Flow-Level Analysis of Load Balancing in HetNets and ... · Flow-Level Analysis of Load Balancing...

Flow-Level Analysis of Load Balancingin HetNets and Dynamic TDD in LTE

Pasi Lassila (joint work with Samuli Aalto,Abdulfetah Khalid and Prajwal Osti)

COMNET DepartmentAalto University, School of ElectricalEngineering

1 / 26HEWINETS seminar, October 24, 2013

Outline

• Introduction to flow-level models

• Overview of 2 problem settings– Load balancing in HetNets (between one macrocell and

one/many microcells)– Intercell coordination between 2 base stations in Dynamic TDD

• Conclusions


What are flow-level models?

• Flow-level models are used to model the system fromthe point of view of elastic data traffic– Elastic data traffic corresponds to, e.g., TCP file transfers– The transmission rate varies during the lifetime of the flow

• Flow-level models are dynamic– Flows arrive according to some stochastic process (Poisson)– Flows have random service requirements (file sizes)– Instantaneous service rate of a flow depends on state of the

system (e.g., number of other flows)– How this dependence is modeled typically contains idealizations


What are flow-level models?

• Performance at the flow level– Performance is expressed as throughput or flow delay (file

transfer delay)– For example, mean flow delay would describe how long file

transfers on the average last

• Importance– Users do not care about delays of individual packets, but only

about the total time to transmit a file of a given size

• Flow-level models characterize the system at the time-scale where users experience the performance


M/G/1-PS queue

• Consider a single link with constant capacity C– In a single server processor sharing (PS) queue the capacity C of

the server is equally shared between the customers in system– If there are n flows in system each receives service at the rate C /n– Flows are served in parallel (there is no queueing!)– Round robin (RR) service can be approximated by PS when

service quantum in RR is small relative to flow durations (cf. timeslot length in 3G/LTE systems)

• M/G/1-PS queueing model– Arrival process is Poisson with intensity l– Flow sizes X obey a general distribution with mean E[X]– Service discipline is PS– Well-known model with many nice analytical properties (robust)


General goal

• Model wireless systems at flow-level– Involves abstractions (simplifications)

• Objective– To learn what queueing/scheduling theory can teach us about

resource allocation and dynamic traffic

HEWINETS seminar, October 24, 2013 6 / 26

Load balancing in HetNets


Scenario

• Single macro cell (index 0)with multiple outband andseparate micro cells(1,…,n)

• No interference betweencells

• Traffic consists of elasticDL data flows

• Resources of each celltime-shared uniformlybetween the active flows

0

2

1…

n


Queueing Model

• Single macro cell (index 0)with multiple micro cells(1,…,n)

• Traffic: elastic DL data flows• Poisson arrivals in each cell• Generally distributed flow

sizes• Cells modeled as M/G/1-PS

servers• Assumption: Micro cells faster

than the macro cell

0

2

1 …n

ii "³ 0mm


Dispatching Policy

• Dispatching policy decidesfor each arriving flow(belonging to any traffic class i)whether it should be served by– the ”local” micro cell i or– the ”global” macro cell 0

• Maximal stability region:

0

2

1 …n

010 }0,max{ mmll <-+å =ni ii


Optimal Dispatching Problem

• Optimal dispatching policyminimizes the mean flowdelay

• Static probabilistic policies– state-independent– analytical/numerical approach– optimal static policy used as a

baseline in performancecomparisons

• Dynamic policies– state-dependent– JSQ, MJSQ, LWL, MP, and FPI– performance evaluation based

on simulations– better performance?

