From 05806f5b5e7a941aa3771e93c82f134b5c954cc3 Mon Sep 17 00:00:00 2001 From: Ilija Vukotic Date: Fri, 31 Jul 2020 15:50:30 -0500 Subject: [PATCH] cleanup. caching cleanup based on last action. --- README.md | 22 ++++++- gym_cache/__init__.py | 1 + gym_cache/envs/cache_env.py | 73 ++++++++++++++------- results/plots/100TB_LRU.png | Bin 57161 -> 0 bytes results/plots/20TB_LRU.png | Bin 65130 -> 0 bytes results/plots/InfiniteCache_LRU.png | Bin 61321 -> 0 bytes results/plots/combinations/combination.png | Bin 62806 -> 0 bytes 7 files changed, 71 insertions(+), 25 deletions(-) delete mode 100644 results/plots/100TB_LRU.png delete mode 100644 results/plots/20TB_LRU.png delete mode 100644 results/plots/InfiniteCache_LRU.png delete mode 100644 results/plots/combinations/combination.png diff --git a/README.md b/README.md index 25c9639..8eb5b28 100644 --- a/README.md +++ b/README.md @@ -23,15 +23,33 @@ observation space has following variables: There are two discrete action environments (*Cache-v0* and *Cache-large-v0*) and one discrete action environment (*Cache-continuous-v0*). - ## Data extractions and preprocessing This is a two step procedure: * *extract raw data* _data/extract_data.py_ - change PQ, date range * *process raw data* _data/process_data.py_ - tokenizes filenames, generates unique fileIDs, sorts by access time. Processed data should be copied to the directory where actor runs. -It is an hdf5 file with one dataframe: +It is a parque file (.pa) with one dataframe: * index - access time (sorted) * six tokens derived from the filename ('1', '2', '3', '4', '5', '6') * filesize ('kB') * unique file identifier ('fID') + + +## Rewards +* always negative and correspond to cost to get the file - if it was cached it will be smaller +* files are cached irrespectively from what action actor performed for the file +* cleanup + * discrete environment - removes ones with + + +# Technical implementation in XCache server +* There are additional containers in the pod. + * environment container + * recieves gstream pfc, and disk info + * recalculates new state, reward, tokenizes recieved gstream info. + * memorizes last state, actors actions for each file + * triggers cleanup at lower HWM then xcache itself. Loops through memorized paths and removes ones least probably needed. + * redis db - used by environment container to store actor responses + * actor container + \ No newline at end of file diff --git a/gym_cache/__init__.py b/gym_cache/__init__.py index 01fc7e4..51aec57 100644 --- a/gym_cache/__init__.py +++ b/gym_cache/__init__.py @@ -28,6 +28,7 @@ nondeterministic=True, ) +# continuous action cache register( id='Cache-continuous-v0', entry_point='gym_cache.envs:CacheContinousEnv', diff --git a/gym_cache/envs/cache_env.py b/gym_cache/envs/cache_env.py index fce2b11..b6031c5 100644 --- a/gym_cache/envs/cache_env.py +++ b/gym_cache/envs/cache_env.py @@ -20,8 +20,8 @@ class CacheEnv(gym.Env): def __init__(self, InputData, CacheSize): - # self.name = '100TB_LRU' - self.name = '100TB_DDQN' + self.name = '100TB_LRU' + # self.name = '100TB_DDQN' # self.name = 'InfiniteCache_DDQN' self.accesses_filename = InputData + '.pa' @@ -35,14 +35,18 @@ def __init__(self, InputData, CacheSize): self.cache_hwm = .95 * self.cache_size self.cache_lwm = .90 * self.cache_size self.cache_kbytes = 0 + # tuples of fid: [access_no, filesize, decission] self.cache_content = {} self.files_processed = 0 self.data_processed = 0 self.monitoring = [] + # from previous cycle self.weight = 0 # delivered in next cycle. - self.found_in_cache = False # from previous cycle + self.found_in_cache = False + self.fID = None + ######### self.viewer = None @@ -51,19 +55,22 @@ def __init__(self, InputData, CacheSize): self.observation_space = spaces.Box( # first 6 are tokens, 7th is filesize, 8th is how full is cache at the moment low=np.array([0, 0, 0, 0, 0, 0, 0, 0]), - high=np.array([maxes[0], maxes[1], maxes[2], maxes[3], maxes[4], maxes[5], maxes[6], 100]), + high=np.array([maxes[0], maxes[1], maxes[2], maxes[3], + maxes[4], maxes[5], maxes[6], 100]), dtype=np.int32 ) print('environment loaded! cache size [kB]:', self.cache_size) def load_access_data(self): # last variable is the fileID. - - self.accesses= pd.read_parquet(self.accesses_filename) - print("accesses loaded:", self.accesses.shape[0]) + self.accesses = pd.read_parquet(self.accesses_filename) + # self.accesses = self.accesses.head(50000) + self.total_accesses = self.accesses.shape[0] + print("accesses loaded:", self.total_accesses) def save_monitoring_data(self): - mdata = pd.DataFrame(self.monitoring, columns=['kB', 