--- title: "Latency optimizations on the system level" url: https://daily.dev/posts/latency-optimizations-on-the-system-level-97qv2gkll source_url: https://itnext.io/latency-optimizations-on-the-system-level-40dd45ddf2db type: article source: "ITNEXT" published: 2026-08-24T12:45:40.527Z updated: 2026-08-24T12:46:06.776Z tags: ["career", "distributed-systems"] reading_time: 7 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Latency optimizations on the system level **[ITNEXT](https://daily.dev/sources/itnext)** · 7 min read · 0 upvotes · 0 comments ## Summary A catalog of system-level architectural techniques for reducing latency, organized into categories: shortcutting (early responses, bypassing main systems, omitting components), co-locating components to reduce network hops, preloading/preprocessing data (caching, CQRS, event sourcing, materialized views), combining these approaches (FPGA-encoded trading rules, ambassador plugins), non-blocking patterns (proactors, sharding, actor systems), and miscellaneous techniques like request hedging and shared memory. The piece links each technique to established architectural patterns like microkernel, hexagonal architecture, service mesh, and space-based architecture. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is request hedging and how does it improve tail latency? Request hedging sends an incoming request to multiple copies of a system in parallel and accepts whichever response arrives first. This approach greatly improves tail latency and overall system stability by avoiding delays caused by a single slow or unresponsive instance, at the cost of extra load from duplicate processing. _Explore more distributed systems patterns like this on daily.dev when designing resilient low-latency services._ ### How does co-locating actors with related workloads reduce latency in distributed microkernel systems? Distributed microkernels invest in co-location optimizations by moving actors between hosts so that intensely interacting actors run on the same host, eliminating network hops between them. This is similar to AUTOSAR's preference for running an application and the services it depends on on the same chip, minimizing communication overhead. _Follow daily.dev for deeper dives into actor-based architecture and co-location strategies._ ### How do high frequency trading systems achieve near-instantaneous response times? High frequency trading systems encode simple precomputed trading rules directly into an FPGA chip or programmable network card, removing the delay caused by the host operating system entirely. This results in nearly instantaneous trading decisions when an incoming price notification matches one of the preprogrammed rules, bypassing typical software processing layers. _daily.dev surfaces architecture deep dives for engineers optimizing systems at the hardware level._ ## Similar posts on daily.dev - [Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs](https://daily.dev/posts/engineering-speed-at-scale-architectural-lessons-from-sub-100-ms-apis-jlkbvacta) · InfoQ · 0 upvotes · 0 comments --- Tags: [#career](https://daily.dev/tags/career), [#distributed-systems](https://daily.dev/tags/distributed-systems) [View this post on daily.dev](https://daily.dev/posts/latency-optimizations-on-the-system-level-97qv2gkll)