# Load Balancing Published articles for Load Balancing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## How Load Balancers Actually Distribute Traffic DevFeed: [How Load Balancers Actually Distribute Traffic]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-09-09T12:17:03Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Load Balancing](), [load balancing algorithms](), [systems](), [Server]() Tags: [capacity](), [load-balancing](), [load-balancing-algorithms](), [nginx](), [server]() ### AI overview This tutorial explains how load balancers distribute requests across multiple servers using algorithms such as round robin and weighted round robin. It describes their simplicity, health-check behavior, and limitations when servers differ in capacity or handle long-running requests. ### Source excerpt When your application runs on multiple servers, you need a way to distribute incoming requests across them. ## Understanding Load Balancing for Beginners DevFeed: [Understanding Load Balancing for Beginners]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-08-17T17:00:34Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [backends](), [networking](), [Algorithms]() Tags: [backend](), [beginners](), [cloud](), [connection-draining](), [devops](), [failover](), [health-checks](), [high-availability](), [kubernetes](), [layer-4-vs-layer-7](), [least-connections](), [load-balancer](), [load-balancer-for-beginners](), [load-balancing](), [load-balancing-algorithms](), [networking](), [nginx](), [round-robin](), [session-affinity](), [sre](), [sticky-sessions](), [thundering-herd](), [traffic-management]() ### AI overview This beginner-oriented article explains load balancing as both traffic distribution and backend failure detection. It compares round robin and least-connections algorithms, distinguishes Layer 4 from Layer 7 routing, and discusses health checks, failover, draining, sticky sessions, and the risks of overly aggressive checks. ### Source excerpt Spreading traffic is the easy half of load balancing. The half that decides whether an outage lasts ten seconds or ten minutes is noticing that a backend has stopped working, and most health checks cannot. ## Is MCP Stateless or Stateful? DevFeed: [Is MCP Stateless or Stateful?]() Original publisher: [Read original article]() Author: Roger Perkin Published: 2026-08-14T06:51:31Z Content type: article Language: en Sources: [Roger Perkin Network Automation Consultant]() Topics: [Model Context Protocol (MCP)](), [Load Balancing]() Tags: [2026](), [architecture](), [faq](), [load-balancing](), [mcp](), [protocol](), [stateful](), [stateless]() ### AI overview The article states that MCP became stateless in version 2026-07-28, whereas the original release was stateful. It says requests are self-describing and independent, eliminating the need for connection handshakes, session IDs, and sticky load balancing. ### Source excerpt As of version 2026-07-28 the MCP protocol is now stateless. The original release was stateful. Every request is self-describing and independent, removing the need for connection handshakes, session IDs, or sticky load balancing. You can read all the important updates here: https://modelcontextprotocol.io/docs/2026-07-28/learn/architecture ## HTTP QUERY: the method that was missing between GET and POST DevFeed: [HTTP QUERY: the method that was missing between GET and POST]() Original publisher: [Read original article]() Author: Matteo Baccan Published: 2026-07-29T12:26:29Z Content type: article Language: en Sources: [Backend Job: skill, salary and insights - Codemotion Magazine]() Topics: [HTTP](), [Back end](), [Web](), [Security](), [Caching]() Tags: [backend](), [caching](), [firewall](), [http](), [http-query](), [ietf](), [load-balancing](), [post](), [security](), [siem](), [web]() ### AI overview This article examines the proposed HTTP QUERY method, which is intended to combine the safety and idempotency of GET with the ability to carry an extended request body like POST. It discusses the limitations and security risks of putting complex or sensitive queries in URLs, and considers the method's effects on caching, infrastructure compatibility, and future backend implementation. ### Source excerpt For nearly thirty years, the Web has lived with a semantic paradox that anyone developing for the backend knows well: how do you execute a complex, voluminous query, or one containing sensitive data, if the only "safe" method available does not allow a request body? Think about when we need to send a structured search... Read more The post HTTP QUERY: the method that was missing between GET and POST appeared first on Codemotion Magazine. ## Top System Design Performance Metrics DevFeed: [Top System Design Performance Metrics]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-14T08:36:41Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [benchmarking](), [Availability](), [Architecture & Design](), [Scalability](), [Load Balancing](), [health checks](), [Concurrency](), [Disaster Recovery](), [Database](), [sharding](), [IO](), [API]() Tags: [api](), [availability](), [blocking](), [concurrency](), [database](), [disaster-recovery](), [health-checks](), [load-balancing](), [performance](), [performance-metrics](), [scalability](), [sharding](), [system-design]() ### AI overview A tutorial on system design performance metrics, focusing on availability and throughput. It explains how these metrics are measured and outlines techniques such as load balancing, health checks, failover, redundancy, disaster recovery, query optimization, sharding, and asynchronous processing. ### Source excerpt Must Know Metrics ## Isovalent Enterprise Platform 26.05: Bringing Kubernetes, VMs, and Load Balancing Into One Operating Model DevFeed: [Isovalent Enterprise Platform 26.05: Bringing Kubernetes, VMs, and Load Balancing Into One Operating Model]() Original publisher: [Read original article]() Author: Dean Lewis Published: 2026-07-10T08:53:24Z Content type: release Language: en Sources: [Isovalent - The latest articles covering eBPF-based Networking, Observability, and Security]() Topics: [Kubernetes](), [Load Balancing]() Tags: [isovalent](), [kubernetes](), [load-balancing](), [platform]() ### AI overview The article presents Isovalent Enterprise Platform 26.05 and describes an operating model that brings Kubernetes, virtual machines, and load balancing together. ### Source excerpt Learn all about Isovalent Enterprise Platform 26.05 ## RFC 10005: BGP Community for link capacity DevFeed: [RFC 10005: BGP Community for link capacity]() Original publisher: [Read original article]() Author: j2sw Published: 2026-07-01T13:31:00Z Content type: article Language: en Sources: [Justin Wilson (j2sw)]() Topics: [BGP](), [standard](), [Load Balancing](), [Network]() Tags: [bgp](), [capacity](), [internet](), [load-balancing](), [network](), [network-operations](), [rfc](), [rfc-10005](), [route](), [routers](), [routing](), [standard](), [traffic]() ### AI overview RFC 10005 defines a BGP extended community for carrying link bandwidth information in routes. When multiple BGP paths are eligible, routers can use the value to weight traffic according to link capacity, while normal best-path policy still determines route eligibility. The RFC is currently a draft. ### Source excerpt RFC 10005 defines a BGP extended community that lets a router attach link bandwidth information to a route. Another router can use that value when it spreads traffic across multiple BGP paths. RFC 10005 matters because links are not always of the same capacity. This RFC provides routers with a standard way to carry bandwidth ... Read more The post RFC 10005: BGP Community for link capacity appeared first on Justin Wilson (j2sw). ## How I Chose the Best Managed Network Switch for My Home Lab DevFeed: [How I Chose the Best Managed Network Switch for My Home Lab]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-06-29T07:25:47Z Content type: opinion Language: en Sources: [Hayden James]() Topics: [Homelab](), [Network](), [Firewall](), [Ubiquiti]() Tags: [blog](), [cybersecurity](), [failover](), [firewall](), [home-lab](), [homelab](), [linux](), [load-balancing](), [network](), [sysadmins](), [ubiquiti]() ### AI overview A home-lab network upgrade motivates the author's search for a managed switch with 10G SFP+ uplinks and at least 2.5G LAN ports. The article discusses the author's existing Ubiquiti and Peplink setup, network-security goals, and future expansion plans. A later update says the author chose the TP-Link SG2210XMP-M2, which had run for a year without reported issues. ### Source excerpt Update (one year later): The verdict is in. I went with the TP-Link SG2210XMP-M2, and after a full year in the rack it's been running flawlessly: fanless, multigig, VLANs and not a single issue. Continue reading... ## AI gateways: why and how DevFeed: [AI gateways: why and how]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-31T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Amazon API Gateway](), [LLMs](), [Artificial Intelligence](), [Routing (disambiguation)](), [Claude](), [Security](), [litellm](), [Caching](), [Scalability](), [Load Balancing](), [observability]() Tags: [ai](), [ai-gateway](), [anthropic](), [api-gateway](), [backend](), [caching](), [claude](), [claude-code](), [coding-assistant](), [decoupling](), [devstral](), [gateway](), [litellm](), [llm](), [llms](), [load-balancing](), [mistral](), [observability](), [open-source](), [routing](), [scalability](), [security](), [technical]() ### AI overview This article explains how AI gateways apply the API gateway pattern to AI clients and LLM backends. It discusses using a gateway to centralize model and provider management, governance, cost control, routing, caching, failover, load balancing, and observability, and