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Load Balancing of Elastic Traffic in Heterogeneous Wireless Networks Abdulfetah Khalid, Samuli Aalto and Pasi Lassila. 23.01.2013. Outline . Introduction Statement of the research problem Optimal s tatic (probabilistic) allocation Dynamic policies Simulation r esults Conclusions.

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Load Balancing of Elastic Traffic in Heterogeneous Wireless NetworksAbdulfetah Khalid, Samuli Aalto and PasiLassila

23.01.2013

outline
Outline
  • Introduction
  • Statement of the research problem
  • Optimal static (probabilistic) allocation
  • Dynamicpolicies
  • Simulation results
  • Conclusions
heterogeneous server model
Heterogeneous server model

Assumptions:

  • A single macro-cell
  • n microcells
  • Poisson arrival process of elastic flows (such as TCP downloads)
  • General flow size (service requirement) distribution
  • Single cell modeled as Processor Sharing(PS) queue
research problem
Research problem

How to balance the traffic load between a macrocell and microcells?

Target: To find an optimal load balancing policy which minimizes the mean flow level delay

Mean flow delay implies how long it, on average, takes to transfer a file

load balancing policies
Load balancing policies

Apply dispatching (load balancing) policy

Optimal Static Policy

  • Analytical approach
  • State independent policy
  • Used as a base line to compare the performance of other policies

Dynamic Policies

  • State dependent policy
  • Reacts to instantaneous changes in the system
  • JSQ, Modified JSQ, LWL, Myopic
  • Simulations used to study performance
analytical approach optimal probabilistic allocation
Analytical approach: optimal probabilistic allocation

Allocating the incoming arrivals to

  • the micro cells with optimal probability (pi*)
  • the rest to macro cell with prob. (1- pi *)

Objective: is to find this optimal probability values so that the mean flow delay is minimized

analytical approach optimal probabilistic allocation1
Analytical approach: optimal probabilistic allocation
  • For probabilistic allocation the mean flow delay, E[T], is given by
  • Given arrival rates, λi, and mean service rates, µi,
    • Mean flow delay is minimized by finding optimal allocation probabilities, pi*
analytical a pproach optimization problem
Analytical approach: optimization problem
  • Since theobjective function, E[T], and constraints are convex
    • Optimization problem is treated as convex optimization problem
    • So, convex optimization techniques are used

Itcanbestated as a mathematicaloptimizationproblem of the form

dynamic policies
Dynamicpolicies

JSQ: Join the shortestqueue

allocate arriving flows to server with fewest # jobs

MJSQ: Modified join the shortest queue

the # of flows in the server is scaled with the service rate of server

LWL: Least work load

dispatch arriving flows to server with least work load

MP: Myopic

allocate the arrivingflowsto the server with least additional cost.

additional cost =additional delay in the system experienced by all flows

simulation two server case
Simulation: Two server case
  • Assumptions
    • Twomicrocells
      • Dedicated arrivals to macrocell (λ0)
      • flexible arrivals to microcells (λ1 and λ2)
    • Service rate of microcells (µ1 and µ2) is larger than macrocell (µ0)
    • Performance is studied for
      • both exponentially distributed and
      • bounded Pareto distributed flows
        • Used to model traffic that consists of heavy-tailed flow sizes
simulation symmetric traffic scenario
Simulation: Symmetric traffic scenario
  • Twomicrocells
    • No dedicated arrivals to the macrocell
      • With service rate µ0 =1
    • Variable and identical arrival rates to bothmicrocells with
      • Arrival ratesλ1 = λ2 = λ
      • Service rates µ1 =µ2 = 2
simulation results symmetric traffic scenario

exponentially distributed flows

bounded Pareto distributed flows

a=2

Simulation results: Symmetric traffic scenario

Ratio of the number of flows in the system between the dynamic and base line optimal static policies

asymmetric traffic scenario
Asymmetric traffic scenario
  • Twomicrocells
    • Dedicated arrivals to macrocell with
      • Withvariablearrivalrateλ0 = λ
      • Service rate µ0 =1
    • Constant and variable arrival rates macrocells
      • Arrivalratesλ1 =1 and λ2 = 2
      • Symmetric Service rates µ1 =µ2 = 2
simulation results asymmetric traffic scenario
Simulation results: Asymmetric traffic scenario

bounded Pareto distributed flows

a=2

exponentially distributed flows

  • Ratio of the number of flows in the system between the dynamic and base line optimal static policies
simulation results effect of number of microcells
Simulation results: Effect of number of microcells

exponentially distributed flows

bounded Pareto distributed flows

a=2

simulation results effect of flow size variation
Simulation results: Effect of flow size variation

bounded Pareto distributed flows

bounded Pareto distributed flows

a=1.5

a=2

bounded Pareto distributed flows

a=3

exponentially distributed flows

conclusions
Conclusions
  • As expected, dynamic policies perform better than the optimal static policy
  • MP and MJSQ were best policies
  • Highest performance gain is achieved when the load of the system is high
  • Implemented dynamic policies show near insensitivity property to the flow size variation
    • Except the LWL policy
      • Its performance gain decreases as flow size variation increases.
  • Similar performance gain was achieved with MP and MJSQ
    • Most striking observation
    • MJSQ is a robust policy
future work
Future work
  • Study the system performance considering the arrival process to consist of both elastic and streaming flows
    • Only elastic flows was considered
  • Modifying the basic model used in the thesis
    • Specify the service rate of the servers from radio model
  • Is it possible to optimize the implemented policies?
    • with the help of Markov Decision Process (MDP)
  • Study system performance with other metrics
    • Only single metric was considered, i.e mean flow level delay
    • Fairness, throughput,..
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ThankYou !

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