dataDistribution.py 22 KB
Newer Older
1
import random
2
3
4
import copy

from ortools.graph import pywrapgraph
5

Felix Seibert's avatar
Felix Seibert committed
6
from xtreemfs_client import osd
7
from xtreemfs_client import folder
8

9

Felix Seibert's avatar
Felix Seibert committed
10
11
12
13
class DataDistribution(object):
    """
    class to keep track of the osd (object storage device) locations of different folders, i.e.,
    their physical location.
14

Felix Seibert's avatar
Felix Seibert committed
15
16
    this class also allows to calculate several data distributions, e.g., mappings from folders to OSDs (each folder
    gets mapped to one OSD).
Felix Seibert's avatar
Felix Seibert committed
17
    """
18
19

    def __init__(self):
20
        self.OSDs = {}
21

22
    def add_new_osd(self, osd_uuid):
Felix Seibert's avatar
Felix Seibert committed
23
24
25
        """
        create a new empty osd and add it to the existing OSDs.
        """
26
27
        if osd_uuid in self.OSDs:
            print("key: " + osd_uuid + " is already present!")
28
            return
29
30
        new_osd = osd.OSD(osd_uuid)
        self.OSDs[osd_uuid] = new_osd
31

32
    def add_osd(self, new_osd):
Felix Seibert's avatar
Felix Seibert committed
33
34
35
        """
        add the given OSD (object) to the existing OSDs.
        """
36
37
        if new_osd.uuid in self.OSDs:
            print("key: " + new_osd.uuid + " is already present!")
38
            return
39
        self.OSDs[new_osd.uuid] = new_osd
40

41
    def add_osd_list(self, osd_list):
Felix Seibert's avatar
Felix Seibert committed
42
43
44
        """
        add the given list of OSDs (objects) to the existing OSDs.
        """
45
46
47
48
        for osd_uuid in osd_list:
            if osd_uuid not in self.OSDs:
                new_osd = osd.OSD(osd_uuid)
                self.OSDs[osd_uuid] = new_osd
49

50
51
52
53
54
55
56
57
58
    def replace_osd(self, new_osd):
        """
        replaces the osd with uuid new_osd.uuid by new_osd
        :param new_osd:
        :return:
        """
        assert new_osd.uuid in self.OSDs.keys()
        self.OSDs[new_osd.uuid] = new_osd

59
60
61
62
63
64
    def set_osd_capacities(self, osd_capacities):
        """
        set osd capacities
        :param osd_capacities: map from osd uuids to osd capacities
        :return:
        """
65
66
67
68
        # make sure that the keyset of self.OSDs matches the keyset of osd_capacities
        for osd_uuid in osd_capacities:
            assert osd_uuid in self.OSDs.keys()
        assert len(self.OSDs) == len(osd_capacities)
69
70
71
72
73
74
75
76
77
78
79
80
81
        for one_osd in self.OSDs.values():
            assert type(osd_capacities[one_osd.uuid]) is int
            one_osd.capacity = osd_capacities[one_osd.uuid]

    def set_osd_bandwidths(self, osd_bandwidths):
        """
        set osd bandwidths
        :param osd_bandwidths:
        :return:
        """
        for one_osd in self.OSDs.values():
            one_osd.bandwidth = osd_bandwidths[one_osd.uuid]

82
    def get_osd_list(self):
Felix Seibert's avatar
Felix Seibert committed
83
        """
84
        get a list of all existing OSD uuids.
Felix Seibert's avatar
Felix Seibert committed
85
        """
86
        osd_list = []
87
        for osd_name in self.OSDs.keys():
88
89
90
            osd_list.append(osd_name)
        return osd_list

91
    def get_containing_osd(self, folder_id):
Felix Seibert's avatar
Felix Seibert committed
92
93
94
        """
        get the OSD containing the given folder_id, or None if the folder is not assigned to any OSD.
        """
95
96
97
        for checked_osd in self.OSDs.values():
            if checked_osd.contains_folder(folder_id):
                return checked_osd
98
99
        return None

Felix Seibert's avatar
Felix Seibert committed
100
101
102
103
104
105
106
107
108
109
110
    def assign_new_osd(self, folder_id, new_osd):
        """
        assign folder_id to new_osd. if folder_id already is assigned to an OSD, this old assignment is deleted.
        """
        old_osd = self.get_containing_osd(folder_id)
        if old_osd is None:
            self.OSDs[new_osd].add_folder(folder_id, self.get_average_folder_size())
        else:
            self.OSDs[new_osd].add_folder(folder_id, self.OSDs[old_osd.uuid].folders[folder_id])
            self.OSDs[old_osd.uuid].remove_folder(folder_id)

