Rethinking LTE Network Function Virtualization Muhammad Taqi Raza*¶, Dongho Kim§, Kyu-Han Kim§, Songwu Lu* and Mario Gerla* * Computer Science Department, UCLA § Hewlett Packard Labs ¶ Student and corresponding author.

Network Function Virtualization (NFV) NFV replaces dedicated network functions (NFs) with software running on commercial commodity servers. Dedicated Intrusion Detection Dedicated Firewall Dedicated Load Balancer Commodity Intrusion Detection Commodity Firewall Commodity Load Balