| 1 | |
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| 2 | from nevow import inevow, rend, loaders, tags as T |
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| 3 | import math |
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| 4 | import util |
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| 5 | |
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| 6 | # factorial and binomial copied from |
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| 7 | # http://mail.python.org/pipermail/python-list/2007-April/435718.html |
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| 8 | |
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| 9 | def div_ceil(n, d): |
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| 10 | """ |
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| 11 | The smallest integer k such that k*d >= n. |
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| 12 | """ |
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| 13 | return (n/d) + (n%d != 0) |
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| 14 | |
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| 15 | def factorial(n): |
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| 16 | """factorial(n): return the factorial of the integer n. |
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| 17 | factorial(0) = 1 |
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| 18 | factorial(n) with n<0 is -factorial(abs(n)) |
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| 19 | """ |
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| 20 | result = 1 |
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| 21 | for i in range(1, abs(n)+1): |
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| 22 | result *= i |
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| 23 | assert n >= 0 |
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| 24 | return result |
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| 25 | |
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| 26 | def binomial(n, k): |
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| 27 | assert 0 <= k <= n |
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| 28 | if k == 0 or k == n: |
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| 29 | return 1 |
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| 30 | # calculate n!/k! as one product, avoiding factors that |
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| 31 | # just get canceled |
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| 32 | P = k+1 |
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| 33 | for i in range(k+2, n+1): |
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| 34 | P *= i |
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| 35 | # if you are paranoid: |
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| 36 | # C, rem = divmod(P, factorial(n-k)) |
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| 37 | # assert rem == 0 |
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| 38 | # return C |
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| 39 | return P//factorial(n-k) |
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| 40 | |
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| 41 | class ProvisioningTool(rend.Page): |
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| 42 | addSlash = True |
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| 43 | docFactory = loaders.xmlfile(util.sibling("provisioning.xhtml")) |
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| 44 | |
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| 45 | def render_forms(self, ctx, data): |
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| 46 | req = inevow.IRequest(ctx) |
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| 47 | |
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| 48 | def getarg(name, astype=int): |
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| 49 | if req.method != b"POST": |
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| 50 | return None |
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| 51 | if name in req.fields: |
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| 52 | return astype(req.fields[name].value) |
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| 53 | return None |
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| 54 | return self.do_forms(getarg) |
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| 55 | |
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| 56 | |
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| 57 | def do_forms(self, getarg): |
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| 58 | filled = getarg("filled", bool) |
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| 59 | |
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| 60 | def get_and_set(name, options, default=None, astype=int): |
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| 61 | current_value = getarg(name, astype) |
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| 62 | i_select = T.select(name=name) |
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| 63 | for (count, description) in options: |
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| 64 | count = astype(count) |
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| 65 | if ((current_value is not None and count == current_value) or |
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| 66 | (current_value is None and count == default)): |
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| 67 | o = T.option(value=str(count), selected="true")[description] |
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| 68 | else: |
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| 69 | o = T.option(value=str(count))[description] |
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| 70 | i_select = i_select[o] |
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| 71 | if current_value is None: |
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| 72 | current_value = default |
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| 73 | return current_value, i_select |
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| 74 | |
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| 75 | sections = {} |
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| 76 | def add_input(section, text, entry): |
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| 77 | if section not in sections: |
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| 78 | sections[section] = [] |
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| 79 | sections[section].extend([T.div[text, ": ", entry], "\n"]) |
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| 80 | |
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| 81 | def add_output(section, entry): |
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| 82 | if section not in sections: |
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| 83 | sections[section] = [] |
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| 84 | sections[section].extend([entry, "\n"]) |
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| 85 | |
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| 86 | def build_section(section): |
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| 87 | return T.fieldset[T.legend[section], sections[section]] |
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| 88 | |
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| 89 | def number(value, suffix=""): |
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| 90 | scaling = 1 |
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| 91 | if value < 1: |
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| 92 | fmt = "%1.2g%s" |
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| 93 | elif value < 100: |
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| 94 | fmt = "%.1f%s" |
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| 95 | elif value < 1000: |
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| 96 | fmt = "%d%s" |
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| 97 | elif value < 1e6: |
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| 98 | fmt = "%.2fk%s"; scaling = 1e3 |
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| 99 | elif value < 1e9: |
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| 100 | fmt = "%.2fM%s"; scaling = 1e6 |
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| 101 | elif value < 1e12: |
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| 102 | fmt = "%.2fG%s"; scaling = 1e9 |
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| 103 | elif value < 1e15: |
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| 104 | fmt = "%.2fT%s"; scaling = 1e12 |
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| 105 | elif value < 1e18: |
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| 106 | fmt = "%.2fP%s"; scaling = 1e15 |
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| 107 | else: |
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| 108 | fmt = "huge! %g%s" |
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| 109 | return fmt % (value / scaling, suffix) |
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| 110 | |
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| 111 | user_counts = [(5, "5 users"), |
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| 112 | (50, "50 users"), |
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| 113 | (200, "200 users"), |
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| 114 | (1000, "1k users"), |
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| 115 | (10000, "10k users"), |
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| 116 | (50000, "50k users"), |
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| 117 | (100000, "100k users"), |
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| 118 | (500000, "500k users"), |
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| 119 | (1000000, "1M users"), |
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| 120 | ] |
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| 121 | num_users, i_num_users = get_and_set("num_users", user_counts, 50000) |
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| 122 | add_input("Users", |
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| 123 | "How many users are on this network?", i_num_users) |
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| 124 | |
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| 125 | files_per_user_counts = [(100, "100 files"), |
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| 126 | (1000, "1k files"), |
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| 127 | (10000, "10k files"), |
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| 128 | (100000, "100k files"), |
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| 129 | (1e6, "1M files"), |
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| 130 | ] |
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| 131 | files_per_user, i_files_per_user = get_and_set("files_per_user", |
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| 132 | files_per_user_counts, |
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| 133 | 1000) |
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| 134 | add_input("Users", |
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| 135 | "How many files for each user? (avg)", |
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| 136 | i_files_per_user) |
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| 137 | |
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| 138 | space_per_user_sizes = [(1e6, "1MB"), |
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| 139 | (10e6, "10MB"), |
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| 140 | (100e6, "100MB"), |
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| 141 | (200e6, "200MB"), |
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| 142 | (1e9, "1GB"), |
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| 143 | (2e9, "2GB"), |
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| 144 | (5e9, "5GB"), |
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| 145 | (10e9, "10GB"), |
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| 146 | (100e9, "100GB"), |
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| 147 | (1e12, "1TB"), |
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| 148 | (2e12, "2TB"), |
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| 149 | (5e12, "5TB"), |
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| 150 | ] |
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| 151 | # Estimate ~5gb per user as a more realistic case |
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| 152 | space_per_user, i_space_per_user = get_and_set("space_per_user", |
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| 153 | space_per_user_sizes, |
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| 154 | 5e9) |
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| 155 | add_input("Users", |
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| 156 | "How much data for each user? (avg)", |
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| 157 | i_space_per_user) |
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| 158 | |
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| 159 | sharing_ratios = [(1.0, "1.0x"), |
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| 160 | (1.1, "1.1x"), |
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| 161 | (2.0, "2.0x"), |
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| 162 | ] |
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| 163 | sharing_ratio, i_sharing_ratio = get_and_set("sharing_ratio", |
