def get_factors(self, target_latency, pixel_count):
factors = []
if len(self.client_latency)>0:
#client latency: (we want to keep client latency as low as can be)
metric = "client-latency"
l = 0.005 + self.min_client_latency
wm = logp(l / 0.020)
factors.append(calculate_for_target(metric, l, self.avg_client_latency, self.recent_client_latency, aim=0.8, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
if len(self.client_ping_latency)>0:
metric = "client-ping-latency"
l = 0.005 + self.min_client_ping_latency
wm = logp(l / 0.050)
factors.append(calculate_for_target(metric, l, self.avg_client_ping_latency, self.recent_client_ping_latency, aim=0.95, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
if len(self.server_ping_latency)>0:
metric = "server-ping-latency"
l = 0.005 + self.min_server_ping_latency
wm = logp(l / 0.050)
factors.append(calculate_for_target(metric, l, self.avg_server_ping_latency, self.recent_server_ping_latency, aim=0.95, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
#damage packet queue size: (includes packets from all windows)
factors.append(queue_inspect("damage-packet-queue-size", self.damage_packet_qsizes, smoothing=sqrt))
#damage packet queue pixels (global):
qpix_time_values = [(event_time, value) for event_time, _, value in list(self.damage_packet_qpixels)]
factors.append(queue_inspect("damage-packet-queue-pixels", qpix_time_values, div=pixel_count, smoothing=sqrt))
#damage data queue: (This is an important metric since each item will consume a fair amount of memory and each will later on go through the other queues.)
factors.append(queue_inspect("damage-data-queue", self.damage_data_qsizes))
if self.mmap_size>0:
#full: effective range is 0.0 to ~1.2
full = 1.0-float(self.mmap_free_size)/self.mmap_size
#aim for ~33%
factors.append(("mmap-area", "%s%% full" % int(100*full), logp(3*full), (3*full)**2))
return factors
def test_logp():
start = time.time()
for _ in xrange(100000):
x = random.random()
logp(x)
end = time.time()
print("logp:")
print("elapsed time: %.1fms" % (1000*(end-start)))
def test_logp():
start = time.time()
for _ in xrange(100000):
x = random.random()
logp(x)
end = time.time()
print("logp:")
print("elapsed time: %.1fms" % (1000 * (end - start)))
def get_target_quality(wid, window_dimensions, batch, global_statistics, statistics, min_quality):
low_limit = get_low_limit(global_statistics.mmap_size>0, window_dimensions)
#***********************************************************
# quality:
# 0 for lowest quality (low bandwidth usage)
# 100 for best quality (high bandwidth usage)
# here we try minimize client-latency, packet-backlog and batch.delay
packets_backlog, _, _ = statistics.get_client_backlog()
packets_bl = 1.0 - logp(packets_backlog/low_limit)
target = packets_bl
batch_q = -1
if batch is not None:
recs = len(batch.last_actual_delays)
if recs>0:
#weighted average between start delay and min_delay
#so when we start and we don't have any records, we don't lower quality
#just because the start delay is higher than min_delay
ref_delay = (batch.START_DELAY*10.0/recs + batch.min_delay*recs) / (recs+10.0/recs)
batch_q = ref_delay / max(batch.min_delay, batch.delay)
target = min(1.0, target, batch_q)
latency_q = -1
if len(global_statistics.client_latency)>0 and global_statistics.recent_client_latency>0:
latency_q = 3.0 * statistics.target_latency / global_statistics.recent_client_latency
target = min(target, latency_q)
target = min(1.0, max(0.0, target))
mq = min(100.0, max(min_quality, 0.0))
target_quality = mq + (100.0-mq) * target
info = {
"min_quality" : min_quality,
"backlog_factor": int(100.0*packets_bl),
"batch_factor" : int(100.0*batch_q),
"latency_factor": int(100.0*latency_q),
}
return info, target_quality
def get_target_quality(wid, window_dimensions, batch, global_statistics, statistics, min_quality):
low_limit = get_low_limit(global_statistics.mmap_size>0, window_dimensions)
#***********************************************************
# quality:
# 0 for lowest quality (low bandwidth usage)
# 100 for best quality (high bandwidth usage)
# here we try minimize client-latency, packet-backlog and batch.delay
packets_backlog, _, _ = statistics.get_client_backlog()
packets_bl = 1.0 - logp(packets_backlog/low_limit)
target = packets_bl
batch_q = -1
recs = len(batch.last_actual_delays)
if recs>0:
#weighted average between start delay and min_delay
#so when we start and we don't have any records, we don't lower quality
#just because the start delay is higher than min_delay
ref_delay = (batch.START_DELAY*10.0/recs + batch.min_delay*recs) / (recs+10.0/recs)
batch_q = ref_delay / max(batch.min_delay, batch.delay)
target = min(1.0, target, batch_q)
latency_q = -1
if len(global_statistics.client_latency)>0 and global_statistics.recent_client_latency>0:
latency_q = 3.0 * statistics.target_latency / global_statistics.recent_client_latency
target = min(target, latency_q)
target = min(1.0, max(0.0, target))