'cache size', 'cache hit', 'reward']) + mdata = pd.DataFrame(self.monitoring, columns=[ + 'kB', 'cache size', 'cache hit', 'reward']) mdata.to_parquet('results/' + self.name + '.pa', engine='pyarrow') def seed(self, seed=None): @@ -71,8 +78,13 @@ def seed(self, seed=None): return [seed] def _cache_cleanup(self): - # order files by access instance - acc = pd.DataFrame.from_dict(self.cache_content, orient='index', columns=['accNo', 'fs']).sort_values(by='accNo', axis=0) + # create dataframe from cache info + acc = pd.DataFrame.from_dict(self.cache_content, orient='index', columns=[ + 'accNo', 'fs', 'action']) + # move accesses that we want kept to much later times + acc['accNo'] = acc['accNo'] + acc['action']*1000000 + # order files by access instance (equivalent of time) + acc.sort_values(by='accNo', axis=0, inplace=True) # starting from lowest number remove from cache counter = 0 while self.cache_kbytes > self.cache_lwm: @@ -84,24 +96,35 @@ def _cache_cleanup(self): def step(self, action): - # calculate reward from old weight, was it in cache and action + # calculate reward, this is for the access delivered in previous state + # takes into account: + # * weight (was remembered from previous step), + # * was it in cache (was remembered from previous step), + # * what action actor took reward = self.weight if (self.found_in_cache and action == 0) or (not self.found_in_cache and action == 1): reward = -reward - # """ checks if cache hit HWM """ - # print('cache filled:', self.cache_kbytes) - if self.cache_kbytes > self.cache_hwm: - # print('cache cleanup starting on access:', self.files_processed) - self._cache_cleanup() + if self.fID: # check that this is not the first access + # remember what is the action + prevCacheContent = self.cache_content[self.fID] + self.cache_content[self.fID] = ( + prevCacheContent[0], prevCacheContent[1], action) + # checks if cache hit HWM + # print('cache filled:', self.cache_kbytes) + if self.cache_kbytes > self.cache_hwm: + # print('cache cleanup starting on access:', self.files_processed) + self._cache_cleanup() + + # takes next access row = self.accesses.iloc[self.files_processed, :] - fID = row['fID'] + self.fID = row['fID'] fs = row['kB'] # print(row['1'], row['2'], row['3'], row['4'], row['5'], row['6'], row['kB'], row['fID']) - self.found_in_cache = fID in self.cache_content - # print('found in cache', self.found_in_cache, fID, self.cache_content) + self.found_in_cache = self.fID in self.cache_content + # print('found in cache', self.found_in_cache, self.fID, self.cache_content) if self.found_in_cache: # print('cache hit - 5%') self.weight = fs * self.cache_value_weight @@ -110,9 +133,10 @@ def step(self, action): self.weight = fs self.cache_kbytes += fs - self.cache_content[fID] = (self.files_processed, fs) + self.cache_content[self.fID] = (self.files_processed, fs, ) - self.monitoring.append([fs, self.cache_kbytes, self.found_in_cache, int(reward)]) + self.monitoring.append( + [fs, self.cache_kbytes, self.found_in_cache, int(reward)]) self.files_processed += 1 self.data_processed += fs @@ -120,13 +144,17 @@ def step(self, action): state = [row['1'], row['2'], row['3'], row['4'], row['5'], row['6'], fs, self.cache_kbytes * 100 // self.cache_size] - return np.array(state), int(reward), False, {} + if self.files_processed == self.total_accesses: + self.save_monitoring_data() + self.done = True + return np.array(state), int(reward), self.done, {} def reset(self): self.files_processed = 0 self.cache_content = {} self.cache_kbytes = 0 self.monitoring = [] + self.done = False return self.step(0)[0] @@ -145,7 +173,6 @@ def render(self, mode='human'): return def close(self): - self.save_monitoring_data() if self.viewer: self.viewer.close() self.viewer = None diff --git a/results/plots/100TB_LRU.png b/results/plots/100TB_LRU.png deleted file mode 100644 index e4d0a93d928185925cb5d1300fb02afa94967d7e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 57161 zcmd?RWmHvB+cvuC4iN!qL=Z%}8$m!Ck?!v94na^5P>^mA>F#cj?rsn^-JRduKJRHS#nqiZ+L=1L8916iYz&<3 zt?itxEet7LO&pyp>})C7m{^%u-cXu5JKOUyGym@cnCu+Qn6qt`V;~Sph}65c%I?W~ zi*D}9CO1dNhXclAUd%y?c$!;il$4a{bhX7Z`f6R1RfQO;&d;7bQG^>k4>@2ctgUyQ zE>|bOMMV7Y16%r#N(7J6Wh^J*hc zDC($zxfVZyC@T1nX(9!o`}bYj`~UB6%&XhmMN{3^&}e1jMc%zbBn}U*bAnDKx?UbE zek0+=_Ve@m6c|`{+)cvkabjF#@pv5RH&UR++}PN-HCM%Ox;fZk%m(*8Ha1uy%VLC@ zkuh8@iEDC(91pL()S$!tu<4TcJ`p;U5+;#F#n0a`#4%ZtH@^Ml%a@h<-C`r~1Lv5Y zL)Wvj{rQ?HClfQXX)nFcxS6a1n&57~Trj_S6|&JE$FAQBp;bs7Xn8m<(Htie@{VUH zuKg5%fO0Zxmi9qawL6*DiHOq@-|O;#=EaNl*o!Vgy}#fmlc_=t*?5)!GJaPUuM6v; 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