Cloud Computing Solutions

One of the most important laws of frugal architecture is that you can’t optimize what you can’t measure. I learned this long before cloud computing. Growing up in Amsterdam during the energy crisis of the 1970s, we had things like car-free Sundays and rationed energy, but the detail that always stuck with me was closer to home. Households with their energy meter on the main floor of their homes used significantly less energy than those with it hidden in the basement. The same style of house, in the same city, yet dramatically different behaviour. About as clear of a signal as you can get that seeing data changes what you do with it. For years, in the absence of better sustainability metrics, usage (or consumption) was the best proxy we had. The meter was in the basement. With the AWS Sustainability Console, we bring the meter to your “living room”. It gives your builders direct access to Scope 1, 2, and 3 emissions data, broken down by service and Region, exportable via API, without ever touching sensitive cost and billing data. The right data, to the right people, through the right door. When carbon emission becomes just another metric in your observability stack sitting next to latency, cost, and error rates, it stops being a compliance exercise and starts becoming an architectural discipline. The world we are building in the cloud is the world we are leaving to our children. Measure it like it matters. Read more here: https://lnkd.in/efFjU7hG

📌Turning Waste into Warmth: A Smarter Way Forward 🔁🔥 Finland is transforming how cities use energy by integrating sustainability directly into digital infrastructure. New underground data centers in Helsinki are designed not only to host servers but also to recycle the immense heat they generate. Instead of venting this waste energy, it’s captured and redirected into district heating systems that warm nearby homes and buildings. This closed-loop approach allows the same energy that powers cloud computing to heat thousands of apartments, reducing reliance on fossil fuels and cutting urban carbon emissions dramatically. Data centers, once known for their high energy consumption, are becoming key players in renewable urban ecosystems. This is the kind of circular solution modern facilities must aspire to. By integrating technology, engineering, and smart planning, even high-energy systems like data centres can become contributors to a greener city. For facilities and estates professionals, the message is clear: Sustainability isn’t always about new resources — it’s about using what we already have, better. The project underscores Finland’s leadership in green innovation — turning what was once environmental waste into community benefit. As cities worldwide search for climate solutions, this model shows how technology and sustainability can work hand in hand to reshape the future of energy. A powerful reminder of what’s possible when we rethink infrastructure with efficiency and environmental responsibility at the core. Sources: ✍️TechTimes #GreenEnergy #FinlandInnovation #SustainableCities #DataCenters #CleanTechnology #Infrastructure #Environmental #Technology

Yesterday, Reuters reported that OpenAI finalized a cloud deal with Google in May. This might look like routine tech news. It is not. This is a strategic inflection point in the AI infrastructure wars. OpenAI, whose ChatGPT threatens the core of Google Search, is now paying Google billions of dollars to power its growth. This was not a partnership of choice. It was a partnership of necessity. Since ChatGPT launched in late 2022, OpenAI has struggled to meet soaring demand for computing power. Training and inference workloads have outpaced what Microsoft’s Azure alone can support. OpenAI had to expand. Google Cloud was the solution. For OpenAI, the deal reduces its dependency on Microsoft. For Google, it is a calculated win. Google Cloud generated $43 billion in revenue last year, about 12 percent of Alphabet’s total. By serving a direct competitor, Google is positioning its cloud business as a neutral, high-performance platform for AI at scale. The market responded. Alphabet shares rose 2.1 percent on the news. Microsoft fell 0.6 percent. There are only a handful of true hyperscalers in the U.S. AWS, Azure, and GCP dominate, with Oracle and IBM trailing behind. The appetite for compute is growing faster than any one company can satisfy. In this new phase of the AI era, exclusivity is a luxury no one can afford. Collaboration across competitive lines is inevitable. -s

