Scaling the LTE control-plane for future mobile access

In addition to growth of data traffic, mobile networks are bracing for a significant rise in the control-plane signaling. While a complete re-design of the network to overcome inefficiencies may help alleviate the effects of signaling, our goal is to improve the design of the current platform to better manage the signaling. To meet our goal, we combine two key trends. Firstly, mobile operators are keen to transform their networks with the adoption of Network Function Virtualization (NFV) to ensure economies of scales. Secondly, growing popularity of cloud computing has led to advances in distributed systems. In bringing these trends together, we solve several challenges specific to the context of telecom networks. We present SCALE - A framework for effectively virtualizing the MME (Mobility Management Entity), a key control-plane element in LTE. SCALE is fully compatible with the 3GPP protocols, ensuring that it can be readily deployed in today's networks. SCALE enables (i) computational scaling with load and number of devices, and (ii) computational multiplexing across data centers, thereby reducing both, the latencies for control-plane processing, and the VM provisioning costs. Using an LTE prototype implementation and large-scale simulations, we show the efficacy of SCALE.

[1]  Xin Jin,et al.  SoftCell: scalable and flexible cellular core network architecture , 2013, CoNEXT.

[2]  Sneha Kumar Kasera,et al.  Towards understanding TCP performance on LTE/EPC mobile networks , 2014, AllThingsCellular '14.

[3]  Prashant Malik,et al.  Cassandra: a decentralized structured storage system , 2010, OPSR.

[4]  Wenfei Wu,et al.  SoftMoW: Recursive and Reconfigurable Cellular WAN Architecture , 2014, CoNEXT.

[5]  Fabio Pianese,et al.  DMME: A distributed LTE mobility management entity , 2012, Bell Labs Technical Journal.

[6]  Feng Qian,et al.  A close examination of performance and power characteristics of 4G LTE networks , 2012, MobiSys '12.

[7]  K. K. Ramakrishnan,et al.  Load Balancing of Heterogeneous Workloads in Memcached Clusters , 2014, Feedback Computing.

[8]  Lusheng Ji,et al.  A first look at cellular machine-to-machine traffic: large scale measurement and characterization , 2012, SIGMETRICS '12.

[9]  Feng Qian,et al.  Periodic transfers in mobile applications: network-wide origin, impact, and optimization , 2012, WWW.

[10]  Jeffrey C. Mogul,et al.  Remote Direct Memory Access (RDMA) over IP Problem Statement , 2005, RFC.

[11]  David R. Karger,et al.  Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web , 1997, STOC '97.

[12]  Tony Tung,et al.  Scaling Memcache at Facebook , 2013, NSDI.

[13]  Swaminathan Sivasubramanian,et al.  Amazon dynamoDB: a seamlessly scalable non-relational database service , 2012, SIGMOD Conference.