#micro-vms

[kenyatta]

The history of computing is one of innovation followed by scale up which is then broken by a model that “scales out”—when a bigger and faster approach is replaced by a smaller and more numerous approaches. Mainframe->Mini->Micro->Mobile, Big iron->Distributed computing->Internet, Cray->HPC->Intel/CISC->ARM/RISC, OS/360->VMS->Unix->Windows NT->Linux, and on and on. You can see this at these macro levels, or you can see it at the micro level when it comes to subsystems from networking to storage to memory. The past 5 years of AI have been bigger models, more data, more compute, and so on. Why? Because I would argue the innovation was driven by the cloud hyperscale companies and they were destined to take the approach of doing more of what they already did. They viewed data for training and huge models as their way of winning and their unique architectural approach. The fact that other startups took a similar approach is just Silicon Valley at work—the people move and optimize for different things at a micro scale without considering the larger picture. See the sociological and epidemiological term small area variation. They look to do what they couldn’t do at their previous efforts or what the previous efforts might have been overlooking.

- DeepSeek Has Been Inevitable and Here's Why (History Tells Us) by Steven Sinofsky

[utopicwork]

"⚡ PyXL runs Python directly in hardware — no VM, no OS, no JIT."

[necrotech-puppywitch]

Installed micro c on my flipper, wrote a tiny lil rat (in the flipper zero knockoff duckyscript) and a script to target a windows 10 enterprise machine with virtual hardware priv esc/ ecape program, now to figure out passthrough of the flipper badusb/kb to a vm for testing. This is really fun UwU

note: everything im doing is legal and on my own hardware, this is security research, i have ethics and stuffs and have developed empathy since starting estrowogen a year and a month ago, so dont be like worried or anything im jwust a silly puppy :3

[everydayducksoup]

Third Winter

American men who talk like frogs and silver hoops getting cold and fat jackets and the light that comes through the ceiling of the Harvard art museum café. People-watching and Bossa Nova and all the little things you wanted as a kid, and wondering why you don't walk places anymore.

There are concrete reasons. But the practicality of walking and the nice feeling you get when you blame yourself for life being less exciting are two very different things— change slow.

American men talk like frogs. They croak. They plead with their voices in strange, tight ways, their crutch phrases are terrifying. Everything is sharp-fast in America. America is a place you need frequent vacations for, even if they're back into America. Maybe the world is a place you need frequent vacations from into delusion (re: After Hammond B3 Organ Cistern from yesterday’s post, believe nothing I say about “you”)

Sometimes I wish I was Ai Wei Wei’s blog instead of my blog. That would be fun! His blog was full of funny New York pictures. Maybe I should vacation to New York with every penny I have saved right now. One-day round trip in a magical imagination. Maybe the next 100 words can be a prescient review of Objects of Addiction, the new show at the Harvard museum that's not actually very new.

Review! The show was really really good. We caught a tour by the curator— she seemingly had a bunch of highschool friends visiting who took it together which was funny. They were mostly health professionals and then there was her, mid-to-late 40s, with a dyed red bob and micro bangs and talking about having no-longer-legal PDFs of certain collector's catalogues. I want to be her so severely I considered my worldly chances of getting into Harvard for a Master's degree— I should join the VMS thesis next year.

[andrewbgould]

Yes, I Got a Cloud Server for Free Forever Here’s How You Can Too.

Let me tell you a little secret: I'm running a cloud server right now—for zero dollars. No tricks, no expired trials, no surprise bills. Just forever free. And the best part? You can do it too.

This isn’t one of those “free for 12 months” offers that quietly auto-bills you after a year. This is 100% free forever, and I’ve been using it to host websites, run experiments, and even power a personal VPN.

Here's how I did it—and how you can get your own cloud server for free too.

🧠 First, Why Would You Even Want a Cloud Server? If you’re a dev, student, entrepreneur, or just someone who wants to mess around with tech, a cloud server is basically your Swiss Army knife. You can:

Host your own blog or portfolio

Deploy web apps

Run bots or automation scripts

Set up a secure VPN

Tinker with Docker, Node.js, Python, and other cool tools

Learn cloud infrastructure (and impress your next job interviewer)

But traditionally, cloud servers cost money. Not a lot—but still enough to be annoying if you’re bootstrapping.

🧨 The Hack: Free-Tier Cloud Servers (Yes, They’re Legit) Here’s the part most people don’t realize: some major cloud providers offer "always free" tiers. These aren’t trials. They’re permanent free resources designed to bring developers into their ecosystem.

Let me break down the best ones:

🟢 Oracle Cloud - The Real MVP What You Get (Always Free):

2 Arm-based virtual machines (VMs)

1 GB RAM each, up to 4 vCPUs

200 GB storage

10 TB/month of data transfer

👉 This is what I'm using. It’s not just a toy—this thing can run websites, apps, and even a Minecraft server (with some optimization). It’s shockingly good for something that costs nothing.

Pro Tip: Choose the Ampere A1 (Arm) VM when signing up. They're the magic machines that stay free forever.

🔵 Google Cloud Free Tier 1 f1-micro VM (in select regions)

30 GB HDD + 5 GB snapshot storage

1 GB outbound traffic

It’s more limited, but solid if you want to dip your toes into Google’s ecosystem. Great for small side projects or learning.