mq = min(100.0, max(min_quality, 0.0))
target_quality = mq + (100.0-mq) * target
info = {
"min_quality" : min_quality,
"backlog_factor": int(100.0*packets_bl),
"batch_factor" : int(100.0*batch_q),
"latency_factor": int(100.0*latency_q),
}
return info, target_quality
def get_factors(self, pixel_count, delay):
factors = []
#ratio of "in" and "out" latency indicates network bottleneck:
#(the difference between the two is the time it takes to send)
if len(self.damage_in_latency)>0 and len(self.damage_out_latency)>0:
ad = max(0.001, self.avg_damage_out_latency-self.avg_damage_in_latency)
rd = max(0.001, self.recent_damage_out_latency-self.recent_damage_in_latency)
div = 0.040 / max(ad, rd) #reduce weight for low latencies (matter less)
metric = "damage-network-delay"
#info: avg delay=%.3f recent delay=%.3f" % (ad, rd)
factors.append(calculate_for_average(metric, ad, rd, weight_div=div))
#send speed:
if self.avg_send_speed is not None and self.recent_send_speed is not None:
#our calculate methods aims for lower values, so invert speed
#this is how long it takes to send 1MB:
avg1MB = 1.0*1024*1024/self.avg_send_speed
recent1MB = 1.0*1024*1024/self.recent_send_speed
#we only really care about this when the speed is quite low,
#so adjust the weight accordingly:
minspeed = float(128*1024)
div = logp(max(self.recent_send_speed, minspeed)/minspeed)
metric = "network-send-speed"
#info: avg=%s, recent=%s (KBytes/s), div=%s" % (int(self.avg_send_speed/1024), int(self.recent_send_speed/1024), div)
factors.append(calculate_for_average(metric, avg1MB, recent1MB, weight_offset=1.0, weight_div=div))
#client decode time:
if self.avg_decode_speed is not None and self.recent_decode_speed is not None:
metric = "client-decode-speed"
#info: avg=%.1f, recent=%.1f (MPixels/s)" % (self.avg_decode_speed/1000/1000, self.recent_decode_speed/1000/1000)
#our calculate methods aims for lower values, so invert speed
#this is how long it takes to send 1MB:
avg1MB = 1.0*1024*1024/self.avg_decode_speed
recent1MB = 1.0*1024*1024/self.recent_decode_speed
weight_div = max(0.25, self.recent_decode_speed/(4*1000*1000))
factors.append(calculate_for_average(metric, avg1MB, recent1MB, weight_offset=0.0, weight_div=weight_div))
if self.last_damage_event_time:
#If nothing happens for a while then we can reduce the batch delay,
#however we must ensure this is not caused by a high system latency
#so we ignore short elapsed times.
elapsed = time.time()-self.last_damage_event_time
mtime = max(0, elapsed-self.max_latency*2)
#the longer the time, the more we slash:
weight = sqrt(mtime)
target = max(0, 1.0-mtime)
metric = "damage-rate"
info = {"elapsed" : int(1000.0*elapsed),
"max_latency" : int(1000.0*self.max_latency)}
factors.append((metric, info, target, weight))
return factors
def get_factors(self, pixel_count, delay):
factors = []
#ratio of "in" and "out" latency indicates network bottleneck:
#(the difference between the two is the time it takes to send)
if len(self.damage_in_latency)>0 and len(self.damage_out_latency)>0:
ad = max(0.001, self.avg_damage_out_latency-self.avg_damage_in_latency)
rd = max(0.001, self.recent_damage_out_latency-self.recent_damage_in_latency)
div = 0.040 / max(ad, rd) #reduce weight for low latencies (matter less)
metric = "damage-network-delay"
#info: avg delay=%.3f recent delay=%.3f" % (ad, rd)
factors.append(calculate_for_average(metric, ad, rd, weight_div=div))
#send speed:
if self.avg_send_speed is not None and self.recent_send_speed is not None:
#our calculate methods aims for lower values, so invert speed
#this is how long it takes to send 1MB:
avg1MB = 1.0*1024*1024/self.avg_send_speed
recent1MB = 1.0*1024*1024/self.recent_send_speed
#we only really care about this when the speed is quite low,
#so adjust the weight accordingly:
minspeed = float(128*1024)
div = logp(max(self.recent_send_speed, minspeed)/minspeed)
metric = "network-send-speed"
#info: avg=%s, recent=%s (KBytes/s), div=%s" % (int(self.avg_send_speed/1024), int(self.recent_send_speed/1024), div)
factors.append(calculate_for_average(metric, avg1MB, recent1MB, weight_offset=1.0, weight_div=div))
#client decode time:
if self.avg_decode_speed is not None and self.recent_decode_speed is not None:
metric = "client-decode-speed"
#info: avg=%.1f, recent=%.1f (MPixels/s)" % (self.avg_decode_speed/1000/1000, self.recent_decode_speed/1000/1000)
#our calculate methods aims for lower values, so invert speed
#this is how long it takes to send 1MB:
avg1MB = 1.0*1024*1024/self.avg_decode_speed
recent1MB = 1.0*1024*1024/self.recent_decode_speed
weight_div = max(0.25, self.recent_decode_speed/(4*1000*1000))
factors.append(calculate_for_average(metric, avg1MB, recent1MB, weight_offset=0.0, weight_div=weight_div))
if self.last_damage_event_time:
#If nothing happens for a while then we can reduce the batch delay,
#however we must ensure this is not caused by a high system latency
#so we ignore short elapsed times.
