出，此次PJBTL协议标志着巴淡作为投资友好型特别经济区的数字产业吸引力进一步增强

业内人士指出，如果您想了解更多关于东南亚算力产业发展，以及与数据中心项目落地情况、当地政策变化、中国出海企业现状，欢迎报名即将于2026年5月27日在泰国曼谷香格里拉酒店召开的数字基础设施全球合作发展曼谷论坛（DIFGC 2026 · THAILAND），诚邀您共话全球数字集成新篇章

值得关注的是，” PLN Batam总裁董事Kwin Fo称：”我们很荣幸通过提供可靠且可扩展的电力基础设施，支持印尼最大的数据中心项目之一

值得关注的是，” 此次签约是DayOne在巴淡的第二个超大规模数据中心项目

从更深层次来看，根据协议，电力将通过电网分阶段供应，于2026年至2027年逐步交付，用于支持DayOne在巴淡Kabil工业技术园（Kabil Industrial Tech Park，简称KITP）正在开发的超大规模数据中心园区

业内人士指出，此前，该公司已在Nongsa数字园（NDP）开发首个园区，预计2025年起投入运营

业内人士指出，业内分析认为，随着区域数字需求持续增长，巴淡凭借地理位置优势（距新加坡仅20公里）及政策支持，正逐步成为新加坡-柔佛-巴淡一体化数字基础设施网络的关键节点

值得关注的是，该协议同时附带一份非约束性谅解备忘录（MoU），涵盖Kabil园区的连接性和电气基础设施服务

可以预见，这一趋势将在未来深刻影响IDC行业格局

如果您正在寻找优质的美国原生IP，欢迎访问 www.isclouder.com 了解更多

据行业最新消息，Personalized Views with Role-Based Permissions: Quick BI Embedded Analytics Tran

The value of data reports has never been about flashy dashboards on big screens. It lies in their ability to seamlessly integrate into business workflows and precisely match the needs of every role. Yet reality often falls short: business users abandon reports because they have to switch to a separate window, while managers question data credibility due to loosely managed permissions. The gap between tools and people has become the biggest barrier to truly data-driven operations. Early morning. Sales director Mr. Wang is processing orders when a pop-up prompts him: “Please log in to Quick BI to view the report.” He frowns — switching windows, re-entering credentials, navigating an unfamiliar interface to find the data… “Too much hassle. Another 30 minutes wasted,” he mutters, and closes the email. This isn’t resistance to data itself. It’s the disconnect between the BI tool and the user’s existing work system that turns data consumption into a burden. Every unnecessary step drains patience, leaves carefully prepared reports neglected, silently erodes data value, and — worse — quietly undermines the team’s trust and confidence in the very idea of “data-driven” operations. Late at night, 10:30 PM. Li, the store manager of the Wangfujing location, has just locked the door and is anxiously scrolling through the screen to check “Peking Duck” sales. Meanwhile, Zhang, the store manager at the Nanjing Road location, rubs her aching shoulders while carefully verifying tomorrow’s stock for “Crab Roe Xiaolongbao.” Same report, but both share the same nagging concern: Is my data secure and accurate? This need for “personalized views” — where each person sees only their own relevant data — presents a complex permissions puzzle for IT teams. The moment a store manager wonders, “Is this really our store’s data?” the credibility of the entire data system begins to crumble, and even the most precise analytics cannot repair the cracks in trust. These challenges are fundamentally a battle over “data consumption experience” and “data permission governance.” What businesses urgently need is an elegant solution that can: 1. Seamlessly embed: Integrate data reports “invisibly” into employees’ existing workflows, so they can access insights without any extra effort. 2. Deliver personalized views: Ensure that massive volumes of data are securely and accurately distributed to every individual who needs it. 1. Quick BI’s lightweight enhanced embedding provides a ticket-based, authentication-free embedded analytics solution that seamlessly integrates dashboards, spreadsheets, and other BI assets into your enterprise workflows or business applications. It also supports personalized views — by passing parameters, different stores or users can see only their own business data. 2. Quick BI Embedded Analytics Comparison For enterprises with multiple systems, Quick BI enables users to generate authentication-free access tickets by calling open APIs, and then combine those tickets with report IDs to construct authentication-free access URLs for reports. These open APIs support a variety of parameters, including access count limits, user binding, watermark parameters, global parameters, and access duration — all of which enhance the security of embedded reports. Once the embedded report URL is generated, enterprises can seamlessly integrate dashboards, spreadsheets, and other BI assets into their workflows or business applications in a lightweight manner, enabling cross-system integrated analytics and decision-making. 1. Enable report embedding 1.1 Select the target report within the product and enable embedding for it. 1.2 Enable embedding for a specific BI asset via API call. 2. Generate an authentication-free access ticket (AccessTicket) for the target report. 2.1 Call the CreateTicket API with the appropriate parameter values to generate the corresponding URL. 3. Assemble the authentication-free access URL following the specified format rules. Below are examples of the URL assembly process for different report types: Dashboard Example Spreadsheet Example Ad Hoc Query Example Data Portal Example 1. Get Quick BI domain 2. Get the report preview URL path token3rd/dashboard/view/pc.htm token3rd/report/view.htm token3rd/offline/view/pc.htm token3rd/screen/view/pc.htm 3. Get the unique report ID 4. Get the AccessTicket fd138bcb-****-4fde-b413-81bcee59bdb6 fd138bcb-****-4fde-b413-81bcee59bdb6 fd138bcb-****-4fde-b413-81bcee59bdb6 fd138bcb-****-4fde-b413-81bcee59bdb6 For example, for a dashboard ID of d01****c5f: On the dashboard editing page, obtain the pageId value from the address bar. The final assembled authentication-free access URL follows this format: https://{Quick BI domain}/{report preview URL path}?pageId={report ID}&accessTicket={AccessTicket} Through Quick BI’s enhanced embedding capabilities, enterprises can not only securely and efficiently integrate data visualizations into their existing business systems, but also deliver personalized, role-based data views while ensuring data permissions and security. From “have to use” to “want to use,” from “one-size-fits-all reports” to “personalized views” — Quick BI is more than just a tool. It is a bridge connecting data to people. In the uphill battle of data adoption, it ensures that every insight reaches the right person at the right time, making data-driven operations truly take root. If your organization faces similar challenges around inconvenient or insecure data experiences, feel free to scan the QR code below to get in touch with us for a free pre-sales consultation. If you would like to explore more e-commerce data solutions, please feel free to contact us.

