#capacity-planning

RAID存储阵列数学作为API，本地确定性地计算。容量端点计算RAID级别的可用容量和原始容量、存储效率以及容错能力——RAID 0条带化n×disk无冗余，RAID 1镜像到一个磁盘并容忍n−1个故障，RAID 5提供(n−1)×disk并容忍一个磁盘故障，RAID 6提供(n−2)×disk并容忍两个磁盘故障，RAID 10提供(n/2)×disk——并报告每个级别所需的最小磁盘数。比较端点将相同磁盘和磁盘大小的级别并排放置，以便您权衡容量与冗余。重建端点估计在给定重建速度下重建单个磁盘所需的时间，以及RAID 5/6中第二个故障会导致数据丢失的时间窗口。所有计算均在本地确定性地进行，因此即时且私密。非常适合存储、NAS、服务器和IT管理员应用程序开发人员、容量规划和采购工具以及家庭实验室计算器。纯本地计算——无需密钥、无需第三方服务、即时。实时，不存储任何内容。3个端点。这是RAID阵列大小调整；数据传输时间请使用传输API。

api.oanor.com/raid-api

Queueing-theory maths as an API, computed locally and deterministically. The littles-law endpoint applies Little's law, L = λ·W — the average number in a system equals the arrival rate times the average time in the system — and solves for whichever of the three you leave out; it holds for any stable system, from a checkout line to a request pipeline. The mm1 endpoint gives the full steady-state metrics of a single-server M/M/1 queue from the arrival rate λ and the service rate μ: the utilization ρ = λ/μ, the average number in the system and in the queue, the average time in the system and waiting, and the probability the system is empty — and it flags an unstable queue when ρ ≥ 1. The mmc endpoint extends this to a multi-server M/M/c queue with the Erlang-C waiting probability, returning the offered load in erlangs, the per-server utilization, the chance an arrival has to wait, and the same length and time metrics. Rates must share a time unit, and the times come out in that unit. Everything is computed locally and deterministically, so it is instant and private. Ideal for capacity-planning and operations tools, call-centre and staffing apps, server and throughput sizing, and operations-research education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is queueing theory; for descriptive statistics on a list of numbers use a statistics API.

api.oanor.com/queue-api