considers routing Claude Code requests to another provider. ### Source excerpt Before working for 2 years on the Apache APISIX API gateway, I was mainly oblivious to API gateways. It's only by working with them that I understood their value. Decoupling the client and the server unlocks a lot of options: moving authentication to the API Gateway, securing APIs, deduplicating API requests, etc. In this post, I want to describe how the same pattern applies to AI. AI gateways AI gateways work in a similar way. ## A year of open collaboration: Celebrating the anniversary of A2A DevFeed: [A year of open collaboration: Celebrating the anniversary of A2A]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-04-16T20:55:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [A2A protocol](), [Artificial Intelligence](), [Google](), [interoperability](), [Open Source](), [linux foundation](), [Security](), [Load Balancing]() Tags: [a2a](), [a2a-protocol](), [a2april](), [agent2agent](), [agents](), [ai](), [ai-agents](), [amazon](), [amazon-web-services](), [cisco](), [cryptographic](), [interoperability](), [linux-foundation](), [load-balancing](), [microsoft](), [open-source](), [sap](), [security](), [specifications](), [standard]() ### AI overview Google reflects on the first year of the Agent2Agent (A2A) protocol, including its donation to the Linux Foundation, the growth of its supporting ecosystem, and the release of A2A Protocol v1.0. The article highlights security, enterprise architecture, and interoperability improvements for multi-agent workflows. ### Source excerpt by Patricia Cruz, Google Open Source One year ago, on April 9th, 2025 Google announced the Agent2Agent(A2A) protocol. We saw the need for a "common language" that allows AI agents built on different frameworks to collaborate well across diverse systems. Then, on June 23, 2025 at the Open Source Summit North America in Denver, Mike Smith stood on stage to share a pivotal moment for the future of AI interoperability when Google officially donated the A2A protocol to the Linux Foundation, establishing it as a vendor-neutral, community-governed standard. This move was driven by a core belief: for AI agents to truly transform how we work and live, they must be able to communicate across framework boundaries and organizational silos without being locked into a single provider's ecosystem. By placing A2A under the neutral stewardship of the Linux Foundation, we opened the doors for the entire industry to build, contribute, and innovate together. A Foundation of Partners The formation of the A2A Project was made possible through the support of our founding members, including Amazon Web Services, Cisco, Microsoft, Salesforce, SAP, and ServiceNow. Over the past twelve months, this coalition has grown, with over 100 technology companies now supporting the project. From Prototype to Production The momentum since the donation has been remarkable. What began as a Google-led initiative has evolved into critical infrastructure for horizontal, peer-to-peer collaboration. Just one month ago, in March, the project reached a major milestone with the release of A2A Protocol v1.0, the first stable, fully production-ready version of the standard. Key achievements from the community this year include: Enhanced Security: The implementation of Signed Agent Cards for cryptographic identity verification, ensuring trust in multi-agent workflows. Web-Aligned Architecture: Refined specifications that support familiar load-balancing and security patterns for enterprise-scale deployments. Ecosystem ## Load Balancing and Scaling LLM Serving DevFeed: [Load Balancing and Scaling LLM Serving]() Original publisher: [Read original article]() Author: Mohammad Ashar Khan Published: 2026-04-15T19:03:31Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Load Balancing](), [Caching](), [Large Language Model](), [Routing (disambiguation)](), [Latency](), [sglang](), [vllm](), [Concurrency](), [TensorRT]() Tags: [caching](), [concurrency](), [efficiency](), [engineering](), [http](), [inference](), [latency](), [llm](), [load-balancing](), [performance](), [round-robin](), [sglang](), [tensorrt](), [vllm]() ### AI overview This article explains why scaling LLM serving requires load-balancing strategies that account for prompt-prefix caching. It describes how naive round-robin routing reduces cache-hit probability as the number of replicas grows, and introduces cache-aware routing approaches intended to preserve cache efficiency, control costs, and improve performance. It also discusses inference engines including vLLM, SGLang, and TensorRT, along with prefill and decode processing. ### Source excerpt Load balancing for LLMs is fundamentally different from load balancing for traditional services like web servers, APIs, or databases. Prompt caching is the reason. Prompt caching typically cuts input token costs by 50-90% and can