🟠 Amazon AWS Free Tier 750 hours/month of a t2.micro or t3.micro EC2 instance

5 GB S3 storage

Other bonuses like Lambda and DynamoDB

⚠️ This one’s only free for the first 12 months, so set a calendar reminder unless you want to wake up to a surprise bill.

Honorable Mentions (For Web Devs & Hobby Projects) Flyio – Run full-stack apps with generous free bandwidth

Render / Railway – Deploy static sites, APIs, and databases with ease

GitHub Student Pack – If you’re a student, you unlock a TON of free cloud goodies

⚠️ A Few Quick Warnings Before you go server-crazy:

Stick to free-tier specs (e.g. Oracle’s Ampere A1, Google’s f1-micro)

Watch bandwidth usage—10 TB sounds like a lot, until it isn’t

Avoid regions that aren’t free (yes, it matters where your VM is located)

Set up billing alerts or hard limits if the provider allows it

So… What Can You Actually Do With a Free Server? Here’s what I use mine for:

✅ Hosting my personal website (no ads, no downtime) ✅ Running a WireGuard VPN to stay safe on public Wi-Fi ✅ Testing code 24/7 without killing my laptop ✅ Hosting bots for Discord and Telegram ✅ Learning Linux, Docker, and server security

Basically, it’s my own little lab in the cloud—and I didn’t pay a dime for it.

Final Thoughts: Cloud Power in Your Pocket (For Free) In a world where subscriptions are everywhere and everything feels like a money grab, it's refreshing to find real value that doesn’t cost you anything. These cloud free tiers are hidden gems—quietly sitting there while most people assume you need a credit card and a corporate budget to get started.

So go ahead. Spin up your own free cloud server. Learn. Build. Break things. And have fun doing it—on someone else’s infrastructure.

🔗 Want to try it?

Oracle Cloud Free Tier

Google Cloud Free Tier

AWS Free Tier

[renatoferreiradasilva]

Artigo Técnico: Desafio em Detecção de Ameaças Polimórficas Multicanal com Baixa Latência

Título

"Correlação em Tempo Real de Ameaças Polimórficas Distribuídas em Ecossistemas Assíncronos: Um Problema Aberto em Cibersegurança Preventiva"

Resumo

A evolução de ataques cibernéticos polimórficos distribuídos por múltiplos canais (e-mail, SaaS, armazenamento em nuvem) exige novas abordagens de detecção proativa. Este artigo apresenta um problema crítico ainda não resolvido pela indústria: como correlacionar eventos de ameaças fracionadas em fluxos assíncronos com latência subsegundo e precisão acima de 99%, sem gerar alertas excessivos. Discutimos as limitações das soluções atuais, propomos uma arquitetura baseada em grafos dinâmicos e aprendizado federado, e delineamos métricas rigorosas para validação.

1. Introdução: O Cenário da Ameaça Moderna

Ataques modernos não ocorrem em canais isolados. Um invasor pode:

Distribuir componentes maliciosos entre e-mail (phishing), Slack (instruções) e Google Drive (payload)

Usar técnicas polimórficas para alterar assinaturas entre entregas

Ativar vetores sequencialmente para evitar detecção pontual

Dados preocupantes:

78% dos ataques avançados usam ≥2 canais (CrowdStrike 2023)

0,5 segundos de atraso na detecção podem permitir a exfiltração de 10 GB de dados (IBM Cost of Data Breach)

2. Definição do Problema Técnico

Problema Central

Como projetar um sistema que:

Restrições Críticas

{Lateˆncia Total=tingesta˜o+tcorrelac¸a˜o+tdecisa˜o≤700msP(Detecc¸a˜o∣Ataque Multicanal)≥0.99P(Falso Positivo∣Evento Benigno)≤0.001Overhead Computacional≤40% em noˊs commodity

3. Limitações das Abordagens Atuais

TécnicaFalhas no Contexto MulticanalSandboxing ClássicoNão correlaciona eventos entre canaisEDR TradicionalOpera apenas pós-execuçãoSIEMs ConvencionaisLatência >2s para correlação complexaModelos de ML EstáticosVulneráveis a ataques adversariais

Caso Concreto: Em 2023, um ataque ao setor financeiro usou:

PDF ofuscado via e-mail

Instruções de desofuscação no Slack

Payload final no SharePoint Soluções existentes detectaram componentes isolados, mas falharam na correlação até 72h depois.

4. Proposta de Arquitetura: Sentinel-X

Solução em 4 camadas interconectadas:

4.1. Camada de Ingestão Contextual

Mecanismo: Priorização dinâmica via embeddings semânticos

Inovação: aplicação de modelos BERT para gerar embeddings de contexto e direcionar recursos computacionais para eventos com maior probabilidade de risco.

4.2. Núcleo de Correlação Temporal

Modelo: Grafo dinâmico com Temporal Graph Networks (TGN)

Equação de Atualização:

hv(t+1)=TGN-Cell(hv(t),{muv∣u∈N(v)},t)

Onde muv são mensagens entre entidades (domínios, arquivos, IPs).