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| 164 | sharing_ratios, 1.0, |
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| 165 | float) |
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| 166 | add_input("Users", |
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| 167 | "What is the sharing ratio? (1.0x is no-sharing and" |
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| 168 | " no convergence)", i_sharing_ratio) |
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| 169 | |
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| 170 | # Encoding parameters |
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| 171 | encoding_choices = [("3-of-10-5", "3.3x (3-of-10, repair below 5)"), |
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| 172 | ("3-of-10-8", "3.3x (3-of-10, repair below 8)"), |
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| 173 | ("5-of-10-7", "2x (5-of-10, repair below 7)"), |
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| 174 | ("8-of-10-9", "1.25x (8-of-10, repair below 9)"), |
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| 175 | ("27-of-30-28", "1.1x (27-of-30, repair below 28"), |
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| 176 | ("25-of-100-50", "4x (25-of-100, repair below 50)"), |
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| 177 | ] |
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| 178 | encoding_parameters, i_encoding_parameters = \ |
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| 179 | get_and_set("encoding_parameters", |
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| 180 | encoding_choices, "3-of-10-5", str) |
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| 181 | encoding_pieces = encoding_parameters.split("-") |
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| 182 | k = int(encoding_pieces[0]) |
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| 183 | assert encoding_pieces[1] == "of" |
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| 184 | n = int(encoding_pieces[2]) |
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| 185 | # we repair the file when the number of available shares drops below |
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| 186 | # this value |
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| 187 | repair_threshold = int(encoding_pieces[3]) |
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| 188 | |
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| 189 | add_input("Servers", |
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| 190 | "What are the default encoding parameters?", |
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| 191 | i_encoding_parameters) |
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| 192 | |
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| 193 | # Server info |
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| 194 | num_server_choices = [ (5, "5 servers"), |
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| 195 | (10, "10 servers"), |
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| 196 | (15, "15 servers"), |
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| 197 | (30, "30 servers"), |
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| 198 | (50, "50 servers"), |
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| 199 | (100, "100 servers"), |
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| 200 | (200, "200 servers"), |
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| 201 | (300, "300 servers"), |
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| 202 | (500, "500 servers"), |
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| 203 | (1000, "1k servers"), |
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| 204 | (2000, "2k servers"), |
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| 205 | (5000, "5k servers"), |
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| 206 | (10e3, "10k servers"), |
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| 207 | (100e3, "100k servers"), |
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| 208 | (1e6, "1M servers"), |
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| 209 | ] |
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| 210 | num_servers, i_num_servers = \ |
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| 211 | get_and_set("num_servers", num_server_choices, 30, int) |
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| 212 | add_input("Servers", |
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| 213 | "How many servers are there?", i_num_servers) |
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| 214 | |
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| 215 | # availability is measured in dBA = -dBF, where 0dBF is 100% failure, |
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| 216 | # 10dBF is 10% failure, 20dBF is 1% failure, etc |
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| 217 | server_dBA_choices = [ (10, "90% [10dBA] (2.4hr/day)"), |
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| 218 | (13, "95% [13dBA] (1.2hr/day)"), |
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| 219 | (20, "99% [20dBA] (14min/day or 3.5days/year)"), |
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| 220 | (23, "99.5% [23dBA] (7min/day or 1.75days/year)"), |
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| 221 | (30, "99.9% [30dBA] (87sec/day or 9hours/year)"), |
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| 222 | (40, "99.99% [40dBA] (60sec/week or 53min/year)"), |
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| 223 | (50, "99.999% [50dBA] (5min per year)"), |
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| 224 | ] |
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| 225 | server_dBA, i_server_availability = \ |
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| 226 | get_and_set("server_availability", |
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| 227 | server_dBA_choices, |
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| 228 | 20, int) |
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| 229 | add_input("Servers", |
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| 230 | "What is the server availability?", i_server_availability) |
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| 231 | |
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| 232 | drive_MTBF_choices = [ (40, "40,000 Hours"), |
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| 233 | ] |
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| 234 | drive_MTBF, i_drive_MTBF = \ |
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| 235 | get_and_set("drive_MTBF", drive_MTBF_choices, 40, int) |
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| 236 | add_input("Drives", |
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| 237 | "What is the hard drive MTBF?", i_drive_MTBF) |
