Carregamento dos dados

MRT_1F <-c(517.1468515630205, 85.13094142168089, 30.333207896694553, 
           12.694776264558937, 3.3041601673945418, 1.1823111717498882, 
           1.1892293502386786)

MRT_3F <-c(156.68929936163462, 11.540837783562276, 0.4512835621696538, 
           0.4509797929766453, 0.4502068233039181, 0.4496185276300172, 
           0.4543157082191288)

MRT_5F <-c(83.90319666471157, 0.3068151086494968, 0.30522314133037304, 
           0.3072588968084928, 0.30655265997285697, 0.3055812715727718, 
           0.3053297166713006)

MRT_10F <-c(29.55430642951759, 0.19832832665772515, 0.1971923924717474, 
            0.19796648905716516, 0.19615594370806338, 0.2034569237883263, 
            0.19617420889447737)

MRT_15F <-c(11.317736530583566, 0.167364215666193, 0.16172168266811013, 
            0.16701085329580515, 0.1598052657153692, 0.1645934043532696, 
            0.16216563797118075)

MRT_sem_F <-c(11.93430909937736, 0.6095414637034009, 0.6060645101029295, 
              0.612167181646899, 0.6146761002685637, 0.6096747087200697, 
              0.6125810476877268)

clock <- c(0.1, 0.5, 1, 1.5, 2, 2.5, 3)

Gráfico 1

pchs = c(4, 11, 9, 2, 5, 4)
colors = c("black", "yellow", "red", "blue", "purple", "green")
legends = c("1 Fog", "3 Fogs", "5 Fogs", "10 Fogs", "15 Fogs", "w/o Fog")

plot(clock, MRT_1F, pch=pchs[1], type="o",
     col=colors[1], xlab="Time between Things requests (seconds)", 
     ylab="Response Time (sec.)",
     xlim=c(0, 3.0), ylim=c(0, 550))

lines(clock, MRT_3F, type="o", col=colors[2], pch=pchs[2])
lines(clock, MRT_5F, type="o", col=colors[3], pch=pchs[3])
lines(clock, MRT_10F, type="o", col=colors[4], pch=pchs[4])
lines(clock, MRT_15F, type="o", col=colors[5], pch=pchs[5])
lines(clock, MRT_sem_F, type="o", col=colors[6], pch=pchs[6])
legend("topright", pch=pchs, 
       col=colors, legend=legends)

Gráfico 2

colors <- c("#E6E6E6", "#666666")

par(mfrow = c(3, 2))
barplot(matrix(c(MRT_sem_F, MRT_1F), nrow = 2, ncol = 7, byrow = T), 
        ylab = "Response time (s)", xlab = "Time between Things requests", 
        names.arg = clock, beside = T, col = colors)
legend("topright", pch = c(15, 15), col = colors,
       legend = c("w/o Fog", "1 Fog"))

barplot(matrix(c(MRT_sem_F, MRT_3F), nrow = 2, ncol = 7, byrow = T), 
        ylab = "Response time (s)", xlab = "Time between Things requests", 
        names.arg = clock, beside = T, col = colors)
legend("topright", pch = c(15, 15), col = colors,
       legend = c("w/o Fog", "3 Fog"))

barplot(matrix(c(MRT_sem_F, MRT_5F), nrow = 2, ncol = 7, byrow = T), 
        ylab = "Response time (s)", xlab = "Time between Things requests", 
        names.arg = clock, beside = T, col = colors)
legend("topright", pch = c(15, 15), col = colors,
       legend = c("w/o Fog", "5 Fog"))

barplot(matrix(c(MRT_sem_F, MRT_10F), nrow = 2, ncol = 7, byrow = T), 
        ylab = "Response time (s)", xlab = "Time between Things requests", 
        names.arg = clock, beside = T, col = colors)
legend("topright", pch = c(15, 15), col = colors,
       legend = c("w/o Fog", "10 Fog"))

barplot(matrix(c(MRT_sem_F, MRT_15F), nrow = 2, ncol = 7, byrow = T), 
        ylab = "Response time (s)", xlab = "Time between Things requests", 
        names.arg = clock, beside = T, col = colors)
legend("topright", pch = c(15, 15), col = colors,
       legend = c("w/o Fog", "15 Fog"))

airquality dataset

airquality

Conversão das temperaturas

Utilizei outra representação da fórmula de conversão de temperaturas. C = (F - 32) * (5/9)

airquality$Temp <- mapply(function(far){return((far-32) * (5/9))}, airquality$Temp)

head(airquality)