111
112
113
114
115
116
117
118
119
120
121
122
    def get_total_folder_size(self):
        total_size = 0
        for one_osd in self.OSDs.values():
            total_size += one_osd.total_folder_size
        return total_size

    def get_total_bandwidth(self):
        total_bandwidth = 0
        for one_osd in self.OSDs.values():
            total_bandwidth += one_osd.bandwidth
        return total_bandwidth

123
    def get_average_folder_size(self):
Felix Seibert's avatar
Felix Seibert committed
124
125
126
        """
        get the average folder size of all folders of all OSDs.
        """
127
        total_size = 0
128
        total_number_of_folders = 0
129
        for one_osd in self.OSDs.values():
Felix Seibert's avatar
Felix Seibert committed
130
            total_size += one_osd.total_folder_size
131
            total_number_of_folders += len(one_osd.folders)
132
133
        if total_number_of_folders == 0:
            return 0
134
135
        return total_size / total_number_of_folders

136
    def get_average_load(self):
137
        """
138
        calculate the average OSD load, that is, the average of their total_folder_size.
139
140
        """
        total_folder_size = 0
141
142
143
        for osd in self.OSDs.values():
            total_folder_size += osd.get_load()
        return total_folder_size / len(self.OSDs)
144

145
    def get_maximum_load(self):
146
        """
147
        calculate the maximum OSD load, that is, the maximum of their total_folder_size.
148
149
150
151
        """
        maximum_load = 0
        maximum_osd = None
        for osd in self.OSDs.values():
152
            load = osd.total_folder_size
153
154
155
156
157
            if maximum_osd is None or load > maximum_load:
                maximum_load = load
                maximum_osd = osd
        return maximum_osd, maximum_load

158
    def get_average_processing_time(self):
159
        """
160
161
        calculate the average OSD processing time, that is, the average of their (total_folder_size / bandwidth).
        :return:
162
        """
163
        total_processing_time = 0
164
        for osd in self.OSDs.values():
165
166
            total_processing_time += osd.get_processing_time()
        return total_processing_time / len(self.OSDs)
167

168
169
170
171
172
173
174
175
176
177
178
179
    def get_maximum_processing_time(self):
        """
        calculate the maximum OSD processing time, also known as makespan
        """
        maximum_processing_time = 0
        maximum_osd = None
        for osd in self.OSDs.values():
            processing_time = osd.get_processing_time()
            if maximum_osd is None or processing_time > maximum_processing_time:
                maximum_processing_time = processing_time
                maximum_osd = osd
        return maximum_osd, maximum_processing_time
180

181
182
183
    def add_folders(self, folders,
                    ignore_osd_capacities=True,
                    random_osd_assignment=False,
184
185
                    ignore_folder_sizes=False,
                    debug=False):
Felix Seibert's avatar
Felix Seibert committed
186
187
        """
        adds a list of folders to the data distribution.
Felix Seibert's avatar
Felix Seibert committed
188
        if not specified otherwise, the assignments are calculated using the LPT algorithm.
Felix Seibert's avatar
Felix Seibert committed
189
190
        returns a list of assignments from folders to OSDs, for which (folders) there was previously no assignment.

191
192
        if capacities and bandwidths are set for the OSDs, folders are assigned accordingly
        (capacities are respected and OSDs with higher bandwidth obtain more/larger files).
193
194
195
196
197

        if random_osd_assignment=True and ignore_osd_capacities=True, a totally random OSD assignment generated.

        if random_osd_assignment=True and ignore_folder_sizes=True,
        folders are randomly assigned to OSDs such that all OSDs have the same number of folders (if possible).
198
199

        the assignment is stable (i.e., folders already assigned to an OSD are not reassigned to another OSD).
Felix Seibert's avatar
Felix Seibert committed
200
        """
201
202

        # find out which folders are not assigned yet
203
        new_folders = []
204
        for a_folder in folders:
205
            # TODO adding folders to OSDs might violate their capacity
206
            containing_osd = self.get_containing_osd(a_folder.id)
207
            if containing_osd is not None:
208
                containing_osd.add_folder(a_folder.id, a_folder.size)
209
            else:
210
                new_folders.append(a_folder)
211

212
213
        if debug:
            print("dataDistribution: random_osd_assignment: " + str(random_osd_assignment))
Felix Seibert's avatar
Felix Seibert committed
214

215
216
        # keep track of which unassigned folder gets assigned to which OSD.
        # this information must be returned
217
218
        osds_for_new_folders = []