4.3. Módulo de Emulação Fracionada

Técnica: Execução parcial em micro-VMs (Firecracker)

Métrica Chave:

Tempoemulac¸a˜o=50ms+0.1⋅sizeMB

4.4. Loop de Aprendizado Federado

Protocolo:

sequenceDiagram Participante->>Coordenador: ∇W_local (gradientes criptografados) Coordenador->>Modelo Global: Aggregate(∑∇W) Modelo Global->>Participantes: W_global atualizado

5. Subproblemas de Pesquisa

SP1: Otimização de Consultas em Grafos Temporais

Desafio: Encontrar padrões de ameaça em janelas de 5ms com 1M+ de arestasMATCH (e:Email)-[r:RELATED_TO]->(s:SlackMessage) WHERE r.timestamp > datetime() - duration('PT5M') AND r.correlation_score > 0.95

SP2: Transferência de Aprendizado entre Canais

Problema: Como treinar um modelo para Slack usando dados de e-mail sem vazamento de contexto? Abordagem Proposta:Ltransfer=α⋅Ltask+β⋅MMD(Demail,Dslack)

SP3: Compressão de Emulação com Preservação Semântica

Técnica Experimental:def emulate_fractional(payload): critical_syscalls = ["execve", "invoke", "network_out"] return [syscall for syscall in execute(payload) if syscall in critical_syscalls]

6. Métricas de Validação Rigorosas

MétricaFerramentaBenchmark AceitávelRecall MulticanalMITRE CALDERA + GANs≥98% (ataques 3+ canais)Precisão DecisorialSHAP + LIMEFalso Positivo <0,1%Latência P99Locust + Gatling≤650ms em carga de picoRobustez AdversarialART FrameworkAtaques evasão <0,5%

7. Conclusão e Chamado à Ação

O problema exige colaboração entre:

Pesquisadores de ML/Grafos: Para modelos de correlação espaço-temporal

Engenheiros de Sistemas Distribuídos: Para otimização de latência

Especialistas em Segurança: Para modelagem de TTPs multicanal

[cybersecurityict]

Exascale Computing Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: The Race to One Quintillion Calculations Per Second

The Exascale Computing Market was valued at USD 3.47 billion in 2023 and is expected to reach USD 29.58 billion by 2032, growing at a CAGR of 26.96% from 2024-2032.

The Exascale Computing Market is undergoing a profound transformation, unlocking unprecedented levels of computational performance. With the ability to process a billion billion (quintillion) calculations per second, exascale systems are enabling breakthroughs in climate modeling, genomics, advanced materials, and national security. Governments and tech giants are investing aggressively, fueling a race for exascale dominance that’s reshaping industries and redefining innovation timelines.

Exascale Computing Market revolutionary computing paradigm is being rapidly adopted across sectors seeking to harness the immense data-crunching potential. From predictive simulations to AI-powered discovery, exascale capabilities are enabling new frontiers in science, defense, and enterprise. Its impact is now expanding beyond research labs into commercial ecosystems, paving the way for smarter infrastructure, precision medicine, and real-time global analytics.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/6035 

Market Keyplayers:

Hewlett Packard Enterprise (HPE) [HPE Cray EX235a, HPE Slingshot-11]

International Business Machines Corporation (IBM) [IBM Power System AC922, IBM Power System S922LC]

Intel Corporation [Intel Xeon Max 9470, Intel Max 1550]

NVIDIA Corporation [NVIDIA GH200 Superchip, NVIDIA Hopper H100]

Cray Inc. [Cray EX235a, Cray EX254n]

Fujitsu Limited [Fujitsu A64FX, Tofu interconnect D]

Advanced Micro Devices, Inc. (AMD) [AMD EPYC 64C 2.0GHz, AMD Instinct MI250X]

Lenovo Group Limited [Lenovo ThinkSystem SD650 V3, Lenovo ThinkSystem SR670 V2]

Atos SE [BullSequana XH3000, BullSequana XH2000]

NEC Corporation [SX-Aurora TSUBASA, NEC Vector Engine]

Dell Technologies [Dell EMC PowerEdge XE8545, Dell EMC PowerSwitch Z9332F]

Microsoft [Microsoft Azure NDv5, Microsoft Azure HPC Cache]

Amazon Web Services (AWS) [AWS Graviton3, AWS Nitro System]

Sugon [Sugon TC8600, Sugon I620-G30]

Google [Google TPU v4, Google Cloud HPC VM]

Alibaba Cloud [Alibaba Cloud ECS Bare Metal Instance, Alibaba Cloud HPC Cluster]

Market Analysis The exascale computing landscape is characterized by high-stakes R&D, global governmental collaborations, and fierce private sector competition. With countries like the U.S., China, and members of the EU launching national initiatives, the market is shaped by a mix of geopolitical strategy and cutting-edge technology. Key players are focusing on developing energy-efficient architectures, innovative software stacks, and seamless integration with artificial intelligence and machine learning platforms. Hardware giants are partnering with universities, startups, and defense organizations to accelerate deployments and overcome system-level challenges such as cooling, parallelism, and power consumption.

Market Trends

Surge in demand for high-performance computing in AI and deep learning

Integration of exascale systems with cloud and edge computing ecosystems

Government funding and national strategic investments on the rise

Development of heterogeneous computing systems (CPUs, GPUs, accelerators)

Emergence of quantum-ready hybrid systems alongside exascale architecture

Adoption across healthcare, aerospace, energy, and climate research sectors

Market Scope

Supercomputing for Scientific Discovery: Empowering real-time modeling and simulations at unprecedented speeds

Defense and Intelligence Advancements: Enhancing cybersecurity, encryption, and strategic simulations

Precision Healthcare Applications: Supporting drug discovery, genomics, and predictive diagnostics

Sustainable Energy Innovations: Enabling complex energy grid management and fusion research

Smart Cities and Infrastructure: Driving intelligent urban planning, disaster management, and IoT integration

As global industries shift toward data-driven decision-making, the market scope of exascale computing is expanding dramatically. Its capacity to manage and interpret massive datasets in real-time is making it essential for competitive advantage in a rapidly digitalizing world.