A year has passed since I last visualized the cloud provider landscape, and the changes are striking.  While each provider's strengths remain consistent, several key trends have reshaped the ecosystem: • 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺:  Organizations are increasingly moving away from single-provider reliance, adopting multi-cloud strategies to optimize spending, avoid vendor lock-in, and leverage best-in-breed services from various platforms. • 𝗚𝗿𝗲𝗲𝗻 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀:  Sustainability is no longer optional.  Major cloud providers are doubling down on renewable energy and providing tools for customers to monitor and reduce their environmental impact. • 𝗔𝗜/𝗠𝗟 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻:  The accessibility of artificial intelligence and machine learning has exploded.  Providers are offering increasingly user-friendly tools, empowering businesses of all sizes to harness the power of AI. • 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴'𝘀 𝗥𝗶𝘀𝗲:  Edge computing is transforming industries. Platforms like Azure Arc, AWS Outposts, and Google Anthos are evolving rapidly, enabling innovation in areas like IoT and real-time data processing. • 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Serverless computing continues its ascent, abstracting away infrastructure complexities and allowing developers to focus on code.  Recent advancements have focused on improved tooling and broader functionality. • 𝗧𝗵𝗲 𝗥𝗲𝗽𝗮𝘁𝗿𝗶𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗲𝗻𝗱: Interestingly, alongside cloud adoption, some companies are also exploring "reverse cloud," moving certain workloads back on-premise.  This often reflects a focus on cost optimization for specific applications or data governance requirements. The ideal cloud solution remains dependent on individual business requirements.  Regularly evaluating your cloud strategy is essential to ensure it aligns with your evolving needs. What significant shifts have you noticed in the cloud landscape lately? I'm interested in hearing your insights.

In true Silicon Valley fashion, the AI arms race is getting down to the silicon itself.🤺 The Big 3 hyperscalers, Amazon, Microsoft, and Google—traditionally NVIDIA’s biggest customers—are encroaching on its core turf by developing their own AI chips. Meanwhile, NVIDIA, the juggernaut of GPUs, is pushing into hyperscaler territory with DGX Cloud, offering AI infrastructure that could, in theory, make it less reliant on Big Tech clouds. Why does this matter? Because the silicon layer is a battleground for billions. 💸 Hyperscalers are tired of footing NVIDIA's massive GPU bill, so they’re investing big in in-house silicon to cut costs and assert control. Amazon’s Inferentia and Trainium chips, Google’s TPUs, and Microsoft’s Maia project are all about building tech stacks with minimal dependency on outside hardware. The goal? Price control and performance tailored to hyperscaler needs. For NVIDIA, this is about strategic survival. Its business model relies on selling chips that empower the same hyperscalers who are now racing to break free. DGX Cloud and partnerships with Oracle, Google, and Microsoft (ironically) are NVIDIA’s way of expanding beyond hardware sales into high-margin, AI-driven cloud services. NVIDIA is doubling down on services, building out a powerful software ecosystem, and offering a soup-to-nuts solution for enterprises wanting AI access without the infrastructure burden. If hyperscalers get their chips right, NVIDIA's dominance could be challenged. But if NVIDIA’s DGX Cloud gains traction, it’s a warning shot that it can play in hyperscaler territory too—and may lure AI workloads directly onto its infrastructure. The stakes have never been higher, so let the chips fall where they may.🌐

Hyperscalers are fighting the cloud wars. Startups are fighting for compute. Our latest business relationship data shows GCP taking 38% of new AI startup relationships, AWS with 30%, Azure with 8%, and… 25% of AI startups taking a multi-cloud approach. In most cases, this isn't a sophisticated infrastructure strategy. It's a response to a fundamental supply constraint: there simply aren't enough GPUs to meet demand. Hyperscaler strategies: Google Cloud is making an aggressive play for startups by embedding Gemini into its solutions, creating native AI builders on GCP. Their recent deal with Cipher signals they're serious about expanding compute capacity to support this strategy. If you're trying to capture more of the value chain by betting on startups building with your LLM, you need to invest in the infrastructure to back it. AWS has scale and the largest startup footprint, but without a proprietary LLM driving demand, they're competing primarily on compute access and availability. When GPU supply is tight everywhere, being “Switzerland” has its advantages. Microsoft largely sits out the early-stage startup battle. With existing enterprise distribution and key partnerships with OpenAI and now Anthropic, they can focus on inference and deployment rather than competing for training workloads. Many early-stage AI startups are locked to a single provider because of startup programs, credit packages, and early partnerships. But, as compute costs scale and availability remains constrained, that 25% multi-cloud number is likely to grow. Companies are increasingly willing to add operational complexity if it means access to the compute they need. Compute remains the bottleneck. Navigating the supply-demand imbalance – through infrastructure investment, partnerships, and strategic positioning – will determine the next 12, 18, 24 months of growth for both startups and cloud providers.

