# Load Balancing Load balancing is the distribution of network traffic across multiple servers or other application-supporting resources to improve availability, scalability, security, and performance. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Planning for disaster recovery using AWS Local Zones and AWS Outposts racks DevFeed: [Planning for disaster recovery using AWS Local Zones and AWS Outposts racks]() Original publisher: [Read original article]() Author: Brianna Rosentrater Published: 2026-09-17T17:24:21Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Disaster Recovery](), [AWS Local Zones](), [AWS Outposts](), [Replication](), [Load Balancing](), [Availability]() Tags: [advanced-300](), [availability](), [aws](), [aws-local-zones](), [aws-outposts](), [aws-outposts-rack](), [database](), [disaster-recovery](), [failover](), [hybrid](), [hybrid-cloud](), [infrastructure](), [latency](), [load-balancing](), [replication](), [technical-how-to]() ### AI overview This post presents disaster recovery architectures using AWS Outposts racks and AWS Local Zones, including DNS-based failover, active/active load balancing, and hybrid database replication. It explains their availability, RTO/RPO, complexity, cost, and data-residency trade-offs. ### Source excerpt Build highly available architectures that span two AWS Outposts racks, or an Outpost rack and an AWS Local Zone, without single points of failure. This post covers three disaster recovery approaches: DNS-based failover, active/active load balancing, and hybrid database replication, with the RTO/RPO trade-offs of each. ## 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. ## 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 ## 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). ## Key Components of a Production Web Application DevFeed: [Key Components of a Production Web Application]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-06-09T07:56:22Z Content type: article Language: en Sources: [System Design Codex]() Topics: [web applications](), [CI/CD](), [Load Balancing](), [proxy](), [Caching]() Tags: [cache](), [caching](), [ci-cd](), [cloudflare](), [ddos](), [haproxy](), [web]() ### AI overview This article provides a high-level overview of components used in production web applications, including CI/CD pipelines, DNS resolution, load balancers, reverse proxies, and content delivery networks. It explains their roles in deployment, availability, scalability, performance, and security. ### Source excerpt A big picture view... ## 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. ## 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 ## 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 ... ## 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. ## Load balancing and scaling long-lived connections in Kubernetes DevFeed: [Load balancing and scaling long-lived connections in Kubernetes]() Original publisher: [Read original article]() Author: Daniele Polencic Published: 2024-06-10T00:00:00Z Content type: tutorial Language: en Sources: [Learnk8s]() Topics: [Kubernetes](), [Load Balancing](), [gRPC](), [client](), [HTTP](), [Database]() Tags: [http](), [kubernetes](), [kubernetes-services](), [load-balancer](), [load-balancing](), [round-robin]() ### AI overview This tutorial explains why Kubernetes Services may not evenly distribute requests over long-lived connections. It describes Kubernetes Services and Deployments and discusses client-side load balancing or proxies for HTTP/2, gRPC, RSocket, AMQP, and long-lived database connections. ### Source excerpt Handle long-lived connections in Kubernetes. Master client-side load balancing for HTTP/2, gRPC, and database connections. ## What is X-Forwarded-For and when can you trust it? DevFeed: [What is X-Forwarded-For and when can you trust it?]() Original publisher: [Read original article]() Author: HTTP Toolkit; Phil Sturgeon Published: 2024-01-31T17:00:00Z Content type: tutorial Language: en Sources: [HTTP Toolkit]() Topics: [HTTP](), [client](), [servers](), [backends](), [Load Balancing](), [Authentication]() Tags: [apis](), [authentication](), [backend](), [http](), [ipv4](), [ipv6](), [load-balancer](), [proxy](), [request](), [server](), [servers](), [standards]() ### AI overview This article explains how the X-Forwarded-For HTTP header records the source IP addresses of clients across proxies, CDNs, load balancers, and other intermediaries. It also describes common uses such as authentication checks, traffic distribution, data localization, and geographic content delivery. ### Source excerpt The X-Forwarded-For (XFF) HTTP header provides crucial insight into the origin of web requests. The header works as a mechanism for conveying the original source IP addresses of clients, and not just across one hop, but through chains of multiple intermediaries. This list of IPv4 and IPv6 addresses is helpful to understand where requests have really come from in scenarios where they traverse