Add a new benchmark

README.md

@@ -1,17 +1,49 @@
 # benchmarks
 
-## simple network latency
+## `rpc-amplification.R`
 
-You need R, R development headers, python3, pip3, wget, iproute2 and maybe some other dependencies.
+This benchmark tests how RPC between nodes having
+non negligible latency will impact the final S3 request latency.
 
-run with:
+The idea is that multiple communications must be done over the network
+between nodes of the cluster to answer an S3 request, and some of them
+are even sequentials. Depending of the chosen algorithms and implementations,
+the final S3 request latency will vary.
+
+You need to install first R, R development headers, python3, pip3, wget, iproute2 and maybe some other dependencies.
+Then run:
 
 ```
-sudo ./garage-latency-simple.R
+sudo ./rpc-amplification.R
 ```
 
-you can set a different topology:
+you can test a different topology with:
 
 ```
-MKNET_DC=./mknet/with-vdsl.yml sudo ./garage-latency-simple.R
+MKNET_DC=./mknet/with-vdsl.yml sudo ./rpc-amplification.R
 ```
+
+![Generated plot for RPC amplification](./png/rpc-amplification.png)
+
+*This benchmark should be fully automated.*
+
+## `rpc-complexity.R`
+
+This benchmark validates that the time to complete RPC for a
+given S3 request is stable when the number of node change.
+This is not always the case: some distributed protocols send a quadratic number
+of messages according to the number of nodes.
+
+You need an access to [Grid5000](https://grid5000.fr).
+Then import `jupyter/rpc-complexity.ipynb` in a [Grid5000 Jupyter Notebook](https://intranet.grid5000.fr/).
+
+Download all the `.csv` files in this `csv/` folder.
+And finally run:
+
+```
+./rpc-complexity.R
+```
+
+![Generated plot for RPC complexity](./png/rpc-complexity.png)
+
+*This benchmark is not fully automated.*

csv/rpc-complexity.csv

@@ -0,0 +1,976 @@
+endpoint,aggregation,time,cluster_size
+getobject,Mean,7.93812654,6
+listbuckets,Mean,0.5356363299999999,6
+listobjects,Mean,9.91764255,6
+putobject,Mean,10.49837125,6
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+putobject,Max,45.943678999999996,6
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+putobject,Min,2.544841,6
+removeobject,Min,4.276571,6
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+putobject,Max,48.366113,12
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+putobject,Min,2.4171489999999998,12
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+getobject,Mean,7.50791421,18
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rpc-amplification.R

@@ -94,4 +94,4 @@ ggplot(c, aes(x=endpoint,y=time_mean,fill=daemon,ymin=time_min,ymax=time_max)) +
       theme_classic() + 
       theme(legend.position = c(.8, .3))
 
-ggsave("endpoint-latency.png", width=200, height=110, units="mm")
+ggsave("png/rpc-amplification.png", width=200, height=110, units="mm")

rpc-complexity.R

@@ -0,0 +1,51 @@
+#!/usr/bin/env Rscript
+
+require(tidyverse)
+
+if (!file.exists("csv/rpc-complexity.csv")) {
+  read_csv("csv/clust-shift-no.csv") %>% 
+    group_by(endpoint) %>% 
+    summarise(
+      Mean = mean(nanoseconds) / 1000 / 1000,
+      Max = max(nanoseconds) / 1000 / 1000,
+      Min = min(nanoseconds) / 1000 / 1000,
+    ) %>% 
+    gather(aggregation, time, Mean, Max, Min) %>%
+    add_column(cluster_size=6) -> clust
+  
+  for (i in 1:64) {
+    print(paste("loading file",i))
+    clustsize <- i * 6 + 6
+      
+    read_csv(paste("csv/clust-shift-", i, ".csv", sep = "")) %>% 
+      group_by(endpoint) %>% 
+      summarise(
+        Mean = mean(nanoseconds) / 1000 / 1000,
+        Max = max(nanoseconds) / 1000 / 1000,
+        Min = min(nanoseconds) / 1000 / 1000,
+      ) %>% 
+      gather(aggregation, time, Mean, Max, Min) %>%
+      add_column(cluster_size=clustsize) -> tmp
+    
+    bind_rows(clust, tmp) -> clust
+  }
+  write_csv(clust, "csv/rpc-complexity.csv")
+}
+read_csv("csv/rpc-complexity.csv") -> clust
+
+
+ggplot(clust, aes(x=cluster_size,y=time,fill=aggregation)) +
+  geom_area() +
+  scale_y_continuous(expand=c(0,0))+
+  labs(
+    x="Number of nodes in the cluster", 
+    y="Latency (ms)",
+    fill="Aggregation",
+    caption="Get the code to reproduce this graph at https://git.deuxfleurs.fr/quentin/benchmarks",
+    title="Garage response time on common S3 endpoints with various cluster size",
+    subtitle="ran on 6 physical nodes on Grid5000 with multiple instances on each node\nspread on 3 zones: Lyon (nova), Rennes (paravance) and Nantes (econome)\nfew contention, latency is mainly due to RPC communications except on biggest values") +
+  facet_wrap(~endpoint) +
+  theme_classic() +
+  theme(legend.position = c(.8, .3))
+
+ggsave("png/rpc-complexity.png", width=200, height=110, units="mm")