Filtragem dos valores

library(dplyr)

temps <- airquality %>%
          filter(Month == 5) %>%
          select(Temp)

temps <- temps$Temp
temps

##  [1] 19.44444 22.22222 23.33333 16.66667 13.33333 18.88889 18.33333 15.00000
##  [9] 16.11111 20.55556 23.33333 20.55556 18.88889 20.00000 14.44444 17.77778
## [17] 18.88889 13.88889 20.00000 16.66667 15.00000 22.77778 16.11111 16.11111
## [25] 13.88889 14.44444 13.88889 19.44444 27.22222 26.11111 24.44444

Histograma com as temperaturas do mês de maio

hist(temps, main = "Temperaturas no mês de maio",
     ylab = "Densidade", xlab = "Temperatura",
     density = 20, freq = F, breaks = 4, col = "orange")
densidade <- density(temps)
lines(densidade)

Carrega o dataset

sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)

sales

Gráfico de pizza

pct <- round(sales$SALES/sum(sales$SALES) * 100)
lbls <- paste(pct, "%", sep = "")
pie(pct, labels = lbls, main = "Vendas por país",
    col = plasma(length(sales$COUNTRY)))
legend("topright", legend=sales$COUNTRY, fill=plasma(length(sales$COUNTRY)))

Carregando os dados

monitoring_01 <- read.csv("monitoringCloudData/monitoringCloudData_0.1.csv", stringsAsFactors = F)
monitoring_05 <- read.csv("monitoringCloudData/monitoringCloudData_0.5.csv", stringsAsFactors = F)
monitoring_1 <- read.csv("monitoringCloudData/monitoringCloudData_1.csv", stringsAsFactors = F)
monitoring_NONE <- read.csv("monitoringCloudData/monitoringCloudData_NONE.csv", stringsAsFactors = F)

head(monitoring_NONE)

Atualização da coluna currentTime

monitoring_01$currentTime <- as.numeric(as.POSIXct(monitoring_01$currentTime))
monitoring_05$currentTime <- as.numeric(as.POSIXct(monitoring_05$currentTime))
monitoring_1$currentTime <- as.numeric(as.POSIXct(monitoring_1$currentTime))
monitoring_NONE$currentTime <- as.numeric(as.POSIXct(monitoring_NONE$currentTime))

monitoring_01 <- monitoring_01 %>%
  mutate(currentTime = (currentTime - min(currentTime))/3600)
monitoring_05 <- monitoring_05 %>%
  mutate(currentTime = (currentTime - min(currentTime))/3600)
monitoring_1 <- monitoring_1 %>%
  mutate(currentTime = (currentTime - min(currentTime))/3600)
monitoring_NONE <- monitoring_NONE %>%
  mutate(currentTime = (currentTime - min(currentTime))/3600)

head(monitoring_01)

Atualização da coluna usedMemory

monitoring_01_mb <- monitoring_01
monitoring_01_mb$usedMemory <- gsub("MB", "", monitoring_01$usedMemory)

monitoring_01_mb <- monitoring_01_mb %>%
  filter(!is.na(as.numeric(usedMemory)))

monitoring_01_mb$usedMemory <- as.numeric(monitoring_01_mb$usedMemory)

monitoring_01_gb <- monitoring_01
monitoring_01_gb$usedMemory <- gsub("GB", "", monitoring_01$usedMemory)

monitoring_01_gb <- monitoring_01_gb %>%
  filter(!is.na(as.numeric(usedMemory))) %>%
  mutate(usedMemory = as.numeric(gsub("GB", "", usedMemory))*1000)