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| 238 | # http://www.tgdaily.com/content/view/30990/113/ |
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| 239 | # http://labs.google.com/papers/disk_failures.pdf |
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| 240 | # google sees: |
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| 241 | # 1.7% of the drives they replaced were 0-1 years old |
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| 242 | # 8% of the drives they repalced were 1-2 years old |
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| 243 | # 8.6% were 2-3 years old |
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| 244 | # 6% were 3-4 years old, about 8% were 4-5 years old |
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| 245 | |
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| 246 | drive_size_choices = [ (100, "100 GB"), |
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| 247 | (250, "250 GB"), |
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| 248 | (500, "500 GB"), |
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| 249 | (750, "750 GB"), |
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| 250 | (1000, "1000 GB"), |
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| 251 | (2000, "2000 GB"), |
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| 252 | (3000, "3000 GB"), |
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| 253 | ] |
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| 254 | drive_size, i_drive_size = \ |
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| 255 | get_and_set("drive_size", drive_size_choices, 3000, int) |
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| 256 | drive_size = drive_size * 1e9 |
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| 257 | add_input("Drives", |
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| 258 | "What is the capacity of each hard drive?", i_drive_size) |
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| 259 | drive_failure_model_choices = [ ("E", "Exponential"), |
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| 260 | ("U", "Uniform"), |
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| 261 | ] |
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| 262 | drive_failure_model, i_drive_failure_model = \ |
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| 263 | get_and_set("drive_failure_model", |
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| 264 | drive_failure_model_choices, |
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| 265 | "E", str) |
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| 266 | add_input("Drives", |
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| 267 | "How should we model drive failures?", i_drive_failure_model) |
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| 268 | |
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| 269 | # drive_failure_rate is in failures per second |
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| 270 | if drive_failure_model == "E": |
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| 271 | drive_failure_rate = 1.0 / (drive_MTBF * 1000 * 3600) |
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| 272 | else: |
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| 273 | drive_failure_rate = 0.5 / (drive_MTBF * 1000 * 3600) |
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| 274 | |
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| 275 | # deletion/gc/ownership mode |
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| 276 | ownership_choices = [ ("A", "no deletion, no gc, no owners"), |
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| 277 | ("B", "deletion, no gc, no owners"), |
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| 278 | ("C", "deletion, share timers, no owners"), |
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| 279 | ("D", "deletion, no gc, yes owners"), |
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| 280 | ("E", "deletion, owner timers"), |
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| 281 | ] |
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| 282 | ownership_mode, i_ownership_mode = \ |
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| 283 | get_and_set("ownership_mode", ownership_choices, |
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| 284 | "A", str) |
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| 285 | add_input("Servers", |
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| 286 | "What is the ownership mode?", i_ownership_mode) |
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| 287 | |
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| 288 | # client access behavior |
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| 289 | access_rates = [ (1, "one file per day"), |
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| 290 | (10, "10 files per day"), |
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| 291 | (100, "100 files per day"), |
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| 292 | (1000, "1k files per day"), |
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| 293 | (10e3, "10k files per day"), |
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| 294 | (100e3, "100k files per day"), |
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| 295 | ] |
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| 296 | download_files_per_day, i_download_rate = \ |
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| 297 | get_and_set("download_rate", access_rates, |
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| 298 | 100, int) |
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| 299 | add_input("Users", |
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| 300 | "How many files are downloaded per day?", i_download_rate) |
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| 301 | download_rate = 1.0 * download_files_per_day / (24*60*60) |
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| 302 | |
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| 303 | upload_files_per_day, i_upload_rate = \ |
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| 304 | get_and_set("upload_rate", access_rates, |
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| 305 | 10, int) |
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| 306 | add_input("Users", |
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| 307 | "How many files are uploaded per day?", i_upload_rate) |
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| 308 | upload_rate = 1.0 * upload_files_per_day / (24*60*60) |
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| 309 | |
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| 310 | delete_files_per_day, i_delete_rate = \ |
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| 311 | get_and_set("delete_rate", access_rates, |
