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
def get_factors(self, pixel_count):
factors = []
def mayaddfac(metric, info, factor, weight):
if weight>0.01:
factors.append((metric, info, factor, weight))
if self.client_latency:
#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)
mayaddfac(*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 self.client_ping_latency:
metric = "client-ping-latency"
l = 0.005 + self.min_client_ping_latency
wm = logp(l / 0.050)
mayaddfac(*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 self.server_ping_latency:
metric = "server-ping-latency"
l = 0.005 + self.min_server_ping_latency
wm = logp(l / 0.050)
mayaddfac(*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)
mayaddfac(*queue_inspect("packet-queue-size", self.packet_qsizes, smoothing=sqrt))
#packet queue pixels (global):
qpix_time_values = tuple((event_time, value) for event_time, _, value in tuple(self.damage_packet_qpixels))
mayaddfac(*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.)
mayaddfac(*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-self.mmap_free_size/self.mmap_size
#aim for ~33%
mayaddfac("mmap-area", "%s%% full" % int(100*full), logp(3*full), (3*full)**2)
if self.congestion_value>0:
mayaddfac("congestion", {}, 1+self.congestion_value, self.congestion_value*10)
return factors