reduce Time to First Token (TTFT) latency by up to 80%, but those gains assume your request lands on the replica that already has the relevant prefix cached. Under naive round-robin load balancing across N replicas, that probability is 1/N. The cache hit rate that made caching so attractive at one replica degrades almost linearly as your fleet grows. Solving this requires rethinking how requests are routed at the infrastructure level. This article covers the load balancing strategies and specialized routers that preserve cache efficiency at scale, starting with why standard approaches fall short and progressing to precise, cache-aware routing techniques. Inferencing engines To achieve large-scale inferencing, we use inference engines. These engines simplify the complexities of serving LLMs and offer improved resource utilization on the underlying GPUs. They also enable higher concurrency and allow for customization to suit diverse inference workloads, such as real-time chat completions and long-form document summarization. Noteworthy engine options include vLLM, SGLang, and TensorRT. The inferencing process is largely consistent across different engines. Sending an HTTP request to an engine initiates a standard sequence of steps. Prefill Phase: The input prompt is first converted into token IDs using the model's tokenizer. Requests are grouped into batches for efficient concurrent processing by the engine. During this initial processing, special Key (K) and Value (V) tensors are computed. This phase concludes after the first forward pass, resulting in the generation of the first output token. Decode Phase: This phase involves an auto-regressive loop, continuing until an end-of-sequence token is generated or the maximum sequence length is re ## Advanced Prompt Caching at Scale DevFeed: [Advanced Prompt Caching at Scale]() Original publisher: [Read original article]() Author: Andrew Dugan Published: 2026-04-07T19:11:40Z Content type: tutorial Language: en Sources: [DigitalOcean]() Topics: [Caching](), [Inference Performance](), [Large Language Model](), [Inference](), [Latency](), [Load Balancing](), [round robin](), [sglang](), [TensorRT-LLM](), [vllm](), [model architecture]() Tags: [ai-ml](), [architecture](), [caching](), [decoding](), [efficiency](), [engineering](), [inference](), [latency](), [llm](), [load-balancing](), [prompt](), [round-robin](), [sglang](), [tensorrt-llm](), [token](), [tokens](), [vllm]() ### AI overview This tutorial explains how prompt caching works across multiple LLM replicas. It describes how round-robin load balancing reduces cache-hit rates and presents session affinity, tiered routing, and prefix-aware load balancing as architectural strategies for preserving KV-cache reuse while reducing latency and inference costs. ### Source excerpt Introduction Prompt caching is the process of reusing already computed KV states across inference requests in order to save money and reduce latency. Within a single replica, modern inference engines like vLLM, SGLang, and TensorRT-LLM handle it automatically. Incoming prompts are matched against cached prefixes and recomputed only where necessary, without requiring user configurations The problem nobody talks about is what happens when you scale to many replicas. Under round-robin load balancing, a request with an identical prefix has only a 1/N chance of hitting the replica where that prefix is already cached. The cache hit rate that made prompt caching so attractive at one replica degrades almost linearly as your fleet grows, unless you architect around it deliberately. Done right, prompt caching at scale offers 50-90% discounts on cached input tokens and can reduce time-to-first-token (TTFT) latency by up to 80%. This article covers the architectural strategies that make that possible. The Single-Replica Ceiling Refer to our previous prompt caching article for a detailed explanation of how KV caching works under the hood. Every transformer-based LLM uses KV caching to store key and value vectors from the attention layers in GPU VRAM during decoding. This intra-request caching is baked into the model architecture to increase throughput and maximize efficiency. Within a single replica, modern open-source engines like vLLM, SGLang (via RadixAttention), and TensorRT-LLM support automatic prefix caching out of the box, matching incoming prompts against previously cached prefixes to maximize KV reuse without any user configuration. Reusing KV states across requests from many users and replicas is where inference frameworks differ significantly. In the simplest architecture, the cache lives on individual replicas in VRAM. It is not shared across model instances at all. When a user makes an inference request, the prompt from their request is cached on a single replica. ## Operating Trino at Scale With Trino Gateway DevFeed: [Operating Trino at Scale With Trino Gateway]() Original publisher: [Read original article]() Author: Prakhar Sapre Published: 2026-03-24T12:01:00Z Content type: article Language: en Sources: [Expedia]() Topics: [gateway](), [Load Balancing](), [Routing (disambiguation)](), [data analytics](), [Authentication](), [SQL]() Tags: [analytics](), [authentication](), [clusters](), [concurrency](), [data-science](), [downtime](), [gateway](), [load-balancer](), [load-balancing](), [open-source](), [sql](), [trino](), [trino-gateway](), [trinos]() ### AI overview This article explains how Trino Gateway routes queries across multiple Trino clusters and centralizes routing, authentication, load balancing, monitoring, and cluster management. It describes the project's origins as Presto Gateway at Lyft and its role in supporting larger analytics platforms with more complex workloads and higher concurrency. ### Source excerpt Expedia Group Technology -- DataWorkload-aware routing for TrinoPhoto by Joseph Barrientos on Unsplash Trino -- a fork of PrestoSQL -- is a powerful tool in modern data analytics, enabling organizations to query large datasets quickly and efficiently. As a distributed SQL query engine, Trino provides fast, scalable insights without requiring data relocation. While Trino is robust on its own, its capabilities are further enhanced when paired with a Gateway, which introduces features such as query routing, strong security, and streamlined cluster management. A brief overview The Gateway project originated at Lyft as Presto Gateway, serving as a proxy and load balancer for PrestoDB. It was later forked and integrated into the Trino ecosystem, with contributions from various organizations and the open-source community. The Gateway serves as a central point for managing and routing queries, providing a unified interface for users and administrators. As organizations scale their analytics platforms, they often encounter challenges such as increased query complexity, higher concurrency, and the need for specialized cluster configurations. Directing users to specific cluster endpoints becomes impractical as the user base grows. A Gateway addresses these challenges by routing queries to the most appropriate clusters based on workload, improving efficiency and responsiveness. The Gateway acts as a vital intermediary between users and the Trino query engine. By abstracting the complexities of distributed query execution, it manages critical functions such as routing, authentication, and load balancing across diverse backend clusters. This ensures that queries are efficiently directed to the optimal processing cluster. With an intuitive user interface, the Gateway transforms what was once a convoluted process into a manageable and transparent experience empowering administrators with real-time insights and precise control over their backend cluster infrastructure. Whether it's mon ## NVIDIA Dynamo 1.0 Is Available to DigitalOcean Customers for Inference Performance and Cost Efficiency DevFeed: [NVIDIA Dynamo 1.0 Is Available to DigitalOcean Customers for Inference Performance and Cost Efficiency]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2026-03-19T22:13:37Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Dynamo](), [Digital Ocean](), [Inference Performance](), [Artificial Intelligence](), [Generative AI](), [Inference](), [Cloud](), [GB200](), [sglang](), [TensorRT-LLM](), [vllm](), [Kubernetes]() Tags: [ai-ml](), [cloud](), [cost](), [deployment](), [digitalocean](), [dynamo](), [gb200](), [inference](), [inference-performance](), [kubernetes](), [latency](), [llm](), [load-balancing](), [nvidia](), [product-updates](), [routing](), [sglang](), [tensorrt-llm](), [vllm]() ### AI overview NVIDIA Dynamo 1.0 is now available to DigitalOcean customers as an inference service framework for large-scale generative AI workloads. The article describes claimed performance improvements on NVIDIA GB200 NVL systems, cost-efficiency benefits, deployment options, and features including GPU-aware routing and disaggregated serving. ### Source excerpt NVIDIA Dynamo 1.0, which was released on Monday at NVIDIA GTC, is now available to DigitalOcean customers to help drive performance enhancements and cost efficiency. NVIDIA Dynamo 1.0 offers a 7x inference performance increase on NVIDIA GB200 NVL systems, and by pairing it with DigitalOcean's Agentic Inference Cloud, customers can achieve higher performance at lower costs while benefiting from seamless deployment. Working together, DigitalOcean's optimizations with NVIDIA have already achieved a 67% cost savings for customers like Workato, and this new generation of Dynamo can unlock even greater gains for businesses who run production-grade agentic workflows. DigitalOcean customers can get access to NVIDIA Dynamo 1.0 as a container image that can be run on a Droplet or can deploy directly on DigitalOcean Kubernetes with an inference runtime (vLLM, SGlang, TensorRT). What is