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| 312 | 10, int) |
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| 313 | add_input("Users", |
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| 314 | "How many files are deleted per day?", i_delete_rate) |
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| 315 | delete_rate = 1.0 * delete_files_per_day / (24*60*60) |
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| 316 | |
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| 317 | |
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| 318 | # the value is in days |
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| 319 | lease_timers = [ (1, "one refresh per day"), |
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| 320 | (7, "one refresh per week"), |
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| 321 | ] |
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| 322 | lease_timer, i_lease = \ |
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| 323 | get_and_set("lease_timer", lease_timers, |
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| 324 | 7, int) |
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| 325 | add_input("Users", |
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| 326 | "How frequently do clients refresh files or accounts? " |
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| 327 | "(if necessary)", |
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| 328 | i_lease) |
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| 329 | seconds_per_lease = 24*60*60*lease_timer |
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| 330 | |
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| 331 | check_timer_choices = [ (1, "every week"), |
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| 332 | (4, "every month"), |
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| 333 | (8, "every two months"), |
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| 334 | (16, "every four months"), |
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| 335 | ] |
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| 336 | check_timer, i_check_timer = \ |
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| 337 | get_and_set("check_timer", check_timer_choices, 4, int) |
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| 338 | add_input("Users", |
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| 339 | "How frequently should we check on each file?", |
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| 340 | i_check_timer) |
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| 341 | file_check_interval = check_timer * 7 * 24 * 3600 |
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| 342 | |
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| 343 | |
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| 344 | if filled: |
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| 345 | add_output("Users", T.div["Total users: %s" % number(num_users)]) |
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| 346 | add_output("Users", |
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| 347 | T.div["Files per user: %s" % number(files_per_user)]) |
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| 348 | file_size = 1.0 * space_per_user / files_per_user |
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| 349 | add_output("Users", |
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| 350 | T.div["Average file size: ", number(file_size)]) |
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| 351 | total_files = num_users * files_per_user / sharing_ratio |
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| 352 | |
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| 353 | add_output("Grid", |
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| 354 | T.div["Total number of files in grid: ", |
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| 355 | number(total_files)]) |
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| 356 | total_space = num_users * space_per_user / sharing_ratio |
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| 357 | add_output("Grid", |
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| 358 | T.div["Total volume of plaintext in grid: ", |
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| 359 | number(total_space, "B")]) |
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| 360 | |
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| 361 | total_shares = n * total_files |
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| 362 | add_output("Grid", |
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| 363 | T.div["Total shares in grid: ", number(total_shares)]) |
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| 364 | expansion = float(n) / float(k) |
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| 365 | |
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| 366 | total_usage = expansion * total_space |
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| 367 | add_output("Grid", |
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| 368 | T.div["Share data in grid: ", number(total_usage, "B")]) |
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| 369 | |
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| 370 | if n > num_servers: |
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| 371 | # silly configuration, causes Tahoe2 to wrap and put multiple |
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| 372 | # shares on some servers. |
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| 373 | add_output("Servers", |
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| 374 | T.div["non-ideal: more shares than servers" |
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| 375 | " (n=%d, servers=%d)" % (n, num_servers)]) |
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| 376 | # every file has at least one share on every server |
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| 377 | buckets_per_server = total_files |
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| 378 | shares_per_server = total_files * ((1.0 * n) / num_servers) |
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| 379 | else: |
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| 380 | # if nobody is full, then no lease requests will be turned |
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| 381 | # down for lack of space, and no two shares for the same file |
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| 382 | # will share a server. Therefore the chance that any given |
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| 383 | # file has a share on any given server is n/num_servers. |
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| 384 | buckets_per_server = total_files * ((1.0 * n) / num_servers) |
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| 385 | # since each such represented file only puts one share on a |
