It’s so refreshing to find someone who understands the impact of latency on application performance, and develops a methodology that considers latency when migrating a workload into a public cloud: Adding latency: one step, two step, oops by Lawrence Jones.
Pulumi is an Infrastructure as Code (IaC) tool similar to Terraform, except that, rather than defining resources in a DSL, you defined them in a supported programming language. This means you have access to all of your chosen languages features as well as any supporting libraries...continue reading
This post was originally published in the Packet Pushers’ Human Infrastructure newsletter on September 1, 2022. You can subscribe to the newsletter for free here. Over a year ago, my wife and I moved from the eastern United States to Seoul, South Korea. A great opportunity presented itself for her career, and my US-based employer was […]
The post Lessons Learned Working Abroad appeared first on Packet Pushers.
Before founding software-defined networking startup PlumGrid and then moving to VMware when it bought his company in 2016, Pere Monclus spent almost 12 years with Cisco Systems at a time when while much of enterprise networking was still in the corporate datacenter, the shift to network virtualization and the migration to the cloud were getting underway. …
The Network Binds The Increasingly Distributed Datacenter was written by Jeffrey Burt at The Next Platform.
In a couple of weeks I’m taking the opportunity to broaden my leadership horizons by attending the BSA leadership course known as Philmont Leadership Challenge. It’s a course that builds on a lot of the things that I’ve been learning and teaching for the past five years. It’s designed to be a sort of capstone for servant leadership and learning how to inspire others. I’m excited to be a part of it in large part because I get to participate for a change.
Being a member of the staff for my local council Wood Badge courses has given me a great opportunity to learn the material inside and out. I love being able to teach and see others grow into leaders. It’s also inspired me to share some of those lessons here to help others in the IT community that might not have the chance to attend a course like that. However the past 3 years have also shown me the value of being a beginner at something from time to time.
Everyone is new at something. No one is born knowing every piece of information they’ll need to know for their entire lives. We learn language and Continue reading
On today’s episode of Heavy Networking, we discuss secure wireless planning and design with Jennifer "JJ Minella. JJ is the author of the book "Wireless Security Architecture." We talk about the goals for planning a wireless design, why it's worth the upfront investment, keeping operators in mind as you design your deployment, the importance of communication, design iteration, and more.
The post Heavy Networking 645: Secure Wireless Planning And Design appeared first on Packet Pushers.
This is an adapted transcript of a talk we gave at Monitorama 2022. You can find the slides with presenter’s notes here and video here.
When a request at Cloudflare throws an error, information gets logged in our requests_error pipeline. The error logs are used to help troubleshoot customer-specific or network-wide issues.
We, Site Reliability Engineers (SREs), manage the logging platform. We have been running Elasticsearch clusters for many years and during these years, the log volume has increased drastically. With the log volume increase, we started facing a few issues. Slow query performance and high resource consumption to list a few. We aimed to improve the log consumer's experience by improving query performance and providing cost-effective solutions for storing logs. This blog post discusses challenges with logging pipelines and how we designed the new architecture to make it faster and cost-efficient.
Before we dive into challenges in maintaining the logging pipelines, let us look at the characteristics of logs.
Unpredictable - In today's world, where there are tons of microservices, the amount of logs a centralized logging system will receive is very unpredictable. There are various reasons why capacity estimation of log volume is so difficult. Continue reading
Welcome to Technology Short Take #159! If you’re interested in finding some links to articles around the web on topics like WASM, Git, Sigstore, or EKS—among other things—then you’ve come to the right place. I’ve spent the last few weeks collecting articles I think you’ll find useful, gleaning them from the depths of Twitter, RSS feeds, Reddit, and Slack. Enjoy, and never stop learning!
After completing the discussion of basic Kubernetes networking with a typical inter-pod traffic scenario, Stuart Charlton tackled another confusing topic: an overview of what Kubernetes services are.
After completing the discussion of basic Kubernetes networking with a typical inter-pod traffic scenario, Stuart Charlton tackled another confusing topic: an overview of what Kubernetes services are.
I have always found TCP congestion control algorithms fascinating, and at the same time, I know very little about them. So once in a while, I will spend some time with the hope of gaining some new insights. This blog post will share some of my experiments with various TCP congestion control algorithms. We will start with TCP Reno, then look at Cubic and ends with BBR.I am using Linux network namespaces to emulate topology for experimentation, making it easier to run than setting up a physical test bed.
For many years, the main algorithm of congestion control was TCP Reno. The goal of congestion control is to determine
how much capacity is available in the network, so that source knows how
many packets it can safely have in transit (Inflight). Once a source has these packets in transit, it uses the ACK’s
arrival as a signal that packets are leaving the network, and therefore it’s safe to send more packets into the network.
By using ACKs for pacing the transmission of packets, TCP is self-clocking. The number of packets which
TCP can inject into the network is controlled by Congestion Window(cwnd).
Congestion Window:
China is the world’s second largest economy, it has the world’s largest population, and it is only a matter of time before has a world-class technology ecosystem spanning the smallest transistors to the largest hyperscale and HPC systems. …
GPU Engines Are So Strategic China Will Have To Use Its Own was written by Timothy Prickett Morgan at The Next Platform.