NVIDIA Dynamo 1.0? NVIDIA Dynamo is a cutting-edge, high-performance inference service framework specifically designed to accelerate and optimize large-scale generative AI and inference models. Dynamo is an orchestration layer that sits above engines like vLLM, SGLang, and NVIDIA TensorRT-LLM. Think of it as the distributed traffic controller for your GPU fleet, seamlessly orchestrating GPU and memory resources across a cluster and reducing bottleneck by intelligently routing requests Key technical breakthroughs offered by Dynamo 1.0 include: 7x Performance Boost: When paired with NVIDIA Blackwell Ultra GPUs, Dynamo can increase inference performance by up to 7x, significantly lowering your cost per token. KV-Aware Routing: Instead of simple round-robin load balancing, Dynamo routes requests to the specific GPUs that already have the relevant "memory" from previous turns of a conversation. Disaggregated Serving: Dynamo splits the "prefill" (reading the prompt) and "decode" (generating the answer) phases across different GPUs to maximize utilization and reduce latency. Memory Offloading: The KV B ## Use Additional BGP Paths for IBGP Load Balancing DevFeed: [Use Additional BGP Paths for IBGP Load Balancing]() Original publisher: [Read original article]() Published: 2025-09-19T06:01:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [BGP](), [Networks]() Tags: [bgp](), [load-balancing](), [netlab](), [networks]() ### AI overview This lab exercise demonstrates how BGP Additional Paths can be used for IBGP load balancing in networks that use BGP route reflectors. It can be started in a browser with GitHub Codespaces or run on user-provided lab infrastructure. ### Source excerpt I wrote about the optimal BGP path selection with BGP additional paths in 2021, and I probably mentioned (in one of the 360 BGP-related blog posts) that you need it to implement IBGP load balancing in networks using BGP route reflectors. If you want to try that out, check out the IBGP Load Balancing with BGP Additional Paths lab exercise. Click here to start the lab in your browser using GitHub Codespaces (or set up your own lab infrastructure). After starting the lab environment, change the directory to lb/4-ibgp-add-path and execute netlab up. ## Comparing IP and CLNP: Reaching Off-Subnet Nodes DevFeed: [Comparing IP and CLNP: Reaching Off-Subnet Nodes]() Original publisher: [Read original article]() Published: 2025-03-05T07:08:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [TCP/IP](), [networking](), [DHCP](), [Load Balancing]() Tags: [cisco](), [dhcp](), [ip](), [ipv4](), [ipv6](), [load-balancing](), [networking-fundamentals](), [routing]() ### AI overview This article compares how TCP/IP and CLNP identify a first-hop router for traffic to off-subnet nodes. It describes IPv4 default routes, DHCP configuration, first-hop redundancy protocols, and CLNP Intermediate System Hellos. ### Source excerpt The previous blog post in this series discussed how TCP/IP and CLNP reach adjacent nodes and build ARP/ND/ES caches. Now let's move one step further: how do nodes running IPv4/IPv6 or CLNP discover the first-hop router that could forward their traffic to off-subnet nodes they want to communicate with? Read more ... ## Worker Pooling -- 5x performance for CPU-intensive JavaScript workloads DevFeed: [Worker Pooling -- 5x performance for CPU-intensive JavaScript workloads]() Original publisher: [Read original article]() Author: André Eriksson Published: 2024-12-13T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [JavaScript](), [Concurrent Programming](), [Scalability]() Tags: [javascript](), [launch](), [load-balancing](), [multithreading](), [performance](), [process](), [processes](), [scalability]() ### AI overview Encore.ts introduces Worker Pooling mode for CPU-intensive JavaScript workloads. It combines its multi-threaded Rust runtime with multiple Node.js worker threads to distribute HTTP requests and Pub/Sub messages across event loops, with reported improvements of up to 5x in tested applications. ### Source excerpt Launch Week Day 5 ## Why Dropbox Built the Robinhood Load Balancer DevFeed: [Why Dropbox Built the Robinhood Load Balancer]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2024-12-11T14:01:03Z Content type: article Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Cloud](), [Algorithms](), [cloud-infrastructure](), [servers](), [real-time]() Tags: [cloud](), [dropbox](), [hardware](), [load-balancing](), [network](), [real-time](), [round-robin](), [server](), [servers]() ### AI overview The article explains how Dropbox handled overloaded virtual servers with different hardware capabilities. Its traditional fixed-weight round-robin approach could not distribute load evenly, so the team built Robinhood, a dynamic weighted round-robin load balancer with gRPC support. ### Source excerpt Dropbox saved resources by creating a superior version of a tool