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b8787f4302
This curbs runaway growth while still allowing substantial spikes in block weight Original specification from ArticMine: here is the scaling proposal Define: LongTermBlockWeight Before fork: LongTermBlockWeight = BlockWeight At or after fork: LongTermBlockWeight = min(BlockWeight, 1.4*LongTermEffectiveMedianBlockWeight) Note: To avoid possible consensus issues over rounding the LongTermBlockWeight for a given block should be calculated to the nearest byte, and stored as a integer in the block itself. The stored LongTermBlockWeight is then used for future calculations of the LongTermEffectiveMedianBlockWeight and not recalculated each time. Define: LongTermEffectiveMedianBlockWeight LongTermEffectiveMedianBlockWeight = max(300000, MedianOverPrevious100000Blocks(LongTermBlockWeight)) Change Definition of EffectiveMedianBlockWeight From (current definition) EffectiveMedianBlockWeight = max(300000, MedianOverPrevious100Blocks(BlockWeight)) To (proposed definition) EffectiveMedianBlockWeight = min(max(300000, MedianOverPrevious100Blocks(BlockWeight)), 50*LongTermEffectiveMedianBlockWeight) Notes: 1) There are no other changes to the existing penalty formula, median calculation, fees etc. 2) There is the requirement to store the LongTermBlockWeight of a block unencrypted in the block itself. This is to avoid possible consensus issues over rounding and also to prevent the calculations from becoming unwieldy as we move away from the fork. 3) When the EffectiveMedianBlockWeight cap is reached it is still possible to mine blocks up to 2x the EffectiveMedianBlockWeight by paying the corresponding penalty. Note: the long term block weight is stored in the database, but not in the actual block itself, since it requires recalculating anyway for verification.
74 lines
2.4 KiB
Python
Executable file
74 lines
2.4 KiB
Python
Executable file
#!/usr/bin/python
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# Simulate a maximal block attack on the Monero network
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# This uses the scheme proposed by ArticMine
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# Written by Sarang Nother
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# Copyright (c) 2019 The Monero Project
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import sys
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import math
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MEDIAN_WINDOW_SMALL = 100 # number of recent blocks for median computation
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MEDIAN_WINDOW_BIG = 5000
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MULTIPLIER_SMALL = 1.4 # multipliers for determining weights
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MULTIPLIER_BIG = 50.0
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MEDIAN_THRESHOLD = 300*1000 # initial value for median (scaled kB -> B)
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lcg_seed = 0
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embw = MEDIAN_THRESHOLD
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ltembw = MEDIAN_THRESHOLD
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weights = [MEDIAN_THRESHOLD]*MEDIAN_WINDOW_SMALL # weights of recent blocks (B), with index -1 most recent
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lt_weights = [MEDIAN_THRESHOLD]*MEDIAN_WINDOW_BIG # long-term weights
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# Compute the median of a list
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def get_median(vec):
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#temp = vec
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temp = sorted(vec)
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if len(temp) % 2 == 1:
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return temp[len(temp)/2]
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else:
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return int((temp[len(temp)/2]+temp[len(temp)/2-1])/2)
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def LCG():
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global lcg_seed
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lcg_seed = (lcg_seed * 0x100000001b3 + 0xcbf29ce484222325) & 0xffffffff
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return lcg_seed
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def run(t, blocks):
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global embw
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global ltembw
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weights = [MEDIAN_THRESHOLD]*MEDIAN_WINDOW_SMALL # weights of recent blocks (B), with index -1 most recent
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lt_weights = [MEDIAN_THRESHOLD]*MEDIAN_WINDOW_BIG # long-term weights
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for block in range(blocks):
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# determine the long-term effective weight
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ltmedian = get_median(lt_weights[-MEDIAN_WINDOW_BIG:])
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ltembw = max(MEDIAN_THRESHOLD,ltmedian)
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# determine the effective weight
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stmedian = get_median(weights[-MEDIAN_WINDOW_SMALL:])
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embw = min(max(MEDIAN_THRESHOLD,stmedian),int(MULTIPLIER_BIG*ltembw))
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# drop the lowest values
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weights = weights[1:]
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lt_weights = lt_weights[1:]
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# add a block of max weight
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if t == 0:
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max_weight = 2 * embw
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elif t == 1:
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r = LCG()
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max_weight = int(90 + r % 500000 + 250000 + math.sin(block / 200.) * 350000)
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if max_weight < 90: max_weight = 90
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elif t == 2:
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max_weight = 90
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else:
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sys.exit(1)
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weights.append(max_weight)
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lt_weights.append(min(max_weight,int(ltembw + int(ltembw * 2 / 5))))
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#print "H %u, r %u, BW %u, EMBW %u, LTBW %u, LTEMBW %u, ltmedian %u" % (block, r, max_weight, embw, lt_weights[-1], ltembw, ltmedian)
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print "H %u, BW %u, EMBW %u, LTBW %u" % (block, max_weight, embw, lt_weights[-1])
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run(0, 2 * MEDIAN_WINDOW_BIG)
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run(1, 9 * MEDIAN_WINDOW_BIG)
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run(2, 1 * MEDIAN_WINDOW_BIG)
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