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rvaggrvagg
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feat: add basic sp selection score modelling
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‎pkg/session/model/cmd/main.go

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package main
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import (
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"fmt"
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"math/rand"
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"os"
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"strconv"
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"time"
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"github.com/dustin/go-humanize"
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"github.com/filecoin-project/lassie/pkg/session"
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"github.com/filecoin-project/lassie/pkg/session/model"
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"github.com/multiformats/go-multicodec"
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)
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var (
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GRAPHSYNC_FAST_RELIABLE_LOTS_OF_POPULAR_DATA = model.Provider{
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Name: "graphsync fast, semi-reliable, lots of popular data",
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportGraphsyncFilecoinv1: {
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Candidate: model.Chance(0.5),
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Success: model.Chance(0.6),
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ConnectTimeMs: model.ProbDist{StdDev: 6, Mean: 10},
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TimeToFirstByteMs: model.ProbDist{StdDev: 6, Mean: 10},
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BandwidthBps: model.ProbDist{StdDev: 1e6, Mean: 1e8}, // Mean of 100Mb/s +/- 1MB/s
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LatencyMs: model.ProbDist{StdDev: 1, Mean: 20},
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FastRetrieval: model.Chance(0.9),
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Verified: model.Chance(0.9),
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},
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},
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}
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GRAPHSYNC_MEDIUM_RELIABLE_SOME_POPULAR_DATA = model.Provider{
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Name: "graphsync medium, semi-reliable, some popular data",
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportGraphsyncFilecoinv1: {
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Candidate: model.Chance(0.3),
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Success: model.Chance(0.5),
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ConnectTimeMs: model.ProbDist{StdDev: 6, Mean: 50},
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TimeToFirstByteMs: model.ProbDist{StdDev: 10, Mean: 25},
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BandwidthBps: model.ProbDist{StdDev: 1e6, Mean: 1e7}, // Mean of 10MB/s +/- 1MB/s
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LatencyMs: model.ProbDist{StdDev: 10, Mean: 40},
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FastRetrieval: model.Chance(0.9),
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Verified: model.Chance(0.9),
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},
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},
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}
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GRAPHSYNC_MEDIUM_RELIABLE_MINIMAL_POPULAR_DATA = model.Provider{
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Name: "graphsync medium, semi-reliable, minimal popular data",
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportGraphsyncFilecoinv1: {
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Candidate: model.Chance(0.1),
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Success: model.Chance(0.5),
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ConnectTimeMs: model.ProbDist{StdDev: 6, Mean: 50},
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TimeToFirstByteMs: model.ProbDist{StdDev: 10, Mean: 25},
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BandwidthBps: model.ProbDist{StdDev: 1e6, Mean: 1e7}, // Mean of 10MB/s +/- 1MB/s
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LatencyMs: model.ProbDist{StdDev: 10, Mean: 40},
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FastRetrieval: model.Chance(0.9),
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Verified: model.Chance(0.9),
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},
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},
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}
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GRAPHSYNC_MEDIUM_UNRELIABLE_SOME_POPULAR_DATA = model.Provider{
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Name: "graphsync medium, unreliable, some popular data",
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportGraphsyncFilecoinv1: {
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Candidate: model.Chance(0.3),
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Success: model.Chance(0.3),
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ConnectTimeMs: model.ProbDist{StdDev: 6, Mean: 50},
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TimeToFirstByteMs: model.ProbDist{StdDev: 20, Mean: 50},
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BandwidthBps: model.ProbDist{StdDev: 1e5, Mean: 1e6}, // Mean of 1MB/s +/- 100KB/s
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LatencyMs: model.ProbDist{StdDev: 10, Mean: 40},
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FastRetrieval: model.Chance(0.5),
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Verified: model.Chance(0.5),
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},
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},
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}
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GRAPHSYNC_MEDIUM_VERY_UNRELIABLE_SOME_POPULAR_DATA = model.Provider{
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Name: "graphsync medium, very unreliable, some popular data",
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportGraphsyncFilecoinv1: {
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Candidate: model.Chance(0.3),
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Success: model.Chance(0.1),
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ConnectTimeMs: model.ProbDist{StdDev: 100, Mean: 200},
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TimeToFirstByteMs: model.ProbDist{StdDev: 6, Mean: 100},
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BandwidthBps: model.ProbDist{StdDev: 1e5, Mean: 1e6}, // Mean of 1MB/s +/- 100KB/s
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LatencyMs: model.ProbDist{StdDev: 10, Mean: 100},
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FastRetrieval: model.Chance(0.2),
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Verified: model.Chance(0.2),
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},
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},
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}
