In atusy/pingAnalysis: Analyze Ping Log Files

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

pingAnalysis

Analyze ping log.

Installation

Requires R >= 4.1.

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("atusy/pingAnalysis")

Examples

• Load the package with:

library(pingAnalysis)

• Prepare CSV file with three columns without header:
  • timestamp with YMDhms-style
  • IPv4 address with prefix-size
  • Ping timing. - indicates timeout.

20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.2/24,1
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.2/24,-

1. Treat all ping timeout as server being out of service.

Prepare data

log_csv <- tempfile(fileext = ".csv")
log_df <- tibble::tibble(
  timestamp = 20200101000000 + seq(5),
  address = "192.168.1.1/24",
  ping = c("1", "-", "1", "-", "-")
)
readr::write_csv(log_df, log_csv, col_names = FALSE)

View data

cat(readr::read_lines(log_csv), sep = "\n")

Analyze

measure_all_timeout(read_log(log_csv))

2. Treat ping timeout as server being out of service if timeout continues N-times or more.

Data inherits from the previous example.

Analyze

• N = 1L is equivalent to measure_all_timeout()

measure_timeout(read_log(log_csv), N = 1L)

• N = 2L

measure_timeout(read_log(log_csv), N = 2L)

3. Treat ping result as server being overloaded if rolling mean of ping is greater than or equal to threshold.

Prepare data

basetime <- 20200101000000
pings <- c(
  0, 11, 0, 11, 12, 0, 11, 12, 13,
  0, 11, 0, 11, 12, 0, 11, NA, 13
) |> as.character() |> dplyr::na_if("0") |> dplyr::coalesce("-")
N <- length(pings) / 2L
log_df <- tibble::tibble(
  timestamp = rep(basetime + seq(N), 2L),
  address = paste0("192.168.1.", 1L:2L, "/24") |> rep(each = N),
  ping = pings
)
readr::write_csv(log_df, log_csv, col_names = FALSE)

View data

cat(readr::read_lines(log_csv), sep = "\n")

Analyze

Read log

log_df <- read_log(log_csv)

Analyze varying parameters:

• m: window size for rolling mean
• t: threshold of ping
• N: counts of continuous errors

measure_overload(log_df, m = 1L, t = 10, N = 1L)
measure_overload(log_df, m = 2L, t = 10, N = 1L)
measure_overload(log_df, m = 1L, t = 10, N = 2L)

4. Treat ping result as subnet, or network-switch, being out-of-order if all servers fail to response for N-times or more.

Prepare data

basetime <- 20200101000000
pings <- c(
  # 1  2  3  4  5  6  7  8  9 10
  1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
  1, 1, 0, 0, 0, 0, 1, 1, 0, 1,
  1, 1, 1, 0, 0, 0, 0, 1, 0, 1
)
N <- length(pings) / 3L
log_df <- tibble::tibble(
  timestamp = rep(basetime + seq(N), 3L),
  address = paste0("192.168.1.", 1L:3L, "/24") |> rep(each = N),
  ping = ifelse(pings == 0, "-", as.character(pings))
)

log_df |>
  dplyr::mutate(
    address = stringr::str_replace(address, "^192\\.168\\.1\\.", "192.168.2.")
  ) |>
  dplyr::bind_rows(log_df) |>
  readr::write_csv(log_csv, col_names = FALSE)

View data

cat(readr::read_lines(log_csv), sep = "\n")

Analyze

Read log.

log_df <- read_log(log_csv)

Assume log contains all the required IP addresses.

address_all <- unique(log_df$address)
address_all

Do the analysis varying N.

measure_subnet_timeout(log_df, N = 1L, address_all = address_all)
measure_subnet_timeout(log_df, N = 2L, address_all = address_all)

atusy/pingAnalysis documentation built on Jan. 3, 2022, 12:47 p.m.