Latency vs Throughput vs Bandwidth One thing that made network troubleshooting easier for me was understanding the difference between these three metrics. They're related, but they measure completely different things. - Latency ↳ Measures the delay between sending and receiving traffic ↳ Typically measured in milliseconds (ms) ↳ Think: user experience, API response times, voice/video quality, AI inference. ↳ If your ping shows 40 ms round-trip, that's latency. - Throughput ↳ Measures how much traffic is successfully delivered across the network ↳ Typically measured in Mbps or Gbps ↳ Think: backups, replication, file transfers, and moving large amounts of data efficiently. ↳ If your download shows 62 Mbps, that’s throughput - Bandwidth ↳ Measures the maximum capacity available on a network link ↳ Typically measured in Mbps or Gbps ↳ Think: network capacity, circuit sizing, cloud connectivity, and handling traffic growth. ↳ If your internet plan is 100 Mbps, that's bandwidth. Troubleshooting tips : Application feels slow? → Check latency first. Large file transfers or backups are taking too long? → Check throughput and packet loss. Are network links running near capacity? → Check bandwidth utilization and congestion. High bandwidth but poor performance? → Investigate latency, routing issues, retransmissions, and packet loss. Performance drops during peak usage? → Look for bandwidth saturation and traffic spikes. Simple tools to start with : - Latency → Ping, Traceroute, MTR - Throughput → iPerf3, Speedtest - Bandwidth Utilization → Interface statistics, monitoring platforms, and cloud-native monitoring tools - Deep Analysis → Wireshark The key is identifying which metric is actually the bottleneck before attempting a fix. What's the first thing you investigate when someone says, "The network is slow"? ........ Follow Chinmay Upasani for more Networking, Cloud, and Security insights.

The next era of datacenters is here. The demand for AI is growing rapidly, and with it comes the need to grow the cloud’s physical footprint. Historically, datacenters have been water-intensive and require using large amounts of higher carbon materials like steel. At Microsoft, we're building datacenters with sustainability in mind, and we're constantly innovating to find new ways to reduce our environmental impact. This includes: 🤝 A first-of-its-kind agreement with Stegra, backed by an investment from Microsoft’s Climate Innovation Fund (CIF) in 2024, to procure near zero-emissions steel from Stegra’s new plant in Boden, Sweden, for use in our datacenters. Powered by renewable energy and green hydrogen, Stegra's facility reduces CO2 emissions by up to 95% versus conventional steel production. By committing to purchase this green steel before it rolls off the line, Microsoft is sending a clear market signal, driving demand for cleaner materials and supporting Stegra’s growth. 💧 We also announced a major breakthrough to make our datacenters more sustainable: microfluidic in-chip cooling technology. Unlike traditional cold plates that sit atop chips, microfluidics brings cooling right inside the silicon itself. Engineers carve microscopic channels directly into the chip, letting liquid coolant flow through and absorb heat exactly where it’s generated. This approach is up to three times more effective than current methods. More efficient cooling allows datacenters to support powerful next-gen AI chips without ramping up energy use or investing in costly new gear. 💵 Through our CIF investments, we’ve catalyzed billions in follow-on capital for breakthrough solutions in low-carbon materials, sustainable fuels, carbon removal, and more. We just released a new whitepaper – Building Markets for Sustainable Growth – that distills five key lessons on how catalytic investment and partnership can move markets and accelerate a global transition in energy, waste, water, and ecosystems. Our journey toward sustainable datacenters is only beginning, and we recognize true progress requires collective action and investment. Read more from Building Markets for Sustainable Growth: https://msft.it/6041sq9xD