Market Forecast The trajectory of the exascale computing market points toward rapid scalability and broader accessibility. With increasing collaborations between public and private sectors, we can expect a new wave of deployments that bridge research and industry. The market is moving from proof-of-concept to full-scale operationalization, setting the stage for widespread adoption across diversified verticals. Upcoming innovations in chip design, power efficiency, and software ecosystems will further accelerate this trend, creating a fertile ground for startups and enterprise adoption alike.

Access Complete Report: https://www.snsinsider.com/reports/exascale-computing-market-6035 

Conclusion Exascale computing is no longer a vision of the future—it is the powerhouse of today’s digital evolution. As industries align with the pace of computational innovation, those embracing exascale capabilities will lead the next wave of transformation. With its profound impact on science, security, and commerce, the exascale computing market is not just growing—it is redefining the very nature of progress. Businesses, researchers, and nations prepared to ride this wave will find themselves at the forefront of a smarter, faster, and more resilient future.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[mightilymellowseer]

免费云服务器：全面指南与最佳选择

什么是免费云服务器？

免费云服务器是指云计算服务提供商为用户提供的无需付费即可使用的虚拟服务器资源。这些服务器通常具备基础的计算、存储和网络功能，适用于开发测试、学习或个人项目。与付费云服务器相比，免费云服务器在性能和资源上可能有所限制，但对于初学者和小型项目来说，仍然是一个极具吸引力的选择。

为什么选择免费云服务器？

零成本入门云计算

对于刚接触云计算的新手来说，免费云服务器提供了一个零成本的学习环境，无需担心初期投入。

适合开发和测试

开发者可以使用免费云服务器搭建测试环境，运行小型应用，验证代码的可行性。

个人项目托管

如果你有一个博客、小型网站或私人项目，免费云服务器可以满足基本托管需求。

体验不同云平台

许多云服务商提供免费试用，用户可以通过免费云服务器体验不同平台的功能和服务质量。

主流免费云服务器提供商

1. AWS Free Tier（亚马逊云免费套餐）

AWS（Amazon Web Services）提供12个月的免费云服务器（EC2），包括每月750小时的t2.micro实例使用时间。适合长期学习和轻度使用。

优点：

全球覆盖，稳定性高

丰富的云服务生态

缺点：

超出免费额度后可能产生费用

对新手来说管理界面较复杂

2. Google Cloud Free Tier（谷歌云免费套餐）

Google Cloud 提供$300的免费试用额度，可用于免费云服务器（Compute Engine），有效期为90天。

优点：

高性能计算资源

支持多种操作系统

缺点：

试用期较短

需要绑定信用卡

3. Microsoft Azure Free Account（微软云免费账户）

Azure 提供12个月的免费云服务器（B1S Burstable VM），以及$200的免费额度，适用于新用户。

优点：

适合Windows用户

与微软生态无缝集成

缺点：

免费资源较少

部分高级功能需付费

4. Oracle Cloud Free Tier（甲骨文云免费套餐）

Oracle Cloud 提供永久免费的免费云服务器（2个AMD实例 + 4个ARM核心），存储和带宽也有免费额度。

优点：

永久免费，无时间限制

性能较强，适合长期项目

缺点：

注册审核较严格

部分区域资源有限

5. Alibaba Cloud Free Trial（阿里云免费试用）

阿里云提供1个月的免费云服务器（ECS），适合国内用户使用。

优点：

国内访问速度快

中文支持友好

缺点：

试用期较短

国际用户可能受限

如何选择适合自己的免费云服务器？

根据使用时长选择

如果需要长期免费，Oracle Cloud 和 AWS Free Tier（12个月）是不错的选择。

如果只是短期测试，Google Cloud 或 Azure 的试用额度可能更合适。

根据地理位置选择

国内用户优先考虑阿里云或腾讯云（部分免费资源）。

国际用户可以选择AWS、Google Cloud或Oracle Cloud。

根据技术需求选择

如果需要运行Linux服务器，AWS EC2 或 Oracle Cloud 是理想选择。

如果需要Windows服务器，Azure 更合适。

免费云服务器的限制与注意事项

资源限制

免费云服务器通常只提供1核CPU、1GB内存或更低的配置，不适合高负载应用。

流量和存储限制

大多数免费套餐对带宽和存储有严格限制，超出后可能产生费用。

账户审核与信用卡绑定

部分云服务商（如Google Cloud、Oracle Cloud）要求绑定信用卡，需注意避免意外扣费。

数据安全

免费服务通常不提供高级备份和防护功能，重要数据建议自行备份。

如何最大化利用免费云服务器？

搭建个人网站或博客

使用WordPress、Hexo等工具在免费云服务器上搭建个人博客。

运行开发环境

部署Docker、Node.js、Python等开发环境，进行代码测试。

学习Linux和服务器管理

通过SSH连接免费云服务器，学习Linux命令和服务器运维。

搭建私有云存储

使用NextCloud或OwnCloud搭建私人云盘。

运行自动化脚本

利用免费云服务器运行Python爬虫、定时任务等自动化程序。

结论

免费云服务器是进入云计算世界的绝佳入口，无论是学习、开发还是托管小型项目，都能提供实用的资源。不同云服务商的免费套餐各有优劣，用户应根据自身需求选择最合适的方案。合理利用免费云服务器，不仅能节省成本，还能提升技术能力，为未来的云计算应用打下��实基础。

