Background IoT and M2M communication will be criticalfor secure, safe, and affluent living Rapid increase in mobile traffics in 5G mobile andwireless communication systems

A Control method for autonomous
mobility management systems
toward 5G mobile networks

Daichi Kominami
Osaka University, Japan

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Exabytes per Cisco predicts an over 8 times increase in mobile traffics
since 2015 to 2020

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Centralized managements in the user plane and the control plane in
the current LTE/EPC network will suffer from bottleneck problem

2ADMME selection mechanism

ADMME: autonomous and distributed MME[1] Load balancing in the control plane Attractor selection algorithm[2] Mobile management function is distributed overeNodeBs, SGWs, PGWs as a logical function

 Each ADMME periodically updates state vector 𝐦 using a dynamics
that has 𝑀 attractors
random walk when 𝛼 is small

ADMME
ADMME

ADMME
ADMME

PGW

ADMME
ADMME

ADMME

ADMME
ADMME

ADMME

 𝐦 = [𝑚1, 𝑚2, 𝑚3, … , 𝑚𝑀 ]
 𝑚𝑖 : How good to select0
ADMME 𝑖𝑚3
 𝑀: #(candidate ADMMEs)
𝑚2𝑚11
 ADMMEs in all nodes on
the path between ADMME itself and the managed UE
 ADMMEs in the nearest SGW and PGW 𝜼: Gaussian noise 𝛼: activity function that reflects goodness of the current 𝐦 By designing 𝛼 to associate system performance, this algorithm
has adaptability to changing environment

・・・
SGW

ADMME
ADMME

 ADMME selection
 Each ADMME manages user equipment (UE) anddynamically determines whether or not to delegate
mobility management of the UE to another ADMME
considering the load balancing and delay reduction[1] H. Yang, N. Wakamiya, M. Murata, T. Iwai, and S. Yamano, “An autonomous and distributed mobility management scheme in mobilecore networks,” in Proc. BICT, Dec. 2015.

3[2] A. Kashiwagi and I. Urabe, “Adaptive response of a gene network to environmenal changes by fitness-induced attractor selection,”
PLoS One, vol. 1, no. 1, pp. e49:1–10, Dec. 2006

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Design of activity 𝛼

Design of activity 𝛼 𝛼 is designed for multi-objective optimization[1] 𝛼 is designed for multi objective optimization[1] to minimize delay between UE and ADMME to minimize delay between UE and ADMME
when delay isminimized, 𝛼𝑑becomes 1

 to balance #(managed UE) among all candidate ADMMEs

 to balance #(managed UE) among all candidate ADMMEs

 integrate the above two activity function

 integrate the above two activity function

when complete
LB is achieved, 𝛼𝑙becomes 1
𝜌 is 0 to 1Problem[1] H. Yang, N. Wakamiya, M. Murata, T. Iwai, and S. Yamano, “An autonomous and distributed mobility management scheme in mobile
core networks,” in Proc. BICT, Dec. 2015.

5- Both 𝛼𝑑 and 𝛼𝑙 do not become 1 at the same time
* with small 𝛼, ADMME frequently switches UE (ADMME switching)- 𝛼 has to be nearly 1 in any situation to stabilize the system

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Evaluation scenario

Design of new activity 𝛼 ' New activity 𝛼′ with an extended sigmoid
function
output 1 PGW
 4 SGWs/PGW
 37 cells (eNodeBs)/SGW 100 UE/cell (total: 14400)

1′ α (ℎ) = max(α ℎ , σ(α(ℎ)))
 𝜎(𝛼(ℎ)) = Network model

11+𝑒 −𝑔 𝛼−𝛼𝑡ℎ0𝛼𝑡ℎsearch &convergence

1 𝛼(ℎ)

 ADMME selection
 5 ADMMEs/SGW, PGW
 1 ADMME/eNodeB selection is conducted when
receiving the below requests
 tracking area update
request (/30 min) handover requeststabilization

 Decision of threshold 𝛼𝑡ℎ Observation and control loop by a control node
ADMME
ADMME
ADMME

PGW

ADMME
ADMME
ADMME
ADMME

Control input for all ADMMEs
Performance of the network
Control node・・・

SGW
ADMME

ADMME
ADMME

ADMME
ADMME

α 1 ・・・ α m

ADMME・・・
UE1UEmSmall 𝛼𝑡ℎ loses a chance to searcha better system state
Large 𝛼𝑡ℎ causes system instability

 Mobility pattern All UE moves to one of neighboring cellevery 100 min (random walk pattern)

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Evaluation results

Evaluation results(Response delay and fairness index)(Response delay and fairness index)

 Mobility pattern: random walk

 Attractor selection algorithm leads the system state tosatisfy delay minimization and load balancing

 Much UE connect to ADMMEs in eNodeB via SGW or PGW More UE connect to ADMMEs in SGW and PGW(RTT between UE to ADMME is 88 or 94 ms)(In this case, RTT is 44 or 50 ms)

 Management load is balanced wellcontrol is weak Fairness decreases as the load of SGW and PGW increases

control is weak

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Delay minimization

Load balancing

control is weak

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Delay minimization

Evaluation results

Evaluation results(ADMME switching) Mobility pattern: random walk

 Mobility pattern: random walk

 Using threshold value 𝛼𝑡ℎ can control the characteristics
in performance Threshold value is set to obtain good performance in addition
to the control-plane load
 We have to consider the number of ADMME switching

Load balancing

control is weak(Response delay and fairness index)

control is weak
Delay minimization

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 Mobility pattern: random walk

 Attractor selection algorithm leads the system state tosatisfy delay minimization and load balancing

Load balancing

Delay between two nodes isstatic in our simulation
Δ0: 2 ms (UE to eNodeB)
Δ1: 20 ms (eNodeB to SGW)
Δ2: 3 ms (SGW to PGW) 𝛼𝑡ℎ is set to 0.6 (fixed) Original activity 𝛼 has more difficulty to become 1comparing to the static pattern Since stabilization makes delay longer when UE
moves, 𝛼′ becomes small with large 𝜌control is weak11

Load balancing

Delay minimization
Original activity

New activity

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Conclusion
 Conclusion
 We propose a simple control method for controllingboth of the network stability and performance in anautonomous system for the 5G mobile and wirelesscommunication system Reduction in the delay and the overhead of the control
plane in the 5G network can be realized Future work
 Further evaluation of the threshold control in activity
function Another application of our proposal, such as a mobile
edge computing scenario

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