monitoring_01 <- rbind(monitoring_01_mb, monitoring_01_gb)

monitoring_01

monitoring_01 <- monitoring_01 %>%
  arrange(currentTime)

monitoring_05_mb <- monitoring_05
monitoring_05_mb$usedMemory <- gsub("MB", "", monitoring_05$usedMemory)

monitoring_05_mb <- monitoring_05_mb %>%
  filter(!is.na(as.numeric(usedMemory)))

monitoring_05_mb$usedMemory <- as.numeric(monitoring_05_mb$usedMemory)

monitoring_05_gb <- monitoring_05
monitoring_05_gb$usedMemory <- gsub("GB", "", monitoring_05$usedMemory)

monitoring_05_gb <- monitoring_05_gb %>%
  filter(!is.na(as.numeric(usedMemory))) %>%
  mutate(usedMemory = as.numeric(gsub("GB", "", usedMemory))*1000)

monitoring_05 <- rbind(monitoring_05_mb, monitoring_05_gb)
monitoring_05

monitoring_05 <- monitoring_05 %>%
  arrange(currentTime)

monitoring_1_mb <- monitoring_1
monitoring_1_mb$usedMemory <- gsub("MB", "", monitoring_1$usedMemory)

monitoring_1_mb <- monitoring_1_mb %>%
  filter(!is.na(as.numeric(usedMemory)))

monitoring_1_mb$usedMemory <- as.numeric(monitoring_1_mb$usedMemory)

monitoring_1_gb <- monitoring_1
monitoring_1_gb$usedMemory <- gsub("GB", "", monitoring_1$usedMemory)

monitoring_1_gb <- monitoring_1_gb %>%
  filter(!is.na(as.numeric(usedMemory))) %>%
  mutate(usedMemory = as.numeric(gsub("GB", "", usedMemory))*1000)

monitoring_1 <- rbind(monitoring_1_mb, monitoring_1_gb)
monitoring_1

monitoring_1 <- monitoring_1 %>%
  arrange(currentTime)

monitoring_NONE_mb <- monitoring_NONE
monitoring_NONE_mb$usedMemory <- gsub("MB", "", monitoring_NONE$usedMemory)

monitoring_NONE_mb <- monitoring_NONE_mb %>%
  filter(!is.na(as.numeric(usedMemory)))

monitoring_NONE_mb$usedMemory <- as.numeric(monitoring_NONE_mb$usedMemory)

monitoring_NONE_gb <- monitoring_NONE
monitoring_NONE_gb$usedMemory <- gsub("GB", "", monitoring_NONE$usedMemory)

monitoring_NONE_gb <- monitoring_NONE_gb %>%
  filter(!is.na(as.numeric(usedMemory))) %>%
  mutate(usedMemory = as.numeric(gsub("GB", "", usedMemory))*1000)

monitoring_NONE <- rbind(monitoring_NONE_mb, monitoring_NONE_gb)
monitoring_NONE

monitoring_NONE <- monitoring_NONE %>%
  arrange(currentTime)

Visualização dos dados

par(mfrow = c(2, 2))
plot(monitoring_NONE$currentTime, monitoring_NONE$usedMemory, type = 'l',
     main = "Memory Analysis (None Workload)", ylab = "Used Memory (MB)",
     xlab = "Time(hour)")
plot(monitoring_01$currentTime, monitoring_01$usedMemory, type = 'l',
     main = "Memory Analysis (Workload of 0.1)", ylab = "Used Memory (MB)",
     xlab = "Time(hour)")
plot(monitoring_05$currentTime, monitoring_05$usedMemory, type = 'l',
     main = "Memory Analysis (Workload of 0.5)", ylab = "Used Memory (MB)",
     xlab = "Time(hour)")
plot(monitoring_1$currentTime, monitoring_1$usedMemory, type = 'l',
     main = "Memory Analysis (Workload of 1.0)", ylab = "Used Memory (MB)",
     xlab = "Time(hour)")