[lucideternitysarcophagus]

免费云服务器：全面指南与最佳选择

在当今数字化时代，免费云服务器成为许多开发者、初创企业和个人用户的首选。无论是用于学习、测试还是小型项目部署，免费云服务器都能提供强大的计算资源，而无需支付高昂的费用。本文���详细介绍免费云服务器的优势、使用场景、推荐平台以及如何最大化利用这些资源。

1. 什么是免费云服务器？

免费云服务器指的是云计算服务商提供的无需付费即可使用的虚拟服务器资源。这些服务器通常具备基础的CPU、内存和存储配置，适合轻量级应用、开发和测试环境。与付费云服务器相比，免费云服务器可能会有一定的限制，例如使用时长、性能或功能上的限制，但对于初学者和小型项目来说已经足够。

免费云服务器的特点

零成本：无需支付费用即可使用。

基础配置：通常提供1-2核CPU、1-2GB内存和少量存储空间。

有限时长：部分服务商仅提供短期免费试用（如1个月或12个月），而有些则长期免费（如AWS Free Tier）。

适合轻量级应用：适用于个人博客、测试环境、学习编程等场景。

2. 免费云服务器的优势

（1）降低学习与开发成本

对于学生和开发者来说，免费云服务器是学习云计算、部署应用的最佳选择。无需购买实体服务器或支付高昂的云服务费用，即可体验云端计算能力。

（2）灵活性与可扩展性

大多数云服务商允许用户在免费套餐的基础上升级配置，这意味着当项目需求增长时，可以无缝切换到付费计划，而无需迁移数据。

（3）全球部署能力

许多云服务提供商（如AWS、Google Cloud、Azure）在全球拥有数据中心，用户可以选择就近的服务器部署应用，提高访问速度。

（4）支持多种操作系统

免费云服务器通常支持Linux（如Ubuntu、CentOS）和Windows Server，用户可以根据需求选择合适的系统环境。

3. 最佳免费云服务器推荐

以下是目前市场上最受欢迎的免费云服务器提供商：

（1）AWS Free Tier（亚马逊云科技）

免费时长：12个月（部分服务永久免费）。

配置：1核CPU、1GB内存、30GB存储（EC2）。

优势：全球覆盖广，适合企业级应用测试。

适用场景：Web应用、数据库、机器学习实验。

（2）Google Cloud Free Tier（谷歌云）

免费时长：90天试用（300美元免费额度）。

配置：1核CPU、0.6GB内存（f1-micro实例）。

优势：强大的AI和大数据分析工具。

适用场景：数据分析、容器化应用（Kubernetes）。

（3）Microsoft Azure Free Tier（微软云）

免费时长：12个月（部分服务永久免费）。

配置：1核CPU、1GB内存（Linux/Windows VM）。

优势：与微软生态（如Office 365、SQL Server）深度集成。

适用场景：企业级应用、.NET开发。

（4）Oracle Cloud Free Tier（甲骨文云）

免费时长：永久免费（部分资源）。

配置：2核CPU、1GB内存（ARM实例可选）。

优势：高性能计算，适合数据库应用。

适用场景：Oracle数据库、Java应用。

（5）Heroku Free Tier

免费时长：永久免费（有休眠限制）。

配置：512MB内存、免费PostgreSQL数据库。

优势：简单易用，支持一键部署。

适用场景：小型Web应用、API服务。

4. 如何最大化利用免费云服务器？

（1）选择合适的服务商

根据需求选择最适合的免费云服务器，例如：

学习Linux/DevOps：AWS EC2或Google Cloud。

部署小型网站：Heroku或Oracle Cloud。

数据库应用：Oracle Cloud或Azure。

（2）优化资源使用

使用轻量级系统：如Alpine Linux减少资源占用。

启用自动休眠：避免因闲置导致服务停止（如Heroku）。

监控使用量：防止超出免费额度（如AWS Free Tier）。

（3）备份重要数据

免费云服务器可能随时调整政策，建议定期备份数据到本地或其他云存储（如Google Drive、Dropbox）。

（4）结合其他免费服务

CDN加速：Cloudflare免费版。

域名与SSL：Freenom提供免费域名，Let’s Encrypt提供免费SSL证书。

数据库：MongoDB Atlas免费层、Firebase实时数据库。

5. 免费云服务器的局限性

尽管免费云服务器极具吸引力，但也存在一些限制：

性能较低：不适合高流量网站或复杂计算任务。

时间限制：部分服务仅提供短期试用。

功能受限：某些高级功能（如GPU加速）需付费。

数据安全风险：免费服务可能不提供企业级数据保护。

6. 结论：免费云服务器值得尝试吗？

对于个人开发者、学生或初创团队来说，免费云服务器是探索云计算、测试项目的绝佳选择。通过合理利用AWS、Google Cloud、Azure等平台的免费资源，可以大幅降低技术成本。然而，对于商业级应用或高负载需求，建议升级到付费方案以获得更好的性能和稳定性。

如果你正在寻找免费云服务器，不妨从AWS Free Tier或Oracle Cloud开始，体验云端计算的强大功能！

[suzukiinstrumentsseo]

The Importance of Hardness Testing Machines in Ensuring Material Quality

In the world of material science and engineering, ensuring that materials meet the desired mechanical properties is crucial for their performance and longevity. One of the most common and reliable ways to assess the durability of materials is through hardness testing. Hardness is defined as a material’s resistance to deformation, scratching, and indentation. To accurately measure this property, hardness testing machines are employed across various industries to ensure the quality, reliability, and performance of materials. Among the most popular hardness testing machines are the Vickers Hardness Testers, which provide precise and reproducible results. This blog will explore several models of Vickers hardness testers and their role in quality control.