3 weeks back while I was working on a Kubernetes project, saw a very common bad practice, i.e. image size is 8 GB which could have been just 436 MB (Multi-stage Build)!!!! After working on such numerous projects, here I have made a list of best practices for containers: Use Minimal Base Images: Start with minimal base images like Alpine Linux or Distroless to reduce the attack surface and improve container startup times. Single Concern per Container: Each container should have only one responsibility. If an application has multiple components (e.g., web server, database etc), they should be split into separate containers and managed as separate services. Stateless Applications: Design your applications to be stateless as much as possible. This allows Kubernetes to easily scale, restart, or replace containers without losing data. Liveness and Readiness Probes: Use liveness probes to let Kubernetes know when to restart a container and readiness probes to know when a container is ready to start accepting traffic. Resource Limits: Set resource requests and limits for CPU and memory to ensure that the container gets its required resources and doesn't consume more than it should. Security: Run containers with a non-root user. Use network policies to control communication between pods. Regularly scan container images for vulnerabilities. Use Kubernetes RBAC (Role-Based Access Control) to limit permissions. Immutable Containers: Avoid making changes to running containers. Instead, create a new container image and deploy it. This ensures consistency across environments. Use Labels and Annotations: Use labels for organizing and selecting groups of resources. Annotations can be used to store additional metadata. Configurations and Secrets: Use ConfigMaps for non-sensitive configuration data and Secrets for sensitive data. Avoid hardcoding configurations in the container image. Logging and Monitoring: Ensure that your applications log to the standard output and standard error streams. This allows Kubernetes to handle and redirect the logs appropriately. Integrate with monitoring tools like Prometheus to keep an eye on the health and performance of your containers. Regularly Update and Patch: Regularly update your container images to include security patches and updates. Use image scanning tools to identify and fix vulnerabilities. Graceful Shutdown: Ensure that your applications handle the SIGTERM signal and shut down gracefully. This allows them to finish processing current requests and release resources before shutting down. Avoid Using latest Tag: Be explicit with container image tags. Avoid using the latest tag as it can lead to unpredictable deployments. Storage Considerations: If your application needs persistent storage, use Persistent Volumes (PV) and Persistent Volume Claims (PVC) in Kubernetes. Ensure that the storage solution you choose is compatible with the dynamic nature of containerized deployments. Follow Sandip Das for more!

🌐 Demystifying Network Protocols: A Quick Guide! 📊 Network protocols function as the main pillars that enable communication between devices over a network. Understanding major networking protocols is important for IT professionals. However, there are a lot to remember, in this piece we'll break down some of the most important ones. 🔌 TCP/IP (Transmission Control Protocol/Internet Protocol) This protocol is the underlying method of how information is passed between devices on the internet. While IP is responsible for addressing and routing data packets, TCP takes care of assembling the data into packets, as well as reliable delivery. 🌐 HTTP (Hypertext Transfer Protocol) When accessing websites, HTTP plays a crucial role. It's responsible for fetching and delivering web content from servers to end-users. 🔐 HTTPS (Hypertext Transfer Protocol Secure) An enhanced version of HTTP, HTTPS integrates security protocols (namely TLS) to encrypt data, ensuring a secure and confidential exchange between browsers and websites. 📂 FTP (File Transfer Protocol) As the name suggests, FTP is used for transferring files (uploading and downloading) between computers on a network. 📧 UDP (User Datagram Protocol) A more streamlined counterpart to TCP, UDP transmits data without the overhead of establishing a connection, leading to faster transmission but without the guarantee that the data will be delivered or in order. 📬 SMTP (Simple Mail Transfer Protocol) The driving force behind email communication, SMTP manages the formatting, routing, and delivery of emails between mail servers. 🔒 SSH (Secure Shell) Secure Shell is a cryptographic network protocol that ensures safe data transmission over an unsecured network. It provides a safe channel, making sure that hackers can't interpret the information by eavesdropping. 🚀 Understanding these protocols is crucial for anyone in the IT and networking field. They are the building blocks of the internet and digital communication. 💬 I'd love to hear your thoughts. Are there any other protocols or concepts you'd like to add to this list?