everyone uses ## Kubernetes networking: service, kube-proxy, load balancing DevFeed: [Kubernetes networking: service, kube-proxy, load balancing]() Original publisher: [Read original article]() Author: Gulcan Topcu Published: 2024-10-21T00:00:00Z Content type: tutorial Language: en Sources: [Learnk8s]() Topics: [Kubernetes](), [networking](), [Load Balancing]() Tags: [api](), [applications](), [backend](), [browser](), [clusters](), [deployment](), [http](), [kubernetes](), [kubernetes-networking](), [load-balancing](), [networking](), [proxy](), [services]() ### AI overview A tutorial on Kubernetes networking that explains Services, kube-proxy, and load balancing. It covers pod communication, ClusterIP, NodePort, and LoadBalancer services, along with traffic from external sources and implementation details using iptables. ### Source excerpt Master Kubernetes networking with Services and load balancing. Learn how traffic flows within clusters and from external sources. ## IBGP Load Balancing with BGP Link Bandwidth DevFeed: [IBGP Load Balancing with BGP Link Bandwidth]() Original publisher: [Read original article]() Published: 2024-09-23T05:13:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [BGP](), [Network]() Tags: [bgp](), [load-balancing](), [netlab](), [network]() ### AI overview A lab exercise explains how to use the BGP Link Bandwidth attribute on IBGP sessions to perform unequal-cost load balancing within a network. ### Source excerpt In the previous BGP load balancing lab exercise, I described the BGP Link Bandwidth attribute and how you can use it on EBGP sessions. This lab moves the unequal-cost load balancing into your network; we'll use the BGP Link Bandwidth attribute on IBGP sessions. ## Supabase Python DevFeed: [Supabase Python]() Original publisher: [Read original article]() Author: Guilherme Souza Published: 2024-08-16T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Python](), [ai and ml]() Tags: [ecosystem](), [feature](), [http](), [latency](), [libraries](), [load-balancing](), [python]() ### AI overview Supabase announces official support for its Python client libraries after increased adoption, particularly among AI and machine-learning users. The article also describes default HTTP/2, redirect-following, and keep-alive improvements. ### Source excerpt Supabase Python is now officially supported in Supabase. ## EBGP Load Balancing with BGP Link Bandwidth DevFeed: [EBGP Load Balancing with BGP Link Bandwidth]() Original publisher: [Read original article]() Published: 2024-07-16T05:04:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [BGP]() Tags: [bgp](), [load-balancing](), [netlab]() ### AI overview A lab exercise demonstrates how to use the BGP Link Bandwidth extended community to distribute traffic across multiple links in an unequal ratio, building on earlier EBGP equal-cost load balancing concepts. ### Source excerpt The first BGP load balancing lab exercise described the basics of EBGP equal-cost load balancing. Now for the fun part: what if you want to spread traffic across multiple links in an unequal ratio? There's a nerd knob for that: the BGP Link Bandwidth extended community that you can test-drive in this lab exercise. ## BGP Labs: a Year Later DevFeed: [BGP Labs: a Year Later]() Original publisher: [Read original article]() Published: 2024-07-08T04:57:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [BGP](), [networking](), [Routing (disambiguation)](), [cloud-infrastructure](), [Open Source]() Tags: [bgp](), [infrastructure](), [load-balancing](), [netlab](), [open-source](), [routing]() ### AI overview The article reviews progress on a project to revive and expand BGP lab exercises. After completing the original exercises, the author added deployment exercises, routing policy exercises, load-balancing exercises, and challenges covering advanced BGP scenarios. The labs are free and can use various network operating systems, but users may need to build supporting infrastructure. ### Source excerpt Last summer, I started a long-term project to revive the BGP labs I created in the mid-1990s. I completed the original lab exercises (BGP sessions, IBGP, local preference, MED, communities) in late 2023 but then kept going. This is how far I got in a year: Twenty-six deploy BGP exercises, including advanced settings like AS path manipulations, MD5 passwords and BFD, and new technologies like TCP/AO and interface EBGP sessions. Fifteen BGP routing policies exercises, covering the basic mechanisms as well as dirty tricks like route disaggregation Four load balancing exercises, from EBGP ECMP to BGP Link Bandwidth and BGP Additional Paths. Five challenges for everyone who got bored doing the simple stuff ;) That completes the BGP technologies I wanted to cover. I'll keep adding the challenge labs and advanced scenarios. Here are some ideas; if you have others, please leave a comment. Read more ... [Next page]()