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HTTP_FAST_SEMIRELIABLE_LOTS_OF_POPULAR_DATA = model.Provider{
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Name: "http fast, semi-reliable, lots of popular data", // e-ipfs?
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportIpfsGatewayHttp: {
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Candidate: model.Chance(0.5),
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Success: model.Chance(0.5),
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ConnectTimeMs: model.ProbDist{StdDev: 0, Mean: 0},
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TimeToFirstByteMs: model.ProbDist{StdDev: 6, Mean: 10},
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BandwidthBps: model.ProbDist{StdDev: 1e6, Mean: 1e8}, // Mean of 100Mb/s +/- 1MB/s
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LatencyMs: model.ProbDist{StdDev: 1, Mean: 20},
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},
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},
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}
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HTTP_MEDIUM_FLAKY_SOME_POPULAR_DATA = model.Provider{
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Name: "http medium, semi-reliable, lots of popular data", // e-ipfs?
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Probabilities: map[multicodec.Code]model.Probabilities{
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multicodec.TransportIpfsGatewayHttp: {
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Candidate: model.Chance(0.7),
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Success: model.Chance(0.6),
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ConnectTimeMs: model.ProbDist{StdDev: 0, Mean: 0},
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TimeToFirstByteMs: model.ProbDist{StdDev: 6, Mean: 10},
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BandwidthBps: model.ProbDist{StdDev: 1e6, Mean: 1e7}, // Mean of 10MB/s +/- 1MB/s
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LatencyMs: model.ProbDist{StdDev: 10, Mean: 40},
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},
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},
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}
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)
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func main() {
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seed := time.Now().UnixNano()
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switch len(os.Args) {
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case 1:
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case 2:
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// first arg is a seed if it's a number
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if s, err := strconv.ParseInt(os.Args[1], 10, 64); err == nil {
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seed = s
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} else {
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fmt.Println("Usage: go run main.go [seed]")
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os.Exit(1)
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}
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default:
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fmt.Println("Usage: go run main.go [seed]")
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os.Exit(1)
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}
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simRand := rand.New(rand.NewSource(seed))
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// TODO: generate static population up-front with fixed characteristics
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pop := &model.Population{}
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pop.Add(GRAPHSYNC_FAST_RELIABLE_LOTS_OF_POPULAR_DATA, 4)
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pop.Add(GRAPHSYNC_MEDIUM_RELIABLE_SOME_POPULAR_DATA, 20)
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pop.Add(GRAPHSYNC_MEDIUM_UNRELIABLE_SOME_POPULAR_DATA, 20)
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pop.Add(GRAPHSYNC_MEDIUM_RELIABLE_MINIMAL_POPULAR_DATA, 50)
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pop.Add(HTTP_FAST_SEMIRELIABLE_LOTS_OF_POPULAR_DATA, 1)
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sim := model.Simulation{
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Population: pop,
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Retrievals: 50000,
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RetrievalSize: model.ProbDist{StdDev: 2e7, Mean: 1e7}, // Mean of 20MB +/- 10MB
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HttpChance: model.Chance(0.5),
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GraphsyncChance: model.Chance(0.5),
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}
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ret := sim.Run(simRand)
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cfg := session.DefaultConfig()
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cfg.Random = simRand
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ses := session.NewSession(cfg, true)
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res := ret.RunWith(simRand, ses)
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fmt.Println("---------------------------------------------------------------")
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fmt.Println("Simulation of of", len(ret), "retrievals, seed:", seed)
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fmt.Println()
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fmt.Printf("\t Size per retrieval: %s < %s < %s\n", humanize.IBytes(uint64(ret.MinSize())), humanize.IBytes(uint64(ret.AvgSize())), humanize.IBytes(uint64(ret.MaxSize())))
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fmt.Printf("\tCandidate per retrieval: %s < %s < %s\n", humanize.Comma(int64(ret.MinCandidateCount())), humanize.Comma(int64(ret.AvgCandidateCount())), humanize.Comma(int64(ret.MaxCandidateCount())))
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fmt.Println("---------------------------------------------------------------")
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fmt.Printf("\t Runs: %d\n", res.Runs)
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fmt.Printf("\t Successes: %d\n", res.Successes)
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fmt.Printf("\t Retrieval failures: %d\n", res.RetrievalFailures)
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fmt.Printf("\t Size: %s\n", humanize.IBytes(uint64(res.Size)))
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fmt.Printf("\t Total time: %v\n", time.Duration(res.TotalTimeMs)*time.Millisecond)
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fmt.Printf("\t Average TTFB: %s\n", time.Duration(res.AverageTimeToFirstByteMs)*time.Millisecond)
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fmt.Printf("\t Average bandwidth: %s/s\n", humanize.IBytes(uint64(res.AverageBandwidth)))
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fmt.Printf("\t Total bandwidth: %s/s\n", humanize.IBytes(uint64(res.Size)/uint64(res.TotalTimeMs/1000)))
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fmt.Println("---------------------------------------------------------------")
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}