elapsed = time.time()-self.last_damage_event_time
mtime = max(0, elapsed-self.max_latency*2)
#the longer the time, the more we slash:
weight = sqrt(mtime)
target = max(0, 1.0-mtime)
metric = "damage-rate"
info = {"elapsed" : int(1000.0*elapsed),
"max_latency" : int(1000.0*self.max_latency)}
factors.append((metric, info, target, weight))
return factors
def update_batch_delay(batch, factors):
"""
Given a list of factors of the form:
[(description, factor, weight)]
we calculate a new batch delay.
We use a time-weighted average of previous delays as a starting value,
then combine it with the new factors.
"""
current_delay = batch.delay
now = time.time()
tv, tw = 0.0, 0.0
decay = max(1, logp(current_delay/batch.min_delay)/5.0)
max_delay = batch.max_delay
for delays, d_weight in ((batch.last_delays, 0.25), (batch.last_actual_delays, 0.75)):
if delays is not None and len(delays)>0:
#get the weighted average
#older values matter less, we decay them according to how much we batch already
#(older values matter more when we batch a lot)
for when, delay in list(delays):
#newer matter more:
w = d_weight/(1.0+((now-when)/decay)**2)
d = max(0, min(max_delay, delay))
tv += d*w
tw += w
hist_w = tw
for x in factors:
if len(x)!=4:
log.warn("invalid factor line: %s" % str(x))
valid_factors = [x for x in factors if x is not None and len(x)==4]
all_factors_weight = sum([w for _,_,_,w in valid_factors])
if all_factors_weight==0:
log("update_batch_delay: no weights yet!")
return
for _, _, factor, weight in valid_factors:
target_delay = max(0, min(max_delay, current_delay*factor))
w = max(1, hist_w)*weight/all_factors_weight
tw += w
tv += target_delay*w
mv = 0
if batch.always:
mv = batch.min_delay
batch.delay = max(mv, min(max_delay, tv // tw))
batch.last_updated = now
batch.factors = valid_factors
def get_factors(self, target_latency, pixel_count):
factors = []
if len(self.client_latency) > 0:
#client latency: (we want to keep client latency as low as can be)
metric = "client-latency"
l = 0.005 + self.min_client_latency
wm = logp(l / 0.020)
factors.append(
calculate_for_target(metric,
l,
self.avg_client_latency,
self.recent_client_latency,
aim=0.8,
slope=0.005,
smoothing=sqrt,
weight_multiplier=wm))
if len(self.client_ping_latency) > 0:
metric = "client-ping-latency"
l = 0.005 + self.min_client_ping_latency
wm = logp(l / 0.050)
factors.append(
calculate_for_target(metric,
l,
self.avg_client_ping_latency,
self.recent_client_ping_latency,
aim=0.95,
slope=0.005,
smoothing=sqrt,
weight_multiplier=wm))
if len(self.server_ping_latency) > 0:
metric = "server-ping-latency"
l = 0.005 + self.min_server_ping_latency
wm = logp(l / 0.050)
factors.append(
calculate_for_target(metric,
l,
self.avg_server_ping_latency,
self.recent_server_ping_latency,
aim=0.95,
slope=0.005,
smoothing=sqrt,
weight_multiplier=wm))
#packet queue size: (includes packets from all windows)
factors.append(
queue_inspect("packet-queue-size",
self.packet_qsizes,
smoothing=sqrt))
#packet queue pixels (global):
qpix_time_values = [
(event_time, value)
for event_time, _, value in list(self.damage_packet_qpixels)
]
factors.append(
queue_inspect("packet-queue-pixels",
qpix_time_values,
div=pixel_count,
smoothing=sqrt))
#compression data queue: (This is an important metric since each item will consume a fair amount of memory and each will later on go through the other queues.)
factors.append(
queue_inspect("compression-work-queue",
self.compression_work_qsizes))
if self.mmap_size > 0:
#full: effective range is 0.0 to ~1.2
full = 1.0 - float(self.mmap_free_size) / self.mmap_size
#aim for ~33%
factors.append(("mmap-area", "%s%% full" % int(100 * full),
logp(3 * full), (3 * full)**2))
return factors