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| 386 | # server, the total number of shares per server is the same. |
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| 387 | shares_per_server = buckets_per_server |
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| 388 | add_output("Servers", |
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| 389 | T.div["Buckets per server: ", |
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| 390 | number(buckets_per_server)]) |
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| 391 | add_output("Servers", |
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| 392 | T.div["Shares per server: ", |
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| 393 | number(shares_per_server)]) |
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| 394 | |
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| 395 | # how much space is used on the storage servers for the shares? |
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| 396 | # the share data itself |
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| 397 | share_data_per_server = total_usage / num_servers |
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| 398 | add_output("Servers", |
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| 399 | T.div["Share data per server: ", |
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| 400 | number(share_data_per_server, "B")]) |
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| 401 | # this is determined empirically. H=hashsize=32, for a one-segment |
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| 402 | # file and 3-of-10 encoding |
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| 403 | share_validation_per_server = 266 * shares_per_server |
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| 404 | # this could be 423*buckets_per_server, if we moved the URI |
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| 405 | # extension into a separate file, but that would actually consume |
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| 406 | # *more* space (minimum filesize is 4KiB), unless we moved all |
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| 407 | # shares for a given bucket into a single file. |
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| 408 | share_uri_extension_per_server = 423 * shares_per_server |
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| 409 | |
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| 410 | # ownership mode adds per-bucket data |
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| 411 | H = 32 # depends upon the desired security of delete/refresh caps |
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| 412 | # bucket_lease_size is the amount of data needed to keep track of |
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| 413 | # the delete/refresh caps for each bucket. |
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| 414 | bucket_lease_size = 0 |
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| 415 | client_bucket_refresh_rate = 0 |
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| 416 | owner_table_size = 0 |
|---|
| 417 | if ownership_mode in ("B", "C", "D", "E"): |
|---|
| 418 | bucket_lease_size = sharing_ratio * 1.0 * H |
|---|
| 419 | if ownership_mode in ("B", "C"): |
|---|
| 420 | # refreshes per second per client |
|---|
| 421 | client_bucket_refresh_rate = (1.0 * n * files_per_user / |
|---|
| 422 | seconds_per_lease) |
|---|
| 423 | add_output("Users", |
|---|
| 424 | T.div["Client share refresh rate (outbound): ", |
|---|
| 425 | number(client_bucket_refresh_rate, "Hz")]) |
|---|
| 426 | server_bucket_refresh_rate = (client_bucket_refresh_rate * |
|---|
| 427 | num_users / num_servers) |
|---|
| 428 | add_output("Servers", |
|---|
| 429 | T.div["Server share refresh rate (inbound): ", |
|---|
| 430 | number(server_bucket_refresh_rate, "Hz")]) |
|---|
| 431 | if ownership_mode in ("D", "E"): |
|---|
| 432 | # each server must maintain a bidirectional mapping from |
|---|
| 433 | # buckets to owners. One way to implement this would be to |
|---|
| 434 | # put a list of four-byte owner numbers into each bucket, and |
|---|
| 435 | # a list of four-byte share numbers into each owner (although |
|---|
| 436 | # of course we'd really just throw it into a database and let |
|---|
| 437 | # the experts take care of the details). |
|---|
| 438 | owner_table_size = 2*(buckets_per_server * sharing_ratio * 4) |
|---|
| 439 | |
|---|
| 440 | if ownership_mode in ("E",): |
|---|
| 441 | # in this mode, clients must refresh one timer per server |
|---|
| 442 | client_account_refresh_rate = (1.0 * num_servers / |
|---|
| 443 | seconds_per_lease) |
|---|
| 444 | add_output("Users", |
|---|
| 445 | T.div["Client account refresh rate (outbound): ", |
|---|
| 446 | number(client_account_refresh_rate, "Hz")]) |
|---|
| 447 | server_account_refresh_rate = (client_account_refresh_rate * |
|---|
| 448 | num_users / num_servers) |
|---|
| 449 | add_output("Servers", |
|---|
| 450 | T.div["Server account refresh rate (inbound): ", |
|---|
| 451 | number(server_account_refresh_rate, "Hz")]) |
|---|
| 452 | |
|---|
| 453 | # TODO: buckets vs shares here is a bit wonky, but in |
|---|
| 454 | # non-wrapping grids it shouldn't matter |
|---|
| 455 | share_lease_per_server = bucket_lease_size * buckets_per_server |
|---|
| 456 | share_ownertable_per_server = owner_table_size |
|---|
| 457 | |
|---|
| 458 | share_space_per_server = (share_data_per_server + |
|---|
| 459 | share_validation_per_server + |
|---|
| 460 | share_uri_extension_per_server + |
|---|
| 461 | share_lease_per_server + |
|---|
| 462 | share_ownertable_per_server) |
|---|
| 463 | add_output("Servers", |
|---|
| 464 | T.div["Share space per server: ", |
|---|
| 465 | number(share_space_per_server, "B"), |
|---|
| 466 | " (data ", |
|---|
| 467 | number(share_data_per_server, "B"), |
|---|
| 468 | ", validation ", |
|---|
| 469 | number(share_validation_per_server, "B"), |
|---|
| 470 | ", UEB ", |
|---|
| 471 | number(share_uri_extension_per_server, "B"), |
|---|
| 472 | ", lease ", |
|---|
| 473 | number(share_lease_per_server, "B"), |
|---|
| 474 | ", ownertable ", |
|---|
| 475 | number(share_ownertable_per_server, "B"), |
|---|
| 476 | ")", |
|---|
| 477 | ]) |
|---|
| 478 | |
|---|
| 479 | |
|---|
| 480 | # rates |
|---|
| 481 | client_download_share_rate = download_rate * k |
|---|
| 482 | client_download_byte_rate = download_rate * file_size |
|---|
| 483 | add_output("Users", |
|---|
| 484 | T.div["download rate: shares = ", |
|---|
| 485 | number(client_download_share_rate, "Hz"), |
|---|
| 486 | " , bytes = ", |
|---|
| 487 | number(client_download_byte_rate, "Bps"), |
|---|
| 488 | ]) |
|---|
| 489 | total_file_check_rate = 1.0 * total_files / file_check_interval |
|---|