several servers, proxies, or load balancers. A typical HTTP request goes on a bit of a journey, traversing multiple layers of infrastructure before reaching its destination. Without the X-Forwarded-For header, the receiving server would only see the IP address of the last intermediary in the chain (the direct source of the request) rather than the true client origin. {/* sequenceDiagram actor Client as Client 28.178.124.142 participant CDN as CDN 198.40.10.101 participant LB as Load Balancer 198.40.10.102 participant B as Backend App 10.0.0.1 Client->>CDN: CDN->>LB: LB->>B: */} In thie example, by the time the backend application is seeing an incoming request, the IP address of the original client is long forgotten. This is where the X-Forwarded-For header can help out. It looks like this: X-Forwarded-For: 28.178.124.142, 198.40.10.101 The goal here is to give a proxy the chance to say "Alright hang on, I'm forwarding you a request, and this is the history of where it came from, as far as I know". Note that the last proxy will not add its own IP address to the list, because that's already available: if the receiver of the request cares about who is calling it directly, they can combine the X-Forwarded-For with the request's source IP address from the incoming connection, e.g: req.connection.remoteAddress in NodeJS. In this example above, the load balancer has said "Hey backend app, I am forwarding you a request that came from this client, via the CDN", and it doesn't need to pop its own IP in there because the backend app can already tell if it's coming from the load balancer or ## Random Load Balancing is Unevenly Distributed DevFeed: [Random Load Balancing is Unevenly Distributed]() Original publisher: [Read original article]() Published: 2023-08-29T13:13:23Z Content type: article Language: en Sources: [Evan Jones]() Topics: [Load Balancing](), [distributed-systems](), [Simulation](), [Server]() Tags: [capacity](), [distributed](), [load-balancing](), [simulation]() ### AI overview Randomly distributing work across servers creates load imbalance because the most-loaded server, rather than the average server, determines required capacity. A simulation illustrates how this can waste capacity and cause worse-than-linear scaling. ### Source excerpt This is a reminder that random load balancing is unevenly distributed. If we distribute a set of items randomly across a set of servers (e.g. by hashing, or by randomly selecting a server), the average number of items on each server is num_items / num_servers. It is easy to assume each server has close to the same number of items. However, since we are selecting servers at random, they will have different numbers of items, and the imbalance can be important. For load balancing, a reasonable model is that each server has fixed capacity (e.g. it can serve 3000 requests/second, or store 100 items, etc.). We need to divide the total workload over the servers, so that each server stays below its capacity. This means the number of servers is determined by the most loaded server, not the average. This is a classic balls in bins problem that has been well studied, and there are some interesting theoretical results. However, I wanted some specific numbers, so I wrote a small simulation. The summary is that the imbalance varies with the expected number of items per server (that is, num_items / num_servers). A workload is more balanced with more items or with fewer servers. Most interestingly, this means that scaling a system by adding more servers makes the distribution more unfair. This is one reason we can get worse than linear scaling of some distributed systems. Let's make this more concrete with an example. Let's assume we have a workload of 1000 items, and each server can hold a maximum of 100 items. If we place the exact same number of items on each server, we only need 10 servers, and each of them is completely busy. However, if we place the items randomly, then the median (p50) number of items is 100 items. This means half the servers will have more than 100 items, and will be overloaded. If we want less than a 1% chance of an overloaded server, we need to look at the 99th percentile (p99) server load. We need to use at least 13 servers, which has a p99 load of 97 it ## Teleport 13: New Features and Updates DevFeed: [Teleport 13: New Features and Updates]() Original publisher: [Read original article]() Author: kenneth.dumez@goteleport.com (Kenneth DuMez) Published: 2023-05-10T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [configuration](), [Load Balancing](), [Amazon Web Services](), [Kubernetes](), [okta](), [opensearch](), [TLS (Transport Layer Security)](), [Web]() Tags: [announcement](), [aws](), [configuration](), [kubernetes](), [load-balancing](), [new-features](), [okta](), [opensearch](), [tls](), [ui]() ### AI overview Teleport 13 introduces automatic agent updates with configurable maintenance windows, TLS routing through AWS application load balancers