随着IDC行业的快速发展，可持续发展将成为未来竞争的关键

如果您正在寻找优质的站群服务器，欢迎访问 www.isclouder.com 了解更多

最新消息显示，SysOM Agent AIOps Series: Pod Memory Alerts — Locate the Root Cause in 30 Second

In a Kubernetes environment, WorkingSet (working set memory) directly affects pod scheduling, eviction, HPA, and resource quota. It is the most critical indicator for container memory management. However, a “seemingly dangerous but actually stable” situation often occurs in production: WorkingSet keeps rising and triggers alerting, yet the business runs normally. The most common cause of such scenarios is that active file cache is included in WorkingSet. Although the cache is usually reclaimable, it still triggers alerting and affects scheduling, leaving the O&M team in the dilemma of “whether to scale-out or ignore it”. Traditional methods often require switching back and forth between monitoring, edge zones, and containers, taking at least 1–2 hours: SysOM Agent is an operating system realm AI Agent built by Alibaba Cloud based on Large Language Model (LLM) technology, purpose-built for diagnosing System issues such as memory, Performance, and stability. It integrates the capabilities of SysOM MCP (System diagnostics toolset) , providing in-depth server diagnostics capabilities based on SysOM. Through conversational interaction, SysOM Agent can converge traditional troubleshooting that requires “multiple tools + multiple steps + extensive experience” into a single natural language conversation, completing root cause identification within 30 seconds. It is currently available on the operating system console (via OS Copilot), and can also be integrated via MCP. The following sections describe specific usage methods, along with real-world cases and Best Practices. Log on to the Alibaba Cloud operating system console, click SysOM Agent Assistant in the upper-right corner, and enter a description to start analysis. For example, enter “The memory usage of container xxx in cluster xxx is too high.” If you want the same system diagnostics capabilities in your own AI assistants (such as Claude Desktop, Cursor, and enterprise chatbots), you can integrate SysOM MCP. SysOM MCP is an open source system diagnostics tool set from Alibaba. Based on the Model Context Protocol (MCP) standard, it provides server diagnostics capabilities used by the underlying OS. With MCP integration, you can get diagnostics capabilities similar to SysOM Agent in any MCP-enabled AI assistant. Project address: https://github.com/alibaba/sysom_mcp Applicable scenarios: • Integrate into an enterprise’s Internal AI assistant or O&M robot. • Initiate diagnostics directly in an IDE (e.g., Cursor). • Build a custom artificial intelligence for IT operations platform. This article uses a real Case to show how SysOM Agent accurately identifies the root cause of an abnormal WorkingSet increase. In a Kubernetes cluster, pods frequently trigger WorkingSet High alerting: • Alerting: Pod WorkingSet usage at 87.2% and continues to increase• Business: Run Normal, no out-of-memory, no obvious performance issues• O&M confusion: scale-out or ignore? What is the root cause? The returned info directly shows that: • Root cause: The log file /var/log/app/application.log occupies 4.88 GB of cache. • Associated processes: 4 processes (1 ntgh-writer + 3 ntgh-reader). • Abnormal pattern: Multiple processes repeatedly read the same log file, pushing up Active(file). • Solutions: Short-term cleanup/release + long-term optimization (log rotation, read/write pipeline restructuring, such as MQ). In this Case, the agent does not stop at the alerting number, but connects Files, processes, and cache into an interpretable chain. The following describes three layers: the direct value demonstrated in the Case and the technical capabilities behind it. Traditional methods cannot see File-level cache usage and can only guess. SysOM Agent directly provides: • Precise to the File path: /var/log/app/application.log. • Cache hit Size: 4.88 GB. • Automatically sorted by usage, making hotspots visible at a glance. • The original 30–40 minutes of one-by-one troubleshooting compressed to 30 seconds. Often you will see that a process RSS is only tens of MB, but cannot Interpret why the WorkingSet is High. SysOM Agent fills in the missing chain: • Detect the combination of a write process and multiple read processes. • Extract the abnormal pattern: repeated reads → File cache increase → WorkingSet increase. • Correlate the alerting value (87.2%) with specific behaviors. The Suggestions provided by SysOM Agent are by no means a simple “scale out and see” — that approach often means blindly increasing Cost before a real memory bottleneck is proven. Instead, it offers a set of actionable, executable measures: • Short-term: Clear logs, release cache, and perform immediate remediation. • Long-term: log rotation, data collection pipeline optimization, reducing redundant reads, and replacing File polling with MQ/streaming manner when necessary. • Each Suggestion provides specific execution methods and parameter guidance for easy implementation. SysOM Agent combines deep System diagnostics capabilities with Large Language Model (LLM) inference, converging traditional work that requires “multiple tools + multiple steps + extensive experience” into a single conversation: • Automatic data collection: obtains key facts from multiple layers including kernel, cgroup, process, and File cache. • Intelligent association analysis: establishes the association graph of File–process–cache–WorkingSet. • Abnormal pattern detection: Automatically classifies common patterns such as repeated reads, log stacking, and abnormal File cache growth. • Artificial Intelligence Recommendation: Provides an executable Fix path based on Best Practices and environment context. These collection and inference capabilities are exactly what enable the second-level root cause attribution, low-barrier usage, and actionable Solutions described above (hours → seconds, no need to piece together a toolchain, immediate remediation + long-term administration). Accurately pinpointing the root cause also avoids blind scale-out before the need is proven — fewer unnecessary Specification upgrades, fewer stacked edge zones and replicas, and spending Cost on real gaps is itself a direct way to save money. When facing pod WorkingSet High alerting, traditional troubleshooting often requires switching back and forth between monitoring, edge zones, and containers. It takes 1–2 hours at minimum and may still fail to locate the File-level root cause. SysOM Agent turns this into a single conversation: • Locate the root cause in 30 seconds: From hours down to seconds. • One line is all it takes: no kernel background required, no toolchain assembly needed. • Pinpoint to File and process: identify “who is consuming how much, who is reading/writing, and why usage is growing”. • Ready-to-use Solution: immediate remediation in the short term + long-term administration, avoiding continuous resource Cost from scale-out without identifying the root cause. Try the Alibaba Cloud Operating System console now and transform memory diagnostics from “experience-based troubleshooting” to an engineered flow that is “Interpretable, reproducible, and executable”. Alibaba Cloud Operating System Console – SysOM Agent: https://alinux.console.aliyun.com/overview