219
        # totally random OSD assignment, ignoring OSD capacities
220
        # (might lead to I/O errors when too many groups are assigned to an OSD)
221
        if random_osd_assignment and ignore_osd_capacities and not ignore_folder_sizes:
222
223
            if debug:
                print("using totally random osd assignment")
224
            for a_folder in new_folders:
Felix Seibert's avatar
Felix Seibert committed
225
                random_osd = random.choice(list(self.OSDs.values()))
226
227
                random_osd.add_folder(a_folder.id, a_folder.size)
                osds_for_new_folders.append((a_folder.id,
Felix Seibert's avatar
Felix Seibert committed
228
                                             random_osd.uuid))
Felix Seibert's avatar
Felix Seibert committed
229
            return osds_for_new_folders
230

231
232
        # random OSD assignment respecting OSD capacities
        elif random_osd_assignment and not ignore_osd_capacities:
233
234
            if debug:
                print("using random osd assignment, respecting osd capacities")
235
            for a_folder in new_folders:
236
                suitable_osds = self.get_suitable_osds(a_folder.size)  # list of OSDs with enough capacity
237
                suitable_random_osd = random.choice(suitable_osds)
238
239
                suitable_random_osd.add_folder(a_folder.id, a_folder.size)
                osds_for_new_folders.append((a_folder.id,
240
241
242
                                             suitable_random_osd.uuid))
            return osds_for_new_folders

243
        # random OSD assignment ignoring folder sizes // round-robin style distribution with some randomness
244
        elif random_osd_assignment and ignore_folder_sizes:
245
246
247
248
249
250
251
252
253
254
            if debug:
                print("using random osd assignment ignoring folder sizes")

            average_folder_size = self.get_average_folder_size()
            if average_folder_size == 0:
                average_folder_size = 1

            modified_folders = list(map(lambda f: folder.Folder(f.id, average_folder_size, f.origin), folders))
            random.shuffle(modified_folders)
            return self.add_folders(modified_folders)
255

256
257
        # balanced deterministic OSD assignment (LPT)
        # (following largest processing time first, also called post-greedy approach)
258
259
        list.sort(new_folders, key=lambda x: x.size, reverse=True)

260
        # for each folder calculate the best OSD and add it to it
261
        for a_folder in new_folders:
262
            least_used_osd, _ = self.get_lpt_osd(a_folder.size)
263
264
            least_used_osd.add_folder(a_folder.id, a_folder.size)
            osds_for_new_folders.append((a_folder.id,
265
                                         least_used_osd.uuid))
266
267
        return osds_for_new_folders

268
    def rebalance_lpt(self, rebalance_factor=1):
269
270
271
        """
        rebalance folders to OSDs by assigning folders to new OSDs using the following strategy:
                1. 'unroll' the assignment. this means that, for each OSD, folders are removed until the OSD has less
272
                processing time than the average processing time of this distribution multiplied by rebalance_factor.
273
274
                2. reassign the removed folders using the LPT strategy.
        """
275
        total_folder_size = self.get_total_folder_size()
276
277
278
279
        movements = {}
        folders_to_be_reassigned = []

        # for each OSD, remove the smallest folder until its total_folder_size does not exceed the reassignment_limit
280
        # unrolling
281
        for osd in self.OSDs.values():
282
283
            # self.get_total_folder_size / self.get_total_bandwidth() is the optimal processing time for each OSD:
            # this value is a lower bound for the makespan
284
            reassignment_limit = self.get_rebalance_limit(rebalance_factor, total_folder_size)
285
            while osd.get_processing_time() > reassignment_limit:
286
287
288
289
290
                folder_id, folder_size = osd.get_smallest_folder()
                folders_to_be_reassigned.append(folder.Folder(folder_id, folder_size, None))
                movements[folder_id] = osd.uuid
                osd.remove_folder(folder_id)

291
        # reassignment
292
        new_assignments = self.add_folders(folders_to_be_reassigned)
293
294
295
296

        for folder_id, target in new_assignments:
            movements[folder_id] = (movements[folder_id], target)

297
        return movements
298

299
300
301
    def get_rebalance_limit(self, factor, total_folder_size):
        return factor * (total_folder_size / self.get_total_bandwidth())

302
    def rebalance_one_folder(self):
303
304
        """
        rebalance folders to OSDs by assigning folders to new OSDs using the following strategy:
305
                1. find OSD with the highest processing time
306
307
                2. get folder with smallest size on this OSD
                3. find new OSD for this folder using get_lpt_osd
308
309
                4. if the processing time on the new OSD is lower than on the original OSD,
                move the folder to the new OSD. otherwise, return.
310
311
312
313
314
315
316
317
318
319
320
321
        one open question is whether getting the folder with smallest size in step 2 is a clever choice
        (in principle, all folders of the OSD with the highest load are eligible).