for Server Access and Kubernetes Access, Okta application and group imports, and AWS OpenSearch support for Database Access. Several features are marked as preview releases. ### Source excerpt An overview of all of the new features added to Teleport 13. Automatic Updating, Light Theme and more! ## TLS Routing Support for Teleport Behind an AWS Application Load Balancer DevFeed: [TLS Routing Support for Teleport Behind an AWS Application Load Balancer]() Original publisher: [Read original article]() Author: info@goteleport.com (Steve Huang) Published: 2022-11-18T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Load Balancing](), [TLS (Transport Layer Security)](), [Amazon Web Services](), [networking](), [HTTP](), [Amazon Bedrock]() Tags: [aws](), [high-availability](), [https](), [load-balancer](), [load-balancing](), [tls](), [tls-handshake]() ### AI overview This article explains TLS Routing support for Teleport Database Access when Teleport runs behind an AWS Application Load Balancer. It compares application and network load balancers and discusses TLS termination, mutual TLS, ALPN, SNI, and certificate handling. ### Source excerpt How to "penetrate" an AWS Application Load Balancer to enable mutual TLS (mTLS), Application-Layer Protocol Negotiation (ALPN), and more. ## Caddy Partnership With Light Code Labs DevFeed: [Caddy Partnership With Light Code Labs]() Original publisher: [Read original article]() Published: 2019-07-22T00:00:00Z Content type: release Language: en Sources: [William Kennedy]() Topics: [Load Balancing](), [Go Language](), [Open Source](), [Security](), [Software]() Tags: [announce](), [ardan-labs](), [blog](), [go](), [go-programming](), [golang](), [ingress](), [load-balancing](), [open-source](), [partnership](), [programming](), [security]() ### AI overview Ardan Labs announces a partnership with Light Code Labs and Caddy author Matt Holt to bring Caddy into Ardan Labs' products and services. Caddy is a Go-based, open-source ingress and load-balancing service with automated security features and plugin support. ### Source excerpt Ardan Labs (a leader in building Go enterprise software) is excited to announce that we have partnered with Light Code Labs and Matt Holt to bring Caddy into the Ardan Labs family of products and services. Caddy is a lightweight ingress service written in Go that provides automated security features and a plugin environment for extensibility. With the service being free open source (FOSS), small and large Go engineering teams finally have the ability to own and maintain their ingress and load balancing software with ease. We believe the Caddy project is as important as Docker and Kubernetes in maintaining a solid and scalable production environment. ## The speed of BGP network propagation DevFeed: [The speed of BGP network propagation]() Original publisher: [Read original article]() Author: ben@benjojo.co.uk Published: 2019-05-04T12:28:41Z Content type: article Language: en Sources: [benjojo blog]() Topics: [BGP](), [Network](), [Load Balancing](), [Latency](), [incident](), [Routing (disambiguation)](), [configuration]() Tags: [bgp](), [configuration](), [incident](), [latency](), [load-balancing](), [network](), [routing](), [traffic-engineering]() ### AI overview This article explains how BGP anycast routing propagates across regions and affects traffic distribution, latency, and load balancing. It describes the challenge of keeping routing announcements consistent and recounts an incident in which a configuration mistake removed a traffic-engineering prepend, shifting global traffic toward an overloaded site. ### Source excerpt The speed of BGP network propagation > ## 即刻多端实时通信实践 DevFeed: [即刻多端实时通信实践]() Original publisher: [Read original article]() Author: Joway Published: 2018-09-18T00:00:00Z Content type: article Language: zh Sources: [Random Thoughts]() Topics: [Socket.IO](), [WebSocket](), [Redis](), [Load Balancing](), [Back end](), [Publish-subscribe pattern]() Tags: [backend](), [channel](), [clients](), [cluster](), [load-balancing](), [nodejs](), [redis](), [room](), [socket](), [tech](), [websocket]() ### AI overview This Chinese technical article explains how Jike's jike-io real-time communication infrastructure is built with socket.io. It describes server-to-client WebSocket notifications, per-user rooms, HTTP requests for business operations, socket.io's fallback and reconnection behavior, and Redis-based distribution across nodes, including sticky load balancing and the associated message-broadcast overhead. It also discusses connection spikes and Node.js bottlenecks. ### Source excerpt 背景 jike-io 是即刻基于 socket.io 构建的一个实时通信基础设施。目前客户端上的所有实时通信服务都是建立在其基础上，涵盖了私信、消息通知、用户反馈、活动页小游戏等诸多组件。 在目前即刻的实时通信设计里，我们的实时通信只是为了让服务端主动推送消息给客户端，客户端不会主动通过 websocket 发送消息。由于几乎我们所有需要发送消息的请求都会有一定业务逻辑在，而这个业务逻辑我们并不希望 websocket 连接层(jike-io)去处理，所以我们仍旧采用传统 HTTP 请求的方式去发送请求。至于之后是否需要推送消息给用户，由服务端调用 jike-io 的接口进行实现。 ## Customer Invisible Maintenance of the CSC Object Storage Servers DevFeed: [Customer Invisible Maintenance of the CSC Object Storage Servers]() Original publisher: [Read original article]() Author: Unknown (noreply@blogger.com) Published: 2018-03-14T07:36:00Z Content type: article Language: en Sources: [CSC - IT Center For Science - Cloud Team]() Topics: [BGP](), [networking](), [Load Balancing](), [Availability](), [round robin]() Tags: [bgp](), [ceph](), [cpouta](), [dns](), [exabgp](), [failover](), [ha](), [high-availability](), [load-balancing](), [maintenance](), [network](), [networking](), [object-storage](), [radosgw](), [round-robin](), [routing](), [server]() ### AI overview The CSC Pouta Cloud Team describes using ExaBGP on object storage servers to combine round-robin DNS load balancing with BGP routing for high availability and failover during maintenance. ### Source excerpt Hello! It's the CSC Pouta Cloud Team again! You may recognize us from other object storage films such as: http://pouta.blog.csc.fi/2018/02/admin-stories-implement-object-storage.html where we go through in excruciating detail how we configured CEPH RadosGW and why we configured it like we did http://pouta.blog.csc.fi/2018/01/objectively-speaking-in-cpouta.html where we announced object storage! In this episode we're going to talk a bit about how we implemented invisible(*) maintenance for our Object Storage customers. Configuration of ExaBGP with Object Storage CSC - IT Center for Science Ltd. has a close collaboration with the Finnish University and Research Network - FUNET, and because of that we are in a great position to do some fun network stuff. Actually, as long as your network has a BGP relationship with its internet provider you can also do what we have done. When we first launched Object Storage we just had a DNS entry pointing to two IP addresses. This gave us some load balancing and if we really wanted to we could take a node out of the DNS and do maintenance. But we wanted more! *ExaBGP enters the stage* In short we installed ExaBGP on our object storage linux servers, configured one dummy network interface per server and started to announce two IP addresses from each server. Then we pointed object.pouta.csc.fi DNS entry to these two IP addresses. The objective was to use round-robin DNS for load-balancing and BGP routing for High Availability. For failover this means that if one node goes down, the other IP is reachable on the other node and stuff will keep magically(*) working! There are some other BGP softwares out there, like BIRD or Quagga. Despite this, we never actually tried any other software since ExaBG has some great qualities such as: it came recommended by the network team at CSC who uses it for some services its low resource footprint and small number of software dependencies it is not a full routing suite - it is meant to be used for basi ## PostgreSQL High Availability with Primary and Standby Servers DevFeed: [PostgreSQL High Availability with Primary and Standby Servers]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2017-12-11T09:19:24Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [Replication](), [Availability](), [postgresql clusters](), [Database](), [Load Balancing]() Tags: [availability](), [high-availability](), [load-balancing](), [postgresql](), [primary](), [replication](), [standby]() ### AI overview This article explains PostgreSQL high-availability architectures using primary and standby servers. It discusses replication, online backups, automated recovery, and promoting a standby server when the primary becomes unavailable. ### Source excerpt The PostgreSQL community made the explicit choice some times ago that they would not use the infamous master and slave terminology. Instead, the documentation introduces the concepts of High Availability, Load Balancing, and Replication with the terms Primary and Standby, and the even more generic term Replica is used in contexts when only the data flow is considered, rather than the particular role of a node. ## Migrating To Kubernetes DevFeed: [Migrating To Kubernetes]() Original publisher: [Read original article]() Author: sasha@goteleport.com (Sasha Klizhentas) Published: 2017-12-02T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Kubernetes](), [migration](), [Deployment](), [Containers](), [Scalability](), [Availability](), [Load Balancing]() Tags: [availability](), [containers](), [deployment](), [failover](), [high-availability](), [how-to](), [kubernetes](), [load-balancing](), [migration](), [scalability]() ### AI overview This tutorial explains common challenges in migrating existing applications to Kubernetes, including high availability, failover, load balancing, performance, and scalability. It highlights application-specific requirements such as avoiding unsuitable local state, handling database access, and assessing whether an application is ready to run across multiple instances. ### Source excerpt Learn about common problems when migrating your application to Kubernetes. [Next page]()