plugeth/les/randselect.go
Felföldi Zsolt c2003ed63b les, les/flowcontrol: improved request serving and flow control (#18230)
This change

- implements concurrent LES request serving even for a single peer.
- replaces the request cost estimation method with a cost table based on
  benchmarks which gives much more consistent results. Until now the
  allowed number of light peers was just a guess which probably contributed
  a lot to the fluctuating quality of available service. Everything related
  to request cost is implemented in a single object, the 'cost tracker'. It
  uses a fixed cost table with a global 'correction factor'. Benchmark code
  is included and can be run at any time to adapt costs to low-level
  implementation changes.
- reimplements flowcontrol.ClientManager in a cleaner and more efficient
  way, with added capabilities: There is now control over bandwidth, which
  allows using the flow control parameters for client prioritization.
  Target utilization over 100 percent is now supported to model concurrent
  request processing. Total serving bandwidth is reduced during block
  processing to prevent database contention.
- implements an RPC API for the LES servers allowing server operators to
  assign priority bandwidth to certain clients and change prioritized
  status even while the client is connected. The new API is meant for
  cases where server operators charge for LES using an off-protocol mechanism.
- adds a unit test for the new client manager.
- adds an end-to-end test using the network simulator that tests bandwidth
  control functions through the new API.
2019-02-26 12:32:48 +01:00

170 lines
5.2 KiB
Go

// Copyright 2016 The go-ethereum Authors
// This file is part of the go-ethereum library.
//
// The go-ethereum library is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// The go-ethereum library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with the go-ethereum library. If not, see <http://www.gnu.org/licenses/>.
package les
import (
"math/rand"
)
// wrsItem interface should be implemented by any entries that are to be selected from
// a weightedRandomSelect set. Note that recalculating monotonously decreasing item
// weights on-demand (without constantly calling update) is allowed
type wrsItem interface {
Weight() int64
}
// weightedRandomSelect is capable of weighted random selection from a set of items
type weightedRandomSelect struct {
root *wrsNode
idx map[wrsItem]int
}
// newWeightedRandomSelect returns a new weightedRandomSelect structure
func newWeightedRandomSelect() *weightedRandomSelect {
return &weightedRandomSelect{root: &wrsNode{maxItems: wrsBranches}, idx: make(map[wrsItem]int)}
}
// update updates an item's weight, adds it if it was non-existent or removes it if
// the new weight is zero. Note that explicitly updating decreasing weights is not necessary.
func (w *weightedRandomSelect) update(item wrsItem) {
w.setWeight(item, item.Weight())
}
// remove removes an item from the set
func (w *weightedRandomSelect) remove(item wrsItem) {
w.setWeight(item, 0)
}
// setWeight sets an item's weight to a specific value (removes it if zero)
func (w *weightedRandomSelect) setWeight(item wrsItem, weight int64) {
idx, ok := w.idx[item]
if ok {
w.root.setWeight(idx, weight)
if weight == 0 {
delete(w.idx, item)
}
} else {
if weight != 0 {
if w.root.itemCnt == w.root.maxItems {
// add a new level
newRoot := &wrsNode{sumWeight: w.root.sumWeight, itemCnt: w.root.itemCnt, level: w.root.level + 1, maxItems: w.root.maxItems * wrsBranches}
newRoot.items[0] = w.root
newRoot.weights[0] = w.root.sumWeight
w.root = newRoot
}
w.idx[item] = w.root.insert(item, weight)
}
}
}
// choose randomly selects an item from the set, with a chance proportional to its
// current weight. If the weight of the chosen element has been decreased since the
// last stored value, returns it with a newWeight/oldWeight chance, otherwise just
// updates its weight and selects another one
func (w *weightedRandomSelect) choose() wrsItem {
for {
if w.root.sumWeight == 0 {
return nil
}
val := rand.Int63n(w.root.sumWeight)
choice, lastWeight := w.root.choose(val)
weight := choice.Weight()
if weight != lastWeight {
w.setWeight(choice, weight)
}
if weight >= lastWeight || rand.Int63n(lastWeight) < weight {
return choice
}
}
}
const wrsBranches = 8 // max number of branches in the wrsNode tree
// wrsNode is a node of a tree structure that can store wrsItems or further wrsNodes.
type wrsNode struct {
items [wrsBranches]interface{}
weights [wrsBranches]int64
sumWeight int64
level, itemCnt, maxItems int
}
// insert recursively inserts a new item to the tree and returns the item index
func (n *wrsNode) insert(item wrsItem, weight int64) int {
branch := 0
for n.items[branch] != nil && (n.level == 0 || n.items[branch].(*wrsNode).itemCnt == n.items[branch].(*wrsNode).maxItems) {
branch++
if branch == wrsBranches {
panic(nil)
}
}
n.itemCnt++
n.sumWeight += weight
n.weights[branch] += weight
if n.level == 0 {
n.items[branch] = item
return branch
}
var subNode *wrsNode
if n.items[branch] == nil {
subNode = &wrsNode{maxItems: n.maxItems / wrsBranches, level: n.level - 1}
n.items[branch] = subNode
} else {
subNode = n.items[branch].(*wrsNode)
}
subIdx := subNode.insert(item, weight)
return subNode.maxItems*branch + subIdx
}
// setWeight updates the weight of a certain item (which should exist) and returns
// the change of the last weight value stored in the tree
func (n *wrsNode) setWeight(idx int, weight int64) int64 {
if n.level == 0 {
oldWeight := n.weights[idx]
n.weights[idx] = weight
diff := weight - oldWeight
n.sumWeight += diff
if weight == 0 {
n.items[idx] = nil
n.itemCnt--
}
return diff
}
branchItems := n.maxItems / wrsBranches
branch := idx / branchItems
diff := n.items[branch].(*wrsNode).setWeight(idx-branch*branchItems, weight)
n.weights[branch] += diff
n.sumWeight += diff
if weight == 0 {
n.itemCnt--
}
return diff
}
// choose recursively selects an item from the tree and returns it along with its weight
func (n *wrsNode) choose(val int64) (wrsItem, int64) {
for i, w := range n.weights {
if val < w {
if n.level == 0 {
return n.items[i].(wrsItem), n.weights[i]
}
return n.items[i].(*wrsNode).choose(val)
}
val -= w
}
panic(nil)
}