‎pkg/session/model/pop.go

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package model
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import "github.com/multiformats/go-multicodec"
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type Provider struct {
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Name string
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Probabilities map[multicodec.Code]Probabilities
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}
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type Population struct {
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Providers []PC
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}
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type PC struct {
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Provider Provider
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Count int
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}
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func (p *Population) Add(provider Provider, count int) {
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if p.Providers == nil {
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p.Providers = make([]PC, 0)
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}
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p.Providers = append(p.Providers, PC{Provider: provider, Count: count})
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}

‎pkg/session/model/prob.go

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package model
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import "math/rand"
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// Probabilities defines the probabilistic behaviour of a provider for a
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// particular protocol
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type Probabilities struct {
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// Probability of being a candidate for any given retrieval [0,1]
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Candidate Chance
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// Probability of a successful retrieval [0,1]
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Success Chance
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// Distribution for connect time in milliseconds
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ConnectTimeMs ProbDist
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// Distribution for time to first byte in milliseconds
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TimeToFirstByteMs ProbDist
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// Distribution in bandwidth in bytes per second, this has to account for
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// block fetching speed on the remote, not just the pipe
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BandwidthBps ProbDist
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// Distribution for latency in milliseconds, this will be multiplied to
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// simulate connection initialisation round-trips
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LatencyMs ProbDist
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// Probability of having FastRetrieval for a graphsync retrieval [0,1]
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FastRetrieval Chance
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// Probability of having Verified for a graphsync retrieval [0,1]
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Verified Chance
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}
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type ProbDist struct {
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StdDev float64
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Mean float64
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}
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func (pd ProbDist) Sample(rand *rand.Rand) float64 {
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return rand.NormFloat64()*pd.StdDev + pd.Mean
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}
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// Chance is the probability of a Roll() being true, the higher the value in the
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// range [0,1] the more likely it is to be true.
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type Chance float64
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func (c Chance) Roll(rand *rand.Rand) bool {
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return rand.Float64() < float64(c)
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}
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const FIFTY_FIFTY = Chance(0.5)

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