What is Vickers Hardness Testing?

The Vickers hardness test is widely regarded as one of the most versatile and accurate methods for determining the hardness of a material. In this test, a diamond pyramid indenter is pressed into the material’s surface under a specified load. The size of the indentation left on the material is then measured and used to calculate the Vickers Hardness Number (VHN). This method is suitable for testing a wide range of materials, including metals, ceramics, and composites, and can be performed on both large and small specimens.

Hardness testers like the Vickers models offer precision measurements, ensuring that engineers and manufacturers can trust the results when making decisions about material selection and processing.

Types of Vickers Hardness Testers

The Vickers hardness testers come in various models and configurations, each suited to specific testing requirements. Let’s take a closer look at some of the leading models available in the market today.

1. Vickers Hardness Testers Model: VM-50

The VM-50 is an entry-level Vickers hardness tester designed for general-purpose testing in laboratory settings. This model is equipped with a high-quality optical system, allowing users to easily measure the size of the indentation with great accuracy. The VM-50 is widely used in industries where quality control is essential, such as automotive, aerospace, and manufacturing.

2. Computerised Hardness Testers Model: VM 50 PC Vickers

The VM 50 PC Vickers is an upgraded, computerised version of the traditional VM-50 tester. This model features advanced software that automates the measurement process, reducing the risk of human error. The computer interface allows operators to control the testing parameters and monitor the progress of the test in real-time.

3. Computerised Hardness Tester Model: VM-50-TS

The VM-50-TS is a more sophisticated version of the VM series, designed for testing materials with highly specific hardness requirements. This model features a touchscreen interface that simplifies the testing process and makes the machine more intuitive to operate. Additionally, the VM-50-TS is equipped with advanced load and indentation measurement technology that ensures highly accurate and consistent results.

4. Computerised Microvickers Hardness Testers MV-1 PC

The MV-1 PC is a computerised microvickers hardness tester, designed specifically for testing very small and thin materials or coatings. Microvickers hardness tests are often employed for evaluating thin films, coatings, and small components in the electronics and aerospace industries.

5. Computerised Microvickers Hardness Testers MV1-Pro

The MV1-Pro takes the capabilities of the MV-1 PC to the next level by offering advanced features that make it perfect for research and high-precision testing. With an integrated digital camera and powerful software, the MV1-Pro allows operators to view and analyse the indentations on a micro-level, ensuring the most accurate measurements.

Vickers hardness testers play an indispensable role in ensuring the quality and performance of materials across a wide variety of industries. From basic models like the VM-50 to the highly advanced MV1-Pro, these machines offer precise and reliable hardness measurements that are essential for quality control and research. Computerised models, like the VM 50 PC Vickers and MV-1 PC, provide enhanced data management and automation, making them perfect for high-volume testing environments.

As materials continue to evolve and industries demand higher levels of precision and efficiency, investing in the right hardness testing machine is crucial for maintaining the integrity of products. Whether for general-purpose testing or specialized research, Vickers hardness testers remain a cornerstone of material testing and quality assurance.

[ericvanderburg]

Microsoft’s Hyperlight Wasm: Bringing WebAssembly to Secure Micro-VMs

http://securitytc.com/TJp8FG

[infernovm]

Microsoft lauds Hyperlight Wasm for WebAssembly workloads

Microsoft has unveiled Hyperlight Wasm, a virtual machine “micro-guest” that can run WebAssembly component workloads written in a multitude of languages including C and Python. Introduced March 26, Hyperlight Wasm serves as a Rust library crate. Wasm modules and components can be run in a VM-backed sandbox. The purpose of Hyperlight Wasm is to enable applications to safely run untrusted or…

[hawkstack]

Deploying Red Hat Linux on AWS, Azure, and Google Cloud

Red Hat Enterprise Linux (RHEL) is a preferred choice for enterprises looking for a stable, secure, and high-performance Linux distribution in the cloud. Whether you're running applications, managing workloads, or setting up a scalable infrastructure, deploying RHEL on public cloud platforms like AWS, Azure, and Google Cloud offers flexibility and efficiency.

In this guide, we will walk you through the process of deploying RHEL on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Why Deploy Red Hat Linux in the Cloud?

Deploying RHEL on the cloud provides several benefits, including:

Scalability: Easily scale resources based on demand.

Security: Enterprise-grade security with Red Hat’s continuous updates.

Cost-Effectiveness: Pay-as-you-go pricing reduces upfront costs.

High Availability: Cloud providers offer redundancy and failover solutions.

Integration with DevOps: Seamlessly use Red Hat tools like Ansible and OpenShift.