        this optimization scheme classifies as local search. two distributions are neighbors if one can be transformed
        into the other by moving one folder from one OSD to another. note, however, that we do not search the whole
        neighborhood of a distribution.
        but it might be possible to show that if there is no improvement step of the type that we check for,
        there is no improvement step at all.
        """
        movements = {}

        while True:
322
323
            # find OSD with the highest processing time (origin)
            origin_osd, maximum_processing_time = self.get_maximum_processing_time()
324
325
326
327
328
329
330
331
332

            # pick a folder of this OSD
            # there are several ways to pick a folder (like largest, smallest, constrained by the resulting load of the
            # origin OSD, random...), it is not clear which way is a good way
            # for now pick the smallest folder on origin OSD
            smallest_folder_id, smallest_folder_size = self.OSDs[origin_osd.uuid].get_smallest_folder()

            # find other OSD best suited for the picked folder (target)
            # check whether moving folder from origin to target decreases the maximum load of all OSDs (makespan).
333
            best_osd, best_osd_processing_time = self.get_lpt_osd(smallest_folder_size)
334

335
            if best_osd_processing_time < maximum_processing_time:
336
337
338
339
340
341
342
                self.assign_new_osd(smallest_folder_id, best_osd.uuid)
                movements[smallest_folder_id] = (origin_osd.uuid, best_osd.uuid)
            else:
                break

        return movements

343
    def rebalance_two_steps_optimal_matching(self):
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
        """
        rebalance the distribution in two steps:
            1. calculate new distribution, independently of the current one
            2. use a minimum weight matching to transform the current distribution into the new distribution.
            minimum weight perfect matching on bipartite graphs can be solved using the successive shortest path
            algorithm.
        while any algorithm (solving/approximating that kind of problem) could be used for the first step,
        we here only implement the LPT algorithm, as it is a pretty good approximation with extremely good running time.
        :return:
        """
        virtual_distribution = copy.deepcopy(self)
        virtual_distribution.rebalance_lpt(rebalance_factor=0)

        # create a mincostflow object
        min_cost_flow = pywrapgraph.SimpleMinCostFlow()

        # define the directed graph for the flow
        # arcs are added individually, and are added implicitly
        # nodes (OSDs) have to be given by numeric id
        # so we need some conversion logic between current/virtual osds and node ids

        current_osds_list = list(self.OSDs.values())
        current_osds_list.sort(key=lambda x: x.uuid)
        virtual_osds_list = list(virtual_distribution.OSDs.values())
        virtual_osds_list.sort(key=lambda x: x.uuid)

        # conversion logic:
        # n = len(current_osd_list) = len(virtual_osd_list)
        # 0 = source, 1 = sink
        # 2, ..., n + 1: current OSDs
        # n + 2, ..., 2n + 1: virtual OSDs
        num_osds = len(current_osds_list)
        assert num_osds == len(virtual_osds_list)

        # edges between the two partitions
        for i in range(0, num_osds):
            for j in range(0, num_osds):
                current_osd = current_osds_list[i]
                virtual_osd = virtual_osds_list[j]
                # calculate the total size of folders that the current OSD has to fetch if the virtual OSD is assigned
                # to it
                edge_cost = 0
                for folder_id in virtual_osd.folders.keys():
                    if not current_osd.contains_folder(folder_id):
                        edge_cost += virtual_osd.folders[folder_id]
                tail = 2 + i  # current OSD
                head = num_osds + 2 + j  # virtual OSD
                min_cost_flow.AddArcWithCapacityAndUnitCost(tail, head, 1, edge_cost)

        # (artificial) edges between the source node and the current OSDs
        for i in range(0, num_osds):
            edge_cost = 0
            tail = 0
            head = i + 2
            min_cost_flow.AddArcWithCapacityAndUnitCost(tail, head, 1, edge_cost)

        # (artificial) edges between the virtual OSDs and the sink node
        for j in range(0, num_osds):
            edge_cost = 0
            tail = num_osds + 2 + j
            head = 1
            min_cost_flow.AddArcWithCapacityAndUnitCost(tail, head, 1, edge_cost)

        # define the supplies (which equals the number of OSDs)
        min_cost_flow.SetNodeSupply(0, num_osds)
        min_cost_flow.SetNodeSupply(1, -num_osds)

