Applying Ant Colony Optimization for Service Function Chaining in a 5G Network (IEEE IOTSMS 2019)

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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 )

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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.)

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

 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

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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.

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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.

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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)

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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)

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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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Average execution time: 138 ms

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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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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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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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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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CONTACTO:
@amoragar
@Segundo_96
amorag@ugr.es
segundomoto@correo.ugr.es

Applying Ant Colony Optimization for Service Function Chaining in a 5G Network (IEEE IOTSMS 2019)

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