23.01.2013

1. Load Balancing of Elastic Traffic in Heterogeneous Wireless NetworksAbdulfetah Khalid, Samuli Aalto and PasiLassila 23.01.2013

2. Outline • Introduction • Statement of the research problem • Optimal static (probabilistic) allocation • Dynamicpolicies • Simulation results • Conclusions

3. LTE Advanced: Heterogeneous Networks

4. 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

5. 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

6. 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

7. 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

8. 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*

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. Simulation results: Effect of number of microcells exponentially distributed flows bounded Pareto distributed flows a=2

17. 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

18. 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

19. 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,..

20. ThankYou ! AnyCommentsorQuestions?