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QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems. Youngki Kim Mobile R&D Laboratory KT, Korea Kyuho Son and Song Chong School of EECS KAIST, Korea IEEE GLOBECOM 2009 proceedings. Speaker : Tsung-Yin Lee. Outline. Introduction

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qos scheduling for heterogeneous traffic in ofdma based wireless systems

QoS Scheduling for Heterogeneous Traffic in OFDMA-based Wireless Systems

Youngki Kim Mobile R&D Laboratory KT, Korea

Kyuho Son and Song Chong School of EECS KAIST, Korea

IEEE GLOBECOM 2009 proceedings.

Speaker:Tsung-Yin Lee

outline
Outline
  • Introduction
  • Model Description and Problem Formulation
  • Proposed QoS Scheduling Framework
  • Simulation Result
  • Conclusions
introduction
Introduction
  • the key access technologies in current and next generation wireless systems is OFDMA
    • Packet scheduling plays an important role in QoS provisioning by providing mechanisms for the resource allocation
paper goal
Paper Goal
  • provide QoS guarantee to the real-time traffic in multi-carrier wireless systems
  • utility maximization of the non real-time traffic while providing QoS guarantee to the real-time traffic
  • balance between QoS guarantee and utility maximization in a simple and organized manner
model description 1 2
Model Description (1/2)
  • Paper denote by S the set of all sub-channels in the system, NRTand NNRT, the set of all real-time (RT) and non real-time (NRT) flows
  • RT flow, VoIP or MPEG, has its own QoS parameters such as maximum latency
  • NRT flow has no explicit QoS parameters
model description 2 2
Model Description (2/2)
  • In the system, at each slot, the proposed scheduler determines the sub-channel assignment based on each flow’s current channel quality, minimum average throughput and individual packet deadline

Only Consider Downlink

problem formulation
Problem Formulation
  • Proposed scheduling framework that maximizes the weighted sum rate of non real-time flows while maintaining QoS constraints of real-time flows in each time slot with the equal power allocation assumption
problem formulation 1 3
Problem Formulation (1/3)
  • the derivative of utility function of flow i
  • U’i(・) is used as a weight
  • μij(t) is the achievable channel capacity when sub-channel j is assigned to flow i at time slot t
problem formulation 2 3
Problem Formulation (2/3)
  • is the long-term throughput for flow i up to time slot t
  • δij(τ) is the 0-1 indicator of allocating the sub-channel j to the flow i or not
  • OFDMA constraint :
problem formulation 3 3
Problem Formulation (3/3)
  • θi(t) is the actual amount of data allocated to real-time flow i at time slot t andπi(t) is given by :
  • Mi is the minimum required average traffic rate of real-time flow i
  • is the maximum possible data rate of real-time flow i at time slot t
  • properly based on the newly introduced beta deadline parameter
beta deadline parameter 1 2
Beta Deadline Parameter (1/2)
  • urgent scheduling : which only considers the most urgent packets (required data rate is 6)
  • strict priority scheduling : provide higher priority to the real-time traffic than non real-time traffic (required data rate is 18)
  • paper may take a policy somewhere between these two extreme cases
beta deadline parameter 2 2
Beta Deadline Parameter (2/2)
  • lik is the length of the k-th packet of flow i
  • eik is the time to expire value of the k-th packet of flow i
  • Qi is the total number of packets of real-time flow i at time slot t
real time qos scheduling
Real-time QoS Scheduling
  • Paper can formulate the following maximum weighted bipartite matching (MWBM) problem to find the sub-channel allocation matrix
  • : the number of sub-channels to be assigned to flow i at time slot t
  • : the average sub-channel capacity of the flow i
definitions
Definitions

Matching

Free Vertex

definitions1
Definitions
  • Maximum Matching: matching with the largest number of edges
definition
Definition
  • Note that maximum matching is not unique.
alternating path
Alternating Path
  • Alternating between matching and non-matching edges.

a

c

d

e

b

f

h

i

j

g

d-h-e: alternating path

a-f-b-h-d-i: alternating path starts and ends with free vertices

f-b-h-e: not alternating path

e-j: alternating path starts and ends with free vertices

slide19
Idea
  • “Flip” augmenting path to get better matching
  • Note: After flipping, the number of matched edges will increase by 1!

idea of algorithm
Idea of Algorithm
  • Start with an arbitrary matching
  • While we still can find an augmenting path
    • Find the augmenting path P
    • Flip the edges in P
labelling algorithm
Labelling Algorithm
  • Start with arbitrary matching
labelling algorithm1
Labelling Algorithm
  • Pick a free vertex in the bottom
labelling algorithm2
Labelling Algorithm
  • Run Breadth-first search (BFS)
labelling algorithm3
Labelling Algorithm
  • Alternate unmatched/matched edges
labelling algorithm4
Labelling Algorithm
  • Until a augmenting path is found
repeat
Repeat
  • Pick another free vertex in the bottom
repeat1
Repeat
  • Run BFS
answer
Answer
  • Since we cannot find any augmenting path, stop!
weighted matching
Weighted Matching

Score: 6+3+1=10

3

4

6

6

maximum weighted matching
Maximum Weighted Matching

Score: 6+1+1+1+4=13

3

4

6

6

augmenting path change of definition
Augmenting Path (change of definition)
  • Any alternating path such that total score of unmatched edges > that of matched edges
  • The score of the augmenting path is
    • Score of unmatched edges – that of matched edges

3

4

6

6

Note: augmenting path need not start and end at free vertices!

detailed procedure
Detailed Procedure
  • the result of MWBM algorithm using average sub-channel capacity cannot give exact number of sub-channels to the flows
non real time qos scheduling
Non-Real-time QoS Scheduling
  • general utility function is defined for α ≥ 0
    • α = 0 : maximum throughput
    • α = 1 : proportional fairness
    • α = ∞ : max-min fairness
      • the minimum data rate that a dataflow achieves is maximized; secondly, the second lowest data rate that a dataflow achieves is maximized, etc
simulation environment
Simulation Environment
  • VoIP traffic is based on G.711 codec standard and generates each VoIP packet every 20 ms, with 160-byte data
  • Video streaming traffic has more bursty nature because packet size can be different according to the codec rate such as MPEG-FGS
beta deadline parameter characteristics of voip traffic
Beta deadline parameter characteristics of VoIP traffic
  • beta = 0 : strict priority
  • beta = inf : urgent scheduling
burst traffic response
Burst traffic response
  • During the 2000 time slot and 3000 time slot, offered traffic rate increases up to 150% of the average traffic rate.
  • During the 7000 time slot and 8000 time slot, offered traffic rate increases to 300%
  • beta = 0 : strict priority
  • beta = inf : urgent scheduling
conclusions
Conclusions
  • The proposed scheduling algorithm (beta deadline parameter)satisfies the QoS requirements of the real-time traffic and maximizes the utility of the non real-time traffic while utilizing the system resources efficiently