0

2

1 …n


Traffic Scenarios

• Exp flow sizes (2nd experiment: bounded Pareto)• n = 2 (3rd experiment: n = 2,…,10)• m0 = 1• m1 = m2 = 2 (4th experiment: m1 = m2 = 4)

Scenario Fixed Varied

1 (symmetric) l0 = 0 l1 = l2 = l2 (symmetric) l1 = l2 = 2 l0 = l3 (asym.) l0 = 0, l2 = 2 l1 = l4 (asym.) l1 = 1, l2 = 2 l0 = l


Effect of Nmbr of Microcells (Scenario 2)


Conclusions

• All dynamic policies improve significantly the flow-levelperformance compared to the optimal static policy– best performance gain achieved with high load– gain increased when more micro cells

• Among the implemented dynamic policies,– myopic MP appears to be systematically the best;– MP may even be close to optimal in minimizing the mean flow delay;– more robust MJSQ is typically able to achieve almost the same

performance;– FPI policies are not able to give any essential improvements over MJSQ;– classical JSQ typically performs worst

• Performance gain of dynamic polices (except LWL)approximately insensitive with respect to the flow size distribution


Publications

1. A. Khalid, P. Lassila and S. Aalto, Load Balancing ofElastic Data Traffic in Heterogeneous WirelessNetworks, in Proceedings of 25th InternationalTeletraffic Congress (ITC-25), 2013.

2. A. Khalid, Load Balancing of Elastic Data Traffic inHeterogeneous Wireless Networks, Aalto UniversitySchool of Electrical Engineering, 2013, Master'sThesis.


Optimal Intercell coordination inDynamic TDD


Scenario

• Dynamic TDD-LTE– Possible to tune fraction of time used for uplink/downlink in a cell

• Multiple cells– inter-cell interference– downlink-downlink– downlink-uplink– uplink-uplink

• Intercell coordination– Possible to control transmission rate (power) of both cells at fast

time scale


Model (1)(Basic Setting)• 2 neighboring cells

• 2 classes of users per cell/station– uplink and downlink users

• Symmetric service rates (between stations):– UL service rate:– DL service rates:


Model (2)(Interference model)• Uplink power << Downlink power• Low downlink interference

• Service rate vectors


Model (3)

• Traffic assumptions– Poisson arrivals of flows with intensities– Exponential file sizes with mean– Elastic traffic, PS queues with completion rates:

• Four M/G/1-PS queues with coupled service ratesdepending on modes {uu, du, ud, dd}– Objective: Study how much dynamic policies improve

performance (delay) over static (optimal) policies– Literature: MaxWeight policy asymptotically optimal, stochastic

optimality of on/off policy for 2 base stations (but no TDD)


Static policies

• Static (probabilistic) policy: each mode selected withfixed probability– Four independent M/G/1-PS queues

• Maximal stability region

• Optimal static policy– Determine optimal probabilities to minimize delay– Numerically or sometimes explicit result


Dynamic policies

• We define several priority based policies– HU/HD: generalizations of earlier stochastically optimal on/off

policy– E.g., HU is good if uplink rate is high compared with downlink– HU/HD may be unstable sometimes => H3/H4 policies

• Other dynamic policies– Policies based on MDPs (Markov Decision Processes)

• FPI, MDP– Max Weight policy

• MW = Select the mode with the maximum weight• Weight =Σ (service rate) x (queue length)


Stochastic optimality

• Policy p is stochastically optimal if

• Two optimality results


Example from numerical results• Simulations in many scenarios of dynamic policies to

evaluate gain over static optimal policy


Conclusions

• Objective to study achievable gains from dynamiccoordination of two base stations using dynamic TDD

• Priority policies– Can be stochastically optimal but may also run into stability

problems depending on parameters– No unique, robust priority policy

• MW and FPI are robust for all the cases

• In our scenarions dynamic policies perfom better thanthe static optimal policy by as much as 50—60%


Publications

1. P. Osti, S. Aalto and P. Lassila, Optimal dynamic TDDintercell coordination for elastic traffic, 2013,submitted.

2. P. Lassila, A. Penttinen and S. Aalto, Flow-levelmodeling and analysis of dynamic TDD in LTE, inProceedings of Eighth Euro-NF Conference on NextGeneration Internet (NGI 2012), 2012.

3. P. Osti, P. Lassila and S. Aalto, Optimal intercellcoordination for multiple user classes with elastictraffic, in Proceedings of Eighth Euro-NF Conferenceon Next Generation Internet (NGI 2012), 2012.


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