        # solve the min cost flow problem
        min_cost_flow.Solve()

        # we need to transform the calculated optimal assignment into a rebalanced distribution, including the necessary
        # movements
        current_to_virtual_osd_matching = []
        for arc in range(min_cost_flow.NumArcs()):
            tail = min_cost_flow.Tail(arc)
            head = min_cost_flow.Head(arc)
            if tail != 0 and head != 1 and min_cost_flow.Flow(arc) == 1:
                current_osd = current_osds_list[tail - 2]
                virtual_osd = virtual_osds_list[head - num_osds - 2]
                current_to_virtual_osd_matching.append((current_osd, virtual_osd))

        movements = {}
        for current_osd, virtual_osd in current_to_virtual_osd_matching:
            # iterate over virtual folders and check whether they are on the correct OSD.
            # the correct OSD is current_osd, as it is the one that is matched with virtual_osd.
            # if it is not present on current_osd, assign it to it.
            # this also removes it from the origin osd.
            for virtual_folder in virtual_osd.folders.keys():
                if not current_osd.contains_folder(virtual_folder):
                    origin_osd = self.get_containing_osd(virtual_folder).uuid
                    target_osd = current_osd.uuid
                    movements[virtual_folder] = (origin_osd, target_osd)
436
437
438
439

        for current_osd, virtual_osd in current_to_virtual_osd_matching:
            virtual_osd.uuid = current_osd.uuid
            self.replace_osd(virtual_osd)
440
441
442

        return movements

443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
    def rebalance_two_steps_random_matching(self):
        """
        rebalance the distribution in two steps:
            1. calculate new distribution, independently of the current one
            2. the OSDs of the new (virtual) matching are randomly assigned to the actual (current OSDs), i.e.,
            no matter which OSD has which folders.
        while any algorithm (solving/approximating that kind of problem) could be used for the first step,
        we here only implement the LPT algorithm, as it is a pretty good approximation with extremely good running time.
        :return:
        """
        virtual_distribution = copy.deepcopy(self)
        virtual_distribution.rebalance_lpt(rebalance_factor=0)

        movements = {}

        for virtual_osd in virtual_distribution.OSDs.values():
            for virtual_folder in virtual_osd.folders.keys():
                if not self.OSDs[virtual_osd.uuid].contains_folder(virtual_folder):
                    movements[virtual_folder] = (self.get_containing_osd(virtual_folder).uuid, virtual_osd.uuid)

        for virtual_osd in virtual_distribution.OSDs.values():
            self.replace_osd(virtual_osd)

        return movements

468
469
470
471
472
473
474
475
476
477
478
479
    def get_suitable_osds(self, folder_size):
        """
        create a list of OSDs with at least folder_size free capacity.
        :return:
        """
        suitable_osds = []
        for one_osd in self.OSDs.values():
            if one_osd.capacity - one_osd.total_folder_size - folder_size >= 0:
                suitable_osds.append(one_osd)

        return suitable_osds

480
    def get_lpt_osd(self, folder_size):
481
        """
482
        calculate the processing time of all OSDs, using the sum of their current total_folder_size and folder_size.
483
484
        return (OSD with the smallest such value, the smallest value)
        """
485
486
        best_processing_time = None
        best_processing_time_osd = -1
487
        for one_osd in self.get_suitable_osds(folder_size):
488
489
490
491
492
            processing_time = (one_osd.total_folder_size + folder_size) / one_osd.bandwidth
            if (best_processing_time is None) or processing_time < best_processing_time_osd:
                best_processing_time = one_osd
                best_processing_time_osd = processing_time
        return best_processing_time, best_processing_time_osd
493

494
    def update_folder(self, folder, size):
Felix Seibert's avatar
Felix Seibert committed
495
496
497
        """
        updates the size of a given folder
        """
498
499
500
        for one_osd in self.OSDs.values():
            if folder in one_osd.folders.keys():
                one_osd.update_folder(folder, size)
501
502
                break

503
    def description(self):
Felix Seibert's avatar
Felix Seibert committed
504
505
506
        """
        generates a string describing this data distribution
        """
507
        string = ""
508
509
        for one_osd in self.OSDs.values():
            string += str(one_osd)
510
            string += "\n"
511
            string += "folders : " + str(one_osd.folders)
512
            string += "\n"
513
        string += "average folder size: " + str(self.get_average_folder_size())
514
515
516
        return string

    def __str__(self):
517
518
519
520
521
        string_representation = "DataDistribution has " + str(len(self.OSDs)) \
                                + " osds: \n"
        for key, value in self.OSDs.items():
            string_representation += str(value) + " \n"
        return string_representation