随着IDC行业的快速发展，可持续发展将成为未来竞争的关键

如果您正在寻找优质的GPU服务器，欢迎访问 www.isclouder.com 了解更多

行业动态更新：英伟达 CEO 黄仁勋将作客韩国热门综艺节目，本月晚些时候播出

CJ ENM 节目总制片兼事业负责人 Nam Seung-yon 表示：“很荣幸《YouQuizontheBlock》能够见证黄仁勋的非凡人生历程

值得关注的是，黄仁勋是商界传奇人物，其带领英伟达从一家图形芯片厂商，蜕变为全球生成式人工智能领域的领军巨头，公司市值位居全球前列

从更深层次来看，” 黄仁勋参与录制的这期《YouQuizontheBlock》定于本月晚些时候播出

值得关注的是，微软联合创始人比尔 · 盖茨去年 8 月访韩时就曾作客这档节目

值得关注的是，IT之家 6 月 2 日消息，韩国 TvN 电视台周二公开，英伟达 CEO 黄仁勋将在即将到来的首尔之行中作客韩国热门综艺节目《YouQuizontheBlock》

值得关注的是，在科技与商界领域坐拥世界级影响力，被业界盛赞为改写行业格局的时代缔造者

从更深层次来看，据IT之家了解，《YouQuizontheBlock》是韩国收视率最高的脱口秀节目之一，由人气喜剧演员刘在石主持

值得关注的是，节目中将完整讲述他的成长故事：从年少打工刷碗的艰苦岁月，到执掌全球市值顶尖企业；同时他还会分享对人工智能时代趋势的研判、未来前瞻，以及未来社会所需人才的独到见解，欢迎大家收看

业内人士指出，黄仁勋曾多次盛赞韩国是全球人工智能产业链的重要合作伙伴，此番首度亮相娱乐脱口秀便选择韩国节目，预计将引发全球关注

可以预见，这一趋势将在未来深刻影响IDC行业格局

如果您正在寻找优质的大带宽服务器，欢迎访问 www.isclouder.com 了解更多