Learning-Assisted Network Slicing for Diverse Applications ...€¦ · Admission control of slice...
Transcript of Learning-Assisted Network Slicing for Diverse Applications ...€¦ · Admission control of slice...
Learning-Assisted Network Slicing for Diverse Applications in 5G
Tao Han
The Department of Electrical and Computer Engineering
The University of North Carolina at Charlotte, NC, United States
https://webpages.uncc.edu/than3/index.html
What are “killer” applications for 5G?
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Tourist & Navigation
Smart Industry
Education
GamingAutonomous Vehicle
Battlefield
➢Diverse resource requirements from multiple domains: ▪ Radio Access Networks ▪ Transportation▪ Computing
Network Slicing: End-to-End Customization
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* Guan, W., Wen, X., Wang, L., Lu, Z. and Shen, Y., 2018. A service-oriented deployment policy of end-to-end network slicing based on complex network theory. IEEE Access, 6, pp.19691-19701.
Isolation v.s. Multiplexing
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Segment 1 Segment 2 Segment 3Resources:
Slices:
Tim
e
01
2… Can we provide isolation and
allow multiplexing?
What is the “Capacity” formula?
➢ The Shannon–Hartley theorem
𝐶 = 𝐵 𝑙𝑜𝑔2(1 + 𝑆𝑁𝑅)
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RAN Transport Edge/Cloud Computing
20 msGood
Better
Best
Good
Better
Best
Good
Better
Best
200 ms Good
Better
Best
Good
Better
Best
Good
Better
Best
Learning-Assisted Dynamic Network Slicing
1. Slice tenant requests a network slice
2. Admission control of slice requests
3. Orchestrator allocates the multiple domain resources (virtual) for all admitted network slices
4. Each network slice allocates resources (virtual) to its users
5. The virtual resource allocations of all the users are informed to hypervisor
6. The hypervisor maps the virtual resources to physical resources to maximize the efficiency of physical resources
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Qiang Liu and Tao Han, "VirtualEdge: Multi-Domain Resource Orchestration and Virtualization in Cellular Edge Computing", IEEE International Conference on Distributed Computing Systems (ICDCS) 2019.
Admission
Control
Slice Request
Slice Request
Multi-Domain Resource Orchestrator1
Multi-Domain Resource Hypervisor 2
Virtual Resource Virtual Resource
User to Virtual
Resource Mapping
User to Virtual
Resource Mapping
Virtual to Physical Resource Mapping
Slice N
Utility Update
Slice N
Slice 1
Utility Update
Slice 1
Multi-Domain
Resource
Scheduling
Multi-Domain
Resource
Scheduling
System Implementation
GPU
vNode #1
vNode #2
Multi-Domain Resource Orchestrator
UE#1@vNode#1
UE#2@vNode#1
UE#1@vNode#2
UE#2@vNode#2eNodeB GPU
Cellular Edge Computing Node
eNB
vNode #1
vNode #2
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The system is developed and implemented based on the OpenAirInterface (OAI) LTE and CUDA GPU computing platforms
Radio Resource Hypervisor
➢ Managing the MAC layer user scheduling and resource allocation (physical resource blocks (PRBs) in LTE network).
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PRB#1 PRB#2 PRB#3 PRB#4 PRB#5 PRB#6
Sub-Carriers
vNode#1,
RR=360kHz
vNode#2
RR=540kHz
vNode#3
RR=180kHz
1 S
ub
frame
#1
Users B
uffers
RLC and upper layers
MAC layer
Physical layers
The Virtual-to-Physical Mapping
#2 #1 #2 #1
Computing Resource Hypervisor
➢ Managing the dispatch of kernel functions (Token-based)
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name threads parameters
Kernel function inCUDA programing:
Kernel 1 (10k threads)
Kernel 2(1k threads)
Kernel 3(5k threads)
Kernel NCal
ling kernels
asyn
chro
no
usl
y
Kernel 1(10k threads)
Kernel 2(1k threads)
Kernel 3(5k threads)
Executing kernels serially in GPU
CPU side
GPU side
Resource Hypervisor: Computing
➢ Methodology: Managing the dispatch of kernel functions (Token-based)
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name threads parameters
Kernel function inCUDA programing:
Kernel 1 (10k threads)
Kernel 2(1k threads)
Kernel 3(5k threads)
Kernel NCal
ling kernels
asyn
chro
no
usl
y
Kernel 1(10k threads)
Kernel 2(1k threads)
Kernel 3(5k threads)
Executing kernels serially in GPU
CPU side
GPU side
Kernel 1(10k threads)
Kernel 2(1k threads)
Kernel 3(5k threads)
Command Queue
Token
Distributed Resource Orchestration
➢ Considering multiple eNodeBs and computing servers
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Radio Access Network Edge Servers
Qiang Liu and Tao Han, “DIRECT: Distributed Cross-Domain Resource Orchestration in Cellular Edge Computing”, ACM International Symposium on Mobile Ad Hoc Networking and Computing(MOBIHOC) 2019.
Algorithm Overview
➢ Controller side: ▪Updating the dual variables and optimize the auxiliary
variable Z (convex problem)
➢ Node side: ▪Optimize the resource allocation X
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System Overview
➢ DIRECT controller: Coordinate the resource allocations to slices across edge nodes (control-side algorithm)
➢ DIRECT agents in edge nodes: Allocate resources to slices using a learning-based algorithm (edge-side algorithm)
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➢ Slice Orchestrator: Dynamically orchestrate virtual network resources to slices across the network
System Overview
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➢ Resource Hypervisor: map virtual resource to physical resources
Radio Resource Hypervisor
Computing Resource Hypervisor
System Implementation
➢ Hardware Details: ▪OpenAirInterface (OAI): 2x USRP B210 SDR boards, 2x
eNodeB computers, 1x Core network▪CUDA GPU computing platform: 2x NVIDIA GTX 1080Ti,
CUDA 8.0▪ Mobile users: 4x Huawei dongle E2273, 4x Linux computers
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GPU 1
GPU 2
Core
Network
eNodeB 1
UE 1@Slice 1
UE 2@Slice 3
UE 3@Slice 2
UE 4@Slice 3
Edge Node 1 with DIRECT Agent
Edge Node 2 with DIRECT Agent
Th
e D
IRE
CT
Contr
oll
er
eNodeB 2
Experiment Results
➢ DIRECT is aware of the traffic load
➢ DIRECT learns the needs of multi-domain resources of slices
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Res
ou
rce
uti
liza
tion
Uplink Computing
Slice 1
10%
20%
30%
40%
50%
60%
Downlink
Slice 2 Slice 30%
Res
ou
rce
uti
liza
tion
Slice 10%
25%
50%
75%
100%
Slice 2 Slice 3
Edge node 1 Edge node 2
(b)(a)
application MAR MAR VAS
Learn the slice traffic on edges
Learn the resource demand of application
Experiment Results
➢ DIRECT converges in a few iterations.➢ DIRECT reduces about 21% system latency as compared
to Static.➢ DIRECT agents can learn the optimal resource allocations
to network slices.
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0 2 4 6 8 102.0
2.5
3.0
3.5
4.0
4.5
5.0
Iteration
Syst
em-l
aten
cy (
s)
Static
DIRECT
2 4 6 8 10Iteration
Gap
0.0
0.1
0.2
0.3
0.4
0
(a) (b)Iteration
Edg
e-la
tency
(s)
0 5 10 15 20 25 30
1.0
1.5
2.0
2.5
Edge node 2
Edge node 1
(c)
21%
61%
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Tao Han
The University of North Carolina at Charlotte
Tel #:704-687-8406, Email: [email protected]
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