Skip to main content

High Availability Percentage Calculation



Availability %
Downtime per year
Downtime per month
Downtime per week
Downtime per day
90% ("one nine")
36.5 days
72 hours
16.8 hours
2.4 hours
95% ("one and a half nines")
18.25 days
36 hours
8.4 hours
1.2 hours
97%
10.96 days
21.6 hours
5.04 hours
43.2 minutes
98%
7.30 days
14.4 hours
3.36 hours
28.8 minutes
99% ("two nines")
3.65 days
7.20 hours
1.68 hours
14.4 minutes
99.5% ("two and a half nines")
1.83 days
3.60 hours
50.4 minutes
7.2 minutes
99.80%
17.52 hours
86.23 minutes
20.16 minutes
2.88 minutes
99.9% ("three nines")
8.76 hours
43.8 minutes
10.1 minutes
1.44 minutes
99.95% ("three and a half nines")
4.38 hours
21.56 minutes
5.04 minutes
43.2 seconds
99.99% ("four nines")
52.56 minutes
4.38 minutes
1.01 minutes
8.64 seconds
99.995% ("four and a half nines")
26.28 minutes
2.16 minutes
30.24 seconds
4.32 seconds
99.999% ("five nines")
5.26 minutes
25.9 seconds
6.05 seconds
864.3 milliseconds
99.9999% ("six nines")
31.5 seconds
2.59 seconds
604.8 milliseconds
86.4 milliseconds
99.99999% ("seven nines")
3.15 seconds
262.97 milliseconds
60.48 milliseconds
8.64 milliseconds
99.999999% ("eight nines")
315.569 milliseconds
26.297 milliseconds
6.048 milliseconds
0.864 milliseconds
99.9999999% ("nine nines")
31.5569 milliseconds
2.6297 milliseconds
0.6048 milliseconds
0.0864 milliseconds
Labels:

Comments

Post a Comment

Popular posts from this blog

What's replicated, what's not?

Logged operations are replicated. These include, but are not limited to: DDL DML Create/alter table space Create/alter storage group Create/alter buffer pool XML data. Logged LOBs Not logged operations are not replicated. These include, but are not limited to: Database configuration parameters (this allows primary and standby databases to be configured differently). "Not logged initially" tables Not logged LOBs UDF (User Defined Function) libraries. UDF DDL is replicated. But the libraries used by UDF (such as C or Java libraries)  are not replicated, because they are not stored in the database. Users must manually copy the libraries to the standby. Note: You can use database configuration parameter  BLOCKNONLOGGED  to block not logged operations on the primary.
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more