1. On the Statistical Multiplexing Gain of Virtual Base Stations Pools Lewis (Jingchu) LIU, Sheng ZHOU, Jie GONG, Zhisheng Niu, Tsinghua University, Beijing, China Shugong XU Intel Labs, Beijing, China

2. Contents Background and motivation Session-level model for VBS pools Blocking probabilities Statistical multiplexing gain Numerical results Summary 2Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

3. Background and Motivation Challenges to cellular networks Interference in HetNets and Small Cell settings High operational costs and low resource utilization C-RAN is emerging as the new RAN architecture [CRAN][NGMN][WNC] Baseline structure: distributed RRU, centralized BBU Tech. advantages: simplified BS cooperation Economic advantage: reduced site expenditure (rent, electricity, site visit) GPP based BS virtualization and pooling [CIQ][VBS][BigStation] Statistical multiplexing gain by baseband consolidation Flexible in adding new functionalities and provisioning computational resources for, more complex baseband processing Fronthaul challenge in C-RAN Bandwidth: 1.25Gbps / 20Mhz LTE AxC [CPRI], each site has multiple A and C Delay: 4ms deadline of HARQ in LTE Thus, centralization cost of C-RAN is huge (In sheer contrast with Internet Cloud) 3 BS = Base Station GPP = General Purpose Platform AxC = Antenna x Carrier Need to balance the gains and costs of centralization! Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

4. Background and Motivation Why model dynamics of VBS pools? Tell the pooling gain under different pool size, traffic load, etc. Can be incorporated with cost models to evaluate tradeoffs Previous models [Gomez’13] Dynamic resource management (per task) algorithm is not realistic; semi-dynamic (per session) resource management is more realistic Not consider the constraint of radio resource, which is equally important in VBS pool to computational resources Our proposal Captures the session-level dynamics in VBS pools Assumes session-by-session resource scheduling Reflects both radio and computational resources constraints 4 To study the tradeoffs, first we should have a model… Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

5. Session-Level Model for VBS Pools - Model basics 5 Radio – computation double constraint Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

6. Session-Level Model for VBS Pools - Equivalence to Markov chain Multi-dimensional Markov process Arrival and service is memory-less (a.k.a Markovian) Pool state vector (# of sessions) is a multi-dimensional Markov chain Possible states due to double constraints Birth-and-death state transition 6 Only neighboring states has non-zero transitions rate Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

7. Session-Level Model for VBS Pools - Equivalence to Markov chain, cont‘d 2-Dimensional Example (M = 2, K = 3, N = 4) Intuition: possible states lies in the remaining part of a M-dimensional cube after its N-corner is cut off. 7Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

8. Session-Level Model for VBS Pools - Model solution Stationary distribution (see paper for detail) Local balance equation holds (due to reversibility) Product-form stationary distribution ! 8Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

9. Blocking Probabilities - Straight-forward evaluation Significance of blocking probabilities QoS is reflected ONLY by blocking probabilities in our model Blocking events break up Computational blocking B c : Due to in-sufficient c-servers Radio blocking B r : Due to in-sufficient r-servers, but not c-servers Straight-forward calculation 9 Exhaustive summation, exponential complexity, intractable! Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

10. Blocking Probabilities - Recursive evaluation First, define two auxiliary functions… which we can use to express blocking probabilities 10Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

11. Blocking Probabilities - Recursive evaluation These functions can be evaluated recursively Now the complexity is quadratic to the pool size! 11Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

12. Statistical Multiplexing Gain - Large Pool Limit Asymptotic resource utilization ratio in large pools 12Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

13. Numerical Results - Knee Point Effect 13 Knee point effect, knee point is the minimum allowable amount of computational resources. Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

14. Numerical Results - Decreasing Marginal Pooling Gain 14Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

15. Numerical Results - Influence of traffic and QoS 15 Larger traffic pushes curve to the right; Stricter QoS pushes curve down Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

16. Future Work Pooling gain approximation (Closed-form, other traffic) Verification of Markov Assumption (Exp. Service Time) Dynamics in other level (Baseband Task Processing) 16Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

17. Conclusion Multi-dimensional Markov model Captures the session-level dynamics in VBS pool Reflects the interaction btw. radio and computational resources Model Solutions Exist product-form stationary distribution Recursive method to evaluate QoS (P b ) Simple large pool limit Numerical Results and Discussion “Knee” point effect Decreasing marginal pooling gain Lighter traffic or stricter QoS = Larger pooling gain 17Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

18. Thanks Thanks! Questions? 18Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

19. Reference 19 [CRAN] China Mobile, “C-RAN: The road towards green RAN,” 2011. [NGMN] NGMN Alliance, “Suggestions on potential solutions to C-RAN,” 2013. [WNC] Y. Lin, L. Shao, Z. Zhu, Q. Wang, and R. K. Sabhikhi, “Wireless network cloud: Architecture and system requirements,” IBM Journal of Research and Development, vol. 54, no. 1, pp. 4–1, 2010. [CIQ] S. Bhaumik, S. P. Chandrabose, M. K. Jataprolu, G. Kumar, A. Muralidhar, P. Polakos, V. Srinivasan, and T. Woo, “CloudIQ: a framework for processing base stations in a data center,” in Proceedings of the 18 th annual international conference on Mobile computing and networking. Istanbul, Turkey: ACM, 2012, pp. 125–136, 2348561. [VBS] Z. Zhu, P. Gupta, Q. Wang, S. Kalyanaraman, Y. Lin, H. Franke, and S. Sarangi, “Virtual base station pool: towards a wireless network cloud for radio access networks,” in Proceedings of the 8th ACM International Conference on Computing Frontiers. ACM, 2011, p. 34. [BigStation] Q. Yang, X. Li, H. Yao, J. Fang, K. Tan, W. Hu, J. Zhang, and Y. Zhang, “BigStation: enabling scalable real-time signal processing in large mu-mimo systems,” in Proceedings of the ACM SIGCOMM 2013 conference. Hong Kong, China: ACM, 2013, pp. 399–410, 2486016. [CPRI] CPRI Cooperation, “CPRI specification v6.0; interface specification,” 2013. [Gomez’13] I. Gomez-Miguelez, V. Marojevic, and A. Gelonch, “Deployment and management of sdr cloud computing resources: problem definition and fundamental limits,” EURASIP Journal on Wireless Communications and Networking, vol. 2013, no. 1, pp. 1–11, 2013. Lewis LIU, liu-jc12@mails.tsinghua.edu.cn

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