| 490 | client_check_share_rate = total_file_check_rate / num_users |
|---|
| 491 | add_output("Users", |
|---|
| 492 | T.div["file check rate: shares = ", |
|---|
| 493 | number(client_check_share_rate, "Hz"), |
|---|
| 494 | " (interval = %s)" % |
|---|
| 495 | number(1 / client_check_share_rate, "s"), |
|---|
| 496 | ]) |
|---|
| 497 | |
|---|
| 498 | client_upload_share_rate = upload_rate * n |
|---|
| 499 | # TODO: doesn't include overhead |
|---|
| 500 | client_upload_byte_rate = upload_rate * file_size * expansion |
|---|
| 501 | add_output("Users", |
|---|
| 502 | T.div["upload rate: shares = ", |
|---|
| 503 | number(client_upload_share_rate, "Hz"), |
|---|
| 504 | " , bytes = ", |
|---|
| 505 | number(client_upload_byte_rate, "Bps"), |
|---|
| 506 | ]) |
|---|
| 507 | client_delete_share_rate = delete_rate * n |
|---|
| 508 | |
|---|
| 509 | server_inbound_share_rate = (client_upload_share_rate * |
|---|
| 510 | num_users / num_servers) |
|---|
| 511 | server_inbound_byte_rate = (client_upload_byte_rate * |
|---|
| 512 | num_users / num_servers) |
|---|
| 513 | add_output("Servers", |
|---|
| 514 | T.div["upload rate (inbound): shares = ", |
|---|
| 515 | number(server_inbound_share_rate, "Hz"), |
|---|
| 516 | " , bytes = ", |
|---|
| 517 | number(server_inbound_byte_rate, "Bps"), |
|---|
| 518 | ]) |
|---|
| 519 | add_output("Servers", |
|---|
| 520 | T.div["share check rate (inbound): ", |
|---|
| 521 | number(total_file_check_rate * n / num_servers, |
|---|
| 522 | "Hz"), |
|---|
| 523 | ]) |
|---|
| 524 | |
|---|
| 525 | server_share_modify_rate = ((client_upload_share_rate + |
|---|
| 526 | client_delete_share_rate) * |
|---|
| 527 | num_users / num_servers) |
|---|
| 528 | add_output("Servers", |
|---|
| 529 | T.div["share modify rate: shares = ", |
|---|
| 530 | number(server_share_modify_rate, "Hz"), |
|---|
| 531 | ]) |
|---|
| 532 | |
|---|
| 533 | server_outbound_share_rate = (client_download_share_rate * |
|---|
| 534 | num_users / num_servers) |
|---|
| 535 | server_outbound_byte_rate = (client_download_byte_rate * |
|---|
| 536 | num_users / num_servers) |
|---|
| 537 | add_output("Servers", |
|---|
| 538 | T.div["download rate (outbound): shares = ", |
|---|
| 539 | number(server_outbound_share_rate, "Hz"), |
|---|
| 540 | " , bytes = ", |
|---|
| 541 | number(server_outbound_byte_rate, "Bps"), |
|---|
| 542 | ]) |
|---|
| 543 | |
|---|
| 544 | |
|---|
| 545 | total_share_space = num_servers * share_space_per_server |
|---|
| 546 | add_output("Grid", |
|---|
| 547 | T.div["Share space consumed: ", |
|---|
| 548 | number(total_share_space, "B")]) |
|---|
| 549 | add_output("Grid", |
|---|
| 550 | T.div[" %% validation: %.2f%%" % |
|---|
| 551 | (100.0 * share_validation_per_server / |
|---|
| 552 | share_space_per_server)]) |
|---|
| 553 | add_output("Grid", |
|---|
| 554 | T.div[" %% uri-extension: %.2f%%" % |
|---|
| 555 | (100.0 * share_uri_extension_per_server / |
|---|
| 556 | share_space_per_server)]) |
|---|
| 557 | add_output("Grid", |
|---|
| 558 | T.div[" %% lease data: %.2f%%" % |
|---|
| 559 | (100.0 * share_lease_per_server / |
|---|
| 560 | share_space_per_server)]) |
|---|
| 561 | add_output("Grid", |
|---|
| 562 | T.div[" %% owner data: %.2f%%" % |
|---|
| 563 | (100.0 * share_ownertable_per_server / |
|---|
| 564 | share_space_per_server)]) |
|---|
| 565 | add_output("Grid", |
|---|
| 566 | T.div[" %% share data: %.2f%%" % |
|---|
| 567 | (100.0 * share_data_per_server / |
|---|
| 568 | share_space_per_server)]) |
|---|
| 569 | add_output("Grid", |
|---|
| 570 | T.div["file check rate: ", |
|---|
| 571 | number(total_file_check_rate, |
|---|
| 572 | "Hz")]) |
|---|
| 573 | |
|---|
| 574 | total_drives = max(div_ceil(int(total_share_space), |
|---|
| 575 | int(drive_size)), |
|---|
| 576 | num_servers) |
|---|
| 577 | add_output("Drives", |
|---|
| 578 | T.div["Total drives: ", number(total_drives), " drives"]) |
|---|
| 579 | drives_per_server = div_ceil(total_drives, num_servers) |
|---|
| 580 | add_output("Servers", |
|---|
| 581 | T.div["Drives per server: ", drives_per_server]) |
|---|
| 582 | |
|---|
| 583 | # costs |
|---|
| 584 | if drive_size == 3000 * 1e9: |
|---|
| 585 | add_output("Servers", T.div["3000GB drive: $250 each"]) |
|---|
| 586 | drive_cost = 250 |
|---|
| 587 | else: |
|---|
| 588 | add_output("Servers", |
|---|
| 589 | T.div[T.b["unknown cost per drive, assuming $100"]]) |
|---|
| 590 | drive_cost = 100 |
|---|
| 591 | |
|---|
| 592 | if drives_per_server <= 4: |
|---|
| 593 | add_output("Servers", T.div["1U box with <= 4 drives: $1500"]) |
|---|
| 594 | server_cost = 1500 # typical 1U box |
|---|
| 595 | elif drives_per_server <= 12: |
|---|
| 596 | add_output("Servers", T.div["2U box with <= 12 drives: $2500"]) |
|---|
| 597 | server_cost = 2500 # 2U box |
|---|
| 598 | else: |
|---|
| 599 | add_output("Servers", |
|---|
| 600 | T.div[T.b["Note: too many drives per server, " |
|---|
| 601 | "assuming $3000"]]) |
|---|
| 602 | server_cost = 3000 |
|---|
| 603 | |
|---|
| 604 | server_capital_cost = (server_cost + drives_per_server * drive_cost) |
|---|
| 605 | total_server_cost = float(num_servers * server_capital_cost) |
|---|
| 606 | add_output("Servers", T.div["Capital cost per server: $", |
|---|
| 607 | server_capital_cost]) |
|---|
| 608 | add_output("Grid", T.div["Capital cost for all servers: $", |
|---|
| 609 | number(total_server_cost)]) |
|---|
| 610 | # $70/Mbps/mo |
|---|
| 611 | # $44/server/mo power+space |
|---|
| 612 | server_bandwidth = max(server_inbound_byte_rate, |
|---|
| 613 | server_outbound_byte_rate) |
|---|
| 614 | server_bandwidth_mbps = div_ceil(int(server_bandwidth*8), int(1e6)) |
|---|
| 615 | server_monthly_cost = 70*server_bandwidth_mbps + 44 |
|---|
| 616 | add_output("Servers", T.div["Monthly cost per server: $", |
|---|
| 617 | server_monthly_cost]) |
|---|
| 618 | add_output("Users", T.div["Capital cost per user: $", |
|---|
| 619 | number(total_server_cost / num_users)]) |
|---|
| 620 | |
|---|
| 621 | # reliability |
|---|
| 622 | any_drive_failure_rate = total_drives * drive_failure_rate |
|---|
| 623 | any_drive_MTBF = 1 // any_drive_failure_rate # in seconds |
|---|
| 624 | any_drive_MTBF_days = any_drive_MTBF / 86400 |
|---|
| 625 | add_output("Drives", |
|---|
| 626 | T.div["MTBF (any drive): ", |
|---|
| 627 | number(any_drive_MTBF_days), " days"]) |
|---|
| 628 | drive_replacement_monthly_cost = (float(drive_cost) |
|---|
| 629 | * any_drive_failure_rate |
|---|
| 630 | *30*86400) |
|---|
| 631 | add_output("Grid", |
|---|
| 632 | T.div["Monthly cost of replacing drives: $", |
|---|
| 633 | number(drive_replacement_monthly_cost)]) |
|---|
| 634 | |
|---|
| 635 | total_server_monthly_cost = float(num_servers * server_monthly_cost |
|---|
| 636 | + drive_replacement_monthly_cost) |
|---|
| 637 | |
|---|
| 638 | add_output("Grid", T.div["Monthly cost for all servers: $", |
|---|
| 639 | number(total_server_monthly_cost)]) |
