This document presents an ant colony optimization (ACO) approach for solving the optimization of routing costs in service function chaining (SFC) for 5G networks, addressing a previously NP-hard problem. It discusses the methodology, including pheromone management and solution construction, along with experimental results demonstrating execution times and optimal solutions across different network scenarios. Future work includes adapting the algorithm for strict time constraints and refining heuristic guidance for improved efficiency.

1. 1
Segundo Moreno, Antonio Mora, Javier Carmona-Murillo, Pablo Padilla, Pedro Castillo
Universidad de Granada, Universidad de Extremadura
International Conference on Internet of Things:
Systems, Management and Security (IOTSMS 2019)
--| The International Workshop on Efficient and Smart 5G Technologies for IoT (ES5TI) |--
( Granada, October 22-25, 2019 )

2. 2
Hardware to software functions
virtualization
SDN (Software Defined Networks)
NFV (Network Function Virtualization)
 VNF (Virtual Network Functions)
SFC (Service Function Chaining)
 Composition of an ordered list of virtual
network functions (VNF) to execute advanced
services.
 Optimization of network resources.
Optimization of Routing with SFC (OR-SFC)
 Build the shortest path the data should follow between the adjacent VNFs to compose a
requested network service from an origin to a destination.
Source: Estimating VNF resource requirements using machine learning techniques
(Jmila, Houda,Khedher; Mohamed Ibn; El Yacoubi, Mounim A.)

3. OR-SFC is an optimization NP-Hard problem.
Existing solutions:
Exact solutions: Mainly models based on lineal programming implementations.
Heuristic: Set of rules to follow. They obtain results close to the optimal.
Proposal made in this paper:
 Ant Colony Optimization based solution for OR-SFC (Optimization of Routing
cost for Service Function Chaining)
 First existing proposal to date for the resolution of this problem with this
method (even the first proposal applying effectively a metaheuristic). 3

4.  Inspired in the behaviour of natural ants
when searching for food.
 They cooperate to get the fastest paths
between the nest and the source of food.
STIGMERGY:
 They use a chemical substance named
pheromone to define trails.
 Ants tend to follow high concentrations.
 Pheromone is evaporated over time. 4Source: WIKIPEDIA CC BY-SA 3.0

5. 5
ARTIFICIAL ANT:
 There are a set of agents called ants.
 All of them move in a weighted graph following
and depositing (artificial) pheromone.
 Pheromone and Heuristic information.
 They cooperate to find a solution (every ant
yields a complete solution).
 Formulae applied in the run:
 state transition rule (STR)  decides the next step for
each ant.
 pheromone updating  contribution and evaporation.
 evaluation function  assigns the cost to every
solution.

6. 6
OUR ANT SYSTEM:
 Considers a STR:
 Pheromone update is done once all the ants have built their solutions:
 Evaporation performed on all the edges/links.
 Contribution is performed only for the edges/links of the best solution.
if
otherwise

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 Connection: starting node, finish node, demanded capacity, function chain.
 Links: limited bandwidth value (capacity).
 Nodes: offer one or more virtual network functions, each one with required
resources. Node has limited available resources.

8. 8
RESTRICTIONS
Source and destination nodes must be
those indicated in the connection (service
request).
Network functions must be served in the
indicated order.
Links must have enough remaining
capacity.
Nodes must have available resources to
run the desired network function.

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ALGORITHM ADAPTATION
 Network initialization
 Reachable nodes
 Heuristic (higher available
resources)
 Probability roulette
 Restrictions (bandwidth and
resources)
 Update (link and nodes)
Constraints (complete path)
 Pheromone update (evaporation in
all and contribution only in best)
 Cost of a connection (hops)
 Global cost (set of connections)

10. 10
Ant-SFC ( )
Parameters initialization()
Read network configuration()
Read connections()
/* A solution for each connection is
searched */
for each connection c do
while termination criteria is not
met do
for each ant h do
s[h]=Build Solution(c,h)
end for
/* In all graph links */
Pheromone Evaporation()
/* Used links by the best ant
*/
s*=Choose Best Solution(s[h])
Global Pheromone Update(s*)
end while
/* Links bandwidth and resources of
nodes are updated*/
Network Update(c,s*)
end for
Build Solution (connection, ant id)
Ant initialization(ant id)
Network initialization() /* Set current network values */
current node = initial node.connec
current function = functions.connec[start]
L = save(current node) /* Visited states list */
F = save(current function) /* Served functions list */
while (current node ≠ connec(final node)) AND (current
function ≠ function.connec[end]) do
/* A: feasible nodes list, P: probability of moving to
each feasible node, : problem restrictions */
P = calculate transition probabilities(current node, A,
F, L, )
next node = probability roulette(P, )
/* Links bandwidth are updated */
Link Update(next node)
L = save(next node)
current node = next node
/* If the function is available it is served, and node
resources are updated */
if current function in current.functions node[] then
Update Node(current function)
F = save(current function)
current function = following.connec(functions[])
end if
end while

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SETUP
There will be 10 executions of each instance
 Parameters considered in the configuration:
Parameter Considered value
α (pheromone weight) 1.2
β (heuristic weight) 2
ρ (evaporation rate) 0.3
Number of Ants Double of numer of nodes (12and 38)
Number of iterations Equal to number of nodes (6 and 19)

12. 12
INSTANCE 1 – 6 NODES
Connection 1: (A, F, 2, [3,5,6])
Connection 2: (A, E, 8, [1,2,4])
Connection 3: (A, D, 5, [2,4,5])

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INSTANCE 1 – 6 NODES
Average execution time: 138 ms

14. 14
INSTANCE 1 – 6 NODES
Best solution
Connection 1: (A, F, 2, [3,5,6])
Connection 2: (A, E, 8, [1,2,4])
Connection 3: (A, D, 5, [2,4,5])

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INSTANCE 2 – 19 NODES
Connection 1: (A, F, 2, [3,5,6])
Connection 2: (A, E, 8, [1,2,4])
Connection 3: (A, D, 5, [2,4,5])

16. 16
INSTANCE 2 – 19 NODES
Connection 1: (H, J, 8, [5,1,2]) Connection 4: (R, J, 3, [5,2,3])
Connection 2: (B, D, 8, [4,3,1]) Connection 5: (J, S, 8, [4,1,3])
Connection 3: (Q, B, 1, [2,3,1])

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INSTANCE 2 – 19 NODES
Average Execution time: 389 ms
Connection 1: (H, J, 8, [5,1,2])
Connection 2: (B, D, 8, [4,3,1])
Connection 3: (Q, B, 1, [2,3,1])
Connection 4: (R, J, 3, [5,2,3])
Connection 5: (J, S, 8, [4,1,3])

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ACO implementation for the resolution of the Routing problem for SFC
(Service Function Chaining).
Tests performed in realistic scenarios equivalent to 5G network
deployments.
Real-time execution (ACO allows valid solutions to be obtained from
the first iteration).
FUTURE WORK
Adaptation of the algorithm to strong time constraints.
Applying heuristic to guide the search (to nodes that serve network functions).
Local Search Application.
Implementation of other ACO models.

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