1. Load Balancing of Elastic Traffic in Heterogeneous Wireless Networks Abdulfetah Khalid, Samuli Aalto and Pasi Lassila 23.01.2013

2. Outline Introduction Statement of the research problem Optimal static (probabilistic) allocation Dynamic policies 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 (p i *) –the rest to macro cell with prob. (1- p i *) Objective: is to find this optimal probability values so that the mean flow delay is minimized

8. Analytical approach: optimal probabilistic allocation Given arrival rates, λ i, and mean service rates, µ i, Mean flow delay is minimized by finding optimal allocation probabilities, p i * For probabilistic allocation the mean flow delay, E[T], is given by

9. Analytical approach: optimization problem It can be stated as a mathematical optimization problem of the form Since the objective function, E[T], and constraints are convex Optimization problem is treated as convex optimization problem So, convex optimization techniques are used

10. Dynamic policies JSQ: Join the shortest queue –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 arriving flows to the server with least additional cost. –additional cost =additional delay in the system experienced by all flows

11. Simulation: Two server case Assumptions –Two microcells Dedicated arrivals to macrocell (λ 0 ) flexible arrivals to microcells (λ 1 and λ 2 ) –Service rate of microcells (µ 1 and µ 2 ) is larger than macrocell (µ 0 ) –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 Two microcells –No dedicated arrivals to the macrocell With service rate µ 0 =1 –Variable and identical arrival rates to both microcells with Arrival rates λ 1 = λ 2 = λ Service rates µ 1 =µ 2 = 2

13. Simulation results: Symmetric traffic scenario Ratio of the number of flows in the system between the dynamic and base line optimal static policies bounded Pareto distributed flowsexponentially distributed flows  =2

14. Asymmetric traffic scenario Two microcells –Dedicated arrivals to macrocell with With variable arrival rate λ 0 = λ Service rate µ 0 =1 –Constant and variable arrival rates macrocells Arrival rates λ 1 =1 and λ 2 = 2 Symmetric Service rates µ 1 =µ 2 = 2

15. Simulation results: Asymmetric traffic scenario bounded Pareto distributed flows exponentially distributed flows Ratio of the number of flows in the system between the dynamic and base line optimal static policies  =2

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

17. Simulation results: Effect of flow size variation bounded Pareto distributed flows exponentially distributed flows bounded Pareto distributed flows  =2  =3  =1.5

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. Thank You ! Any Comments or Questions?