Deploying Red Hat Linux on AWS

Step 1: Subscribe to RHEL on AWS Marketplace

Go to AWS Marketplace and search for "Red Hat Enterprise Linux."

Choose the version that suits your requirements (RHEL 8, RHEL 9, etc.).

Click on "Continue to Subscribe" and accept the terms.

Step 2: Launch an EC2 Instance

Open the AWS Management Console and navigate to EC2 > Instances.

Click Launch Instance and select your subscribed RHEL AMI.

Choose the instance type (e.g., t2.micro for testing, m5.large for production).

Configure networking, security groups, and storage as needed.

Assign an SSH key pair for secure access.

Review and launch the instance.

Step 3: Connect to Your RHEL Instance

Use SSH to connect:ssh -i your-key.pem ec2-user@your-instance-ip

Update your system:sudo yum update -y

Deploying Red Hat Linux on Microsoft Azure

Step 1: Create a Virtual Machine (VM)

Log in to the Azure Portal.

Click on Create a resource > Virtual Machine.

Search for "Red Hat Enterprise Linux" and select the appropriate version.

Click Create and configure the following:

Choose a subscription and resource group.

Select a region.

Choose a VM size (e.g., Standard_B2s for basic use, D-Series for production).

Configure networking and firewall rules.

Step 2: Configure VM Settings and Deploy

Choose authentication type (SSH key is recommended for security).

Configure disk settings and enable monitoring if needed.

Click Review + Create, then click Create to deploy the VM.

Step 3: Connect to Your RHEL VM

Get the public IP from the Azure portal.

SSH into the VM:ssh -i your-key.pem azureuser@your-vm-ip

Run system updates:sudo yum update -y

Deploying Red Hat Linux on Google Cloud (GCP)

Step 1: Create a Virtual Machine Instance

Log in to the Google Cloud Console.

Navigate to Compute Engine > VM Instances.

Click Create Instance and set up the following:

Choose a name and region.

Select a machine type (e.g., e2-medium for small workloads, n1-standard-4 for production).

Under Boot disk, click Change and select Red Hat Enterprise Linux.

Step 2: Configure Firewall and SSH Access

Enable HTTP/HTTPS traffic if needed.

Add your SSH key under Security.

Click Create to launch the instance.

Step 3: Connect to Your RHEL Instance

Use SSH via Google Cloud Console or terminal:gcloud compute ssh --zone your-zone your-instance-name

Run updates and configure your system:sudo yum update -y

Conclusion

Deploying Red Hat Linux on AWS, Azure, and Google Cloud is a seamless process that provides businesses with a powerful, scalable, and secure operating system. By leveraging cloud-native tools, automation, and Red Hat’s enterprise support, you can optimize performance, enhance security, and ensure smooth operations in the cloud.

Are you ready to deploy RHEL in the cloud? Let us know your experiences and any challenges you've faced in the comments below! For more details www.hawkstack.com 

[jcmarchi]

The role of machine learning in enhancing cloud-native container security - AI News

New Post has been published on https://thedigitalinsider.com/the-role-of-machine-learning-in-enhancing-cloud-native-container-security-ai-news/

The role of machine learning in enhancing cloud-native container security - AI News

The advent of more powerful processors in the early 2000’s shipping with support in hardware for virtualisation started the computing revolution that led, in time, to what we now call the cloud. With single hardware instances able to run dozens, if not hundreds of virtual machines concurrently, businesses could offer their users multiple services and applications that would otherwise have been financially impractical, if not impossible.

But virtual machines (VMs) have several downsides. Often, an entire virtualised operating system is overkill for many applications, and although very much more malleable, scalable, and agile than a fleet of bare-metal servers, VMs still require significantly more memory and processing power, and are less agile than the next evolution of this type of technology – containers. In addition to being more easily scaled (up or down, according to demand), containerised applications consist of only the necessary parts of an application and its supporting dependencies. Therefore apps based on micro-services tend to be lighter and more easily configurable.

Virtual machines exhibit the same security issues that affect their bare-metal counterparts, and to some extent, container security issues reflect those of their component parts: a mySQL bug in a specific version of the upstream application will affect containerised versions too. With regards to VMs, bare metal installs, and containers, cybersecurity concerns and activities are very similar. But container deployments and their tooling bring specific security challenges to those charged with running apps and services, whether manually piecing together applications with choice containers, or running in production with orchestration at scale.

Container-specific security risks

Misconfiguration: Complex applications are made up of multiple containers, and misconfiguration – often only a single line in a .yaml file, can grant unnecessary privileges and increase the attack surface. For example, although it’s not trivial for an attacker to gain root access to the host machine from a container, it’s still a too-common practice to run Docker as root, with no user namespace remapping, for example.

Vulnerable container images: In 2022, Sysdig found over 1,600 images identified as malicious in Docker Hub, in addition to many containers stored in the repo with hard-coded cloud credentials, ssh keys, and NPM tokens. The process of pulling images from public registries is opaque, and the convenience of container deployment (plus pressure on developers to produce results, fast) can mean that apps can easily be constructed with inherently insecure, or even malicious components.

Orchestration layers: For larger projects, orchestration tools such as Kubernetes can increase the attack surface, usually due to misconfiguration and high levels of complexity. A 2022 survey from D2iQ found that only 42% of applications running on Kubernetes made it into production – down in part to the difficulty of administering large clusters and a steep learning curve.

