profilesteering.py

   # Copyright 2023 University of Twente

   # Licensed under the Apache License, Version 2.0 (the "License");
   # you may not use this file except in compliance with the License.
   # You may obtain a copy of the License at

       # http://www.apache.org/licenses/LICENSE-2.0

   # Unless required by applicable law or agreed to in writing, software
   # distributed under the License is distributed on an "AS IS" BASIS,
   # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   # See the License for the specific language governing permissions and
   # limitations under the License.
   
import operator

class ProfileSteering():
	def __init__(self, devices):
		self.devices = devices
		self.p = [] # p in the PS paper
		self.x = [] # x in the PS paper
	
	def init(self, p):
		# Set the desired profile and reset xrange
		self.p = list(p)
		self.x = [0] * len(p)
	
		# Ask all devices to propose an initial planning
		for device in self.devices:
			r = device.init(p) 	# request device to create a planning
			self.x = list(map(operator.add, self.x, r))	# Perform the summation by adding the overall profile to the planning
		
		#print("Initial planning", self.x)
		return self.x
		
	def iterative(self, e_min, max_iters):
		# Iterative Loop
		for i in range(0, max_iters):	# Note we deviate here slightly by also definint a maximum number of iterations
			# Init
			best_improvement = 0
			best_device = None
			
			# difference profile
			d = list(map(operator.sub, self.x, self.p)) # d = x - p
			
			# request a new candidate profile from each device
			for device in self.devices:
				improvement = device.plan(d)
				if improvement > best_improvement:
					best_improvement = improvement
					best_device = device
					
			# Now set the winner (best scoring device) and update the planning
			if best_device is not None:
				diff = best_device.accept()
				self.x = list(map(operator.add, self.x, diff))
			
			print("Iteration", i, "-- Winner", best_device, "Improvement", best_improvement)
			# print("Overall Profile", self.x)
			
			# Now check id the improvement is good enough
			if best_improvement < e_min:
				break # Break the loop
				
				
			
		return self.x # Return the profile