|---|
| 640 | add_output("Users", |
|---|
| 641 | T.div["Monthly cost per user: $", |
|---|
| 642 | number(total_server_monthly_cost / num_users)]) |
|---|
| 643 | |
|---|
| 644 | # availability |
|---|
| 645 | file_dBA = self.file_availability(k, n, server_dBA) |
|---|
| 646 | user_files_dBA = self.many_files_availability(file_dBA, |
|---|
| 647 | files_per_user) |
|---|
| 648 | all_files_dBA = self.many_files_availability(file_dBA, total_files) |
|---|
| 649 | add_output("Users", |
|---|
| 650 | T.div["availability of: ", |
|---|
| 651 | "arbitrary file = %d dBA, " % file_dBA, |
|---|
| 652 | "all files of user1 = %d dBA, " % user_files_dBA, |
|---|
| 653 | "all files in grid = %d dBA" % all_files_dBA, |
|---|
| 654 | ], |
|---|
| 655 | ) |
|---|
| 656 | |
|---|
| 657 | time_until_files_lost = (n-k+1) / any_drive_failure_rate |
|---|
| 658 | add_output("Grid", |
|---|
| 659 | T.div["avg time until files are lost: ", |
|---|
| 660 | number(time_until_files_lost, "s"), ", ", |
|---|
| 661 | number(time_until_files_lost/86400, " days"), |
|---|
| 662 | ]) |
|---|
| 663 | |
|---|
| 664 | share_data_loss_rate = any_drive_failure_rate * drive_size |
|---|
| 665 | add_output("Grid", |
|---|
| 666 | T.div["share data loss rate: ", |
|---|
| 667 | number(share_data_loss_rate,"Bps")]) |
|---|
| 668 | |
|---|
| 669 | # the worst-case survival numbers occur when we do a file check |
|---|
| 670 | # and the file is just above the threshold for repair (so we |
|---|
| 671 | # decide to not repair it). The question is then: what is the |
|---|
| 672 | # chance that the file will decay so badly before the next check |
|---|
| 673 | # that we can't recover it? The resulting probability is per |
|---|
| 674 | # check interval. |
|---|
| 675 | # Note that the chances of us getting into this situation are low. |
|---|
| 676 | P_disk_failure_during_interval = (drive_failure_rate * |
|---|
| 677 | file_check_interval) |
|---|
| 678 | disk_failure_dBF = 10*math.log10(P_disk_failure_during_interval) |
|---|
| 679 | disk_failure_dBA = -disk_failure_dBF |
|---|
| 680 | file_survives_dBA = self.file_availability(k, repair_threshold, |
|---|
| 681 | disk_failure_dBA) |
|---|
| 682 | user_files_survives_dBA = self.many_files_availability( \ |
|---|
| 683 | file_survives_dBA, files_per_user) |
|---|
| 684 | all_files_survives_dBA = self.many_files_availability( \ |
|---|
| 685 | file_survives_dBA, total_files) |
|---|
| 686 | add_output("Users", |
|---|
| 687 | T.div["survival of: ", |
|---|
| 688 | "arbitrary file = %d dBA, " % file_survives_dBA, |
|---|
| 689 | "all files of user1 = %d dBA, " % |
|---|
| 690 | user_files_survives_dBA, |
|---|
| 691 | "all files in grid = %d dBA" % |
|---|
| 692 | all_files_survives_dBA, |
|---|
| 693 | " (per worst-case check interval)", |
|---|
| 694 | ]) |
|---|
| 695 | |
|---|
| 696 | |
|---|
| 697 | |
|---|
| 698 | all_sections = [] |
|---|
| 699 | all_sections.append(build_section("Users")) |
|---|
| 700 | all_sections.append(build_section("Servers")) |
|---|
| 701 | all_sections.append(build_section("Drives")) |
|---|
| 702 | if "Grid" in sections: |
|---|
| 703 | all_sections.append(build_section("Grid")) |
|---|
| 704 | |
|---|
| 705 | f = T.form(action=".", method="post", enctype="multipart/form-data") |
|---|
| 706 | |
|---|
| 707 | if filled: |
|---|
| 708 | action = "Recompute" |
|---|
| 709 | else: |
|---|
| 710 | action = "Compute" |
|---|
| 711 | |
|---|
| 712 | f = f[T.input(type="hidden", name="filled", value="true"), |
|---|
| 713 | T.input(type="submit", value=action), |
|---|
| 714 | all_sections, |
|---|
| 715 | ] |
|---|
| 716 | |
|---|
| 717 | try: |
|---|
| 718 | from allmydata import reliability |
|---|
| 719 | # we import this just to test to see if the page is available |
|---|
| 720 | _hush_pyflakes = reliability |
|---|
| 721 | del _hush_pyflakes |
|---|
| 722 | f = [T.div[T.a(href="../reliability")["Reliability Math"]], f] |
|---|
| 723 | except ImportError: |
|---|
| 724 | pass |
|---|
| 725 | |
|---|
| 726 | return f |
|---|
| 727 | |
|---|
| 728 | def file_availability(self, k, n, server_dBA): |
|---|
| 729 | """ |
|---|
| 730 | The full formula for the availability of a specific file is:: |
|---|
| 731 | |
|---|
| 732 | 1 - sum([choose(N,i) * p**i * (1-p)**(N-i)] for i in range(k)]) |
|---|
| 733 | |
|---|
| 734 | Where choose(N,i) = N! / ( i! * (N-i)! ) . Note that each term of |
|---|
| 735 | this summation is the probability that there are exactly 'i' servers |
|---|
| 736 | available, and what we're doing is adding up the cases where i is too |
|---|
| 737 | low. |
|---|
| 738 | |
|---|
| 739 | This is a nuisance to calculate at all accurately, especially once N |
|---|
| 740 | gets large, and when p is close to unity. So we make an engineering |
|---|
| 741 | approximation: if (1-p) is very small, then each [i] term is much |
|---|
| 742 | larger than the [i-1] term, and the sum is dominated by the i=k-1 |
|---|
| 743 | term. This only works for (1-p) < 10%, and when the choose() function |
|---|
| 744 | doesn't rise fast enough to compensate. For high-expansion encodings |
|---|
| 745 | (3-of-10, 25-of-100), the choose() function is rising at the same |
|---|
| 746 | time as the (1-p)**(N-i) term, so that's not an issue. For |
|---|
| 747 | low-expansion encodings (7-of-10, 75-of-100) the two values are |
|---|
| 748 | moving in opposite directions, so more care must be taken. |
|---|
| 749 | |
|---|
| 750 | Note that the p**i term has only a minor effect as long as (1-p)*N is |
|---|
| 751 | small, and even then the effect is attenuated by the 1-p term. |
|---|
| 752 | """ |
|---|
| 753 | |
|---|
| 754 | assert server_dBA > 9 # >=90% availability to use the approximation |
|---|
| 755 | factor = binomial(n, k-1) |
|---|
| 756 | factor_dBA = 10 * math.log10(factor) |
|---|
| 757 | exponent = n - k + 1 |
|---|
| 758 | file_dBA = server_dBA * exponent - factor_dBA |
|---|
| 759 | return file_dBA |
|---|
| 760 | |
|---|
| 761 | def many_files_availability(self, file_dBA, num_files): |
|---|
| 762 | """The probability that 'num_files' independent bernoulli trials will |
|---|
| 763 | succeed (i.e. we can recover all files in the grid at any given |
|---|
| 764 | moment) is p**num_files . Since p is close to unity, we express in p |
|---|
| 765 | in dBA instead, so we can get useful precision on q (=1-p), and then |
|---|
| 766 | the formula becomes:: |
|---|
| 767 | |
|---|
| 768 | P_some_files_unavailable = 1 - (1 - q)**num_files |
|---|
| 769 | |
|---|
| 770 | That (1-q)**n expands with the usual binomial sequence, 1 - nq + |
|---|
| 771 | Xq**2 ... + Xq**n . We use the same approximation as before, since we |
|---|
| 772 | know q is close to zero, and we get to ignore all the terms past -nq. |
|---|
| 773 | """ |
|---|
| 774 | |
|---|
| 775 | many_files_dBA = file_dBA - 10 * math.log10(num_files) |
|---|
| 776 | return many_files_dBA |
|---|