According to Ari Weil at Akamai, “Kubernetes is mature, but most companies and developers don’t realise how complex […] it can be until they’re actually at scale.”

Container security with machine learning

The specific challenges of container security can be addressed using machine learning algorithms trained on observing the components of an application when it’s ‘running clean.’ By creating a baseline of normal behaviour, machine learning can identify anomalies that could indicate potential threats from unusual traffic, unauthorised changes to configuration, odd user access patterns, and unexpected system calls.

ML-based container security platforms can scan image repositories and compare each against databases of known vulnerabilities and issues. Scans can be automatically triggered and scheduled, helping prevent the addition of harmful elements during development and in production. Auto-generated audit reports can be tracked against standard benchmarks, or an organisation can set its own security standards – useful in environments where highly-sensitive data is processed.

The connectivity between specialist container security functions and orchestration software means that suspected containers can be isolated or closed immediately, insecure permissions revoked, and user access suspended. With API connections to local firewalls and VPN endpoints, entire environments or subnets can be isolated, or traffic stopped at network borders.

Final word

Machine learning can reduce the risk of data breach in containerised environments by working on several levels. Anomaly detection, asset scanning, and flagging potential misconfiguration are all possible, plus any degree of automated alerting or amelioration are relatively simple to enact.

The transformative possibilities of container-based apps can be approached without the security issues that have stopped some from exploring, developing, and running microservice-based applications. The advantages of cloud-native technologies can be won without compromising existing security standards, even in high-risk sectors.

(Image source)

[learning-code-ficusoft]

Building Scalable Infrastructure with Ansible and Terraform

Building Scalable Infrastructure with Ansible and Terraform

Modern cloud environments require scalable, efficient, and automated infrastructure to meet growing business demands. Terraform and Ansible are two powerful tools that, when combined, enable Infrastructure as Code (IaC) and Configuration Management, allowing teams to build, manage, and scale infrastructure seamlessly.

1. Understanding Terraform and Ansible

📌 Terraform: Infrastructure as Code��(IaaC)

Terraform is a declarative IaaC tool that enables provisioning and managing infrastructure across multiple cloud providers.

🔹 Key Features: ✅ Automates infrastructure deployment. ✅ Supports multiple cloud providers (AWS, Azure, GCP). ✅ Uses HCL (HashiCorp Configuration Language). ✅ Manages infrastructure as immutable code.

🔹 Use Case: Terraform is used to provision infrastructure — such as setting up VMs, networks, and databases — before configuration.

📌 Ansible: Configuration Management & Automation

Ansible is an agentless configuration management tool that automates software installation, updates, and system configurations.

🔹 Key Features: ✅ Uses YAML-based playbooks. ✅ Agentless architecture (SSH/WinRM-based). ✅ Idempotent (ensures same state on repeated runs). ✅ Supports cloud provisioning and app deployment.

🔹 Use Case: Ansible is used after infrastructure provisioning to configure servers, install applications, and manage deployments.

2. Why Use Terraform and Ansible Together?

Using Terraform + Ansible combines the strengths of both tools:

TerraformAnsibleCreates infrastructure (VMs, networks, databases).Configures and manages infrastructure (installing software, security patches). Declarative approach (desired state definition).Procedural approach (step-by-step execution).Handles infrastructure state via a state file. Doesn’t track state; executes tasks directly. Best for provisioning resources in cloud environments. Best for managing configurations and deployments.

Example Workflow: 1️⃣ Terraform provisions cloud infrastructure (e.g., AWS EC2, Azure VMs). 2️⃣ Ansible configures servers (e.g., installs Docker, Nginx, security patches).

3. Building a Scalable Infrastructure: Step-by-Step

Step 1: Define Infrastructure in Terraform

Example Terraform configuration to provision AWS EC2 instances:hclprovider "aws" { region = "us-east-1" }resource "aws_instance" "web" { ami = "ami-12345678" instance_type = "t2.micro" tags = { Name = "WebServer" } }

Step 2: Configure Servers Using Ansible

Example Ansible Playbook to install Nginx on the provisioned servers:yaml- name: Configure Web Server hosts: web_servers become: yes tasks: - name: Install Nginx apt: name: nginx state: present - name: Start Nginx Service service: name: nginx state: started enabled: yes

Step 3: Automate Deployment with Terraform and Ansible

1️⃣ Use Terraform to create infrastructure:bash terraform init terraform apply -auto-approve

2️⃣ Use Ansible to configure servers:bashansible-playbook -i inventory.ini configure_web.yaml

4. Best Practices for Scalable Infrastructure

✅ Modular Infrastructure — Use Terraform modules for reusable infrastructure components. ✅ State Management — Store Terraform state in remote backends (S3, Terraform Cloud) for team collaboration. ✅ Use Dynamic Inventory in Ansible — Fetch Terraform-managed resources dynamically. ✅ Automate CI/CD Pipelines — Integrate Terraform and Ansible with Jenkins, GitHub Actions, or GitLab CI. ✅ Follow Security Best Practices — Use IAM roles, secrets management, and network security groups.

5. Conclusion

By combining Terraform and Ansible, teams can build scalable, automated, and well-managed cloud infrastructure. 

Terraform ensures consistent provisioning across multiple cloud environments, while Ansible simplifies configuration management and application deployment.

WEBSITE: https://www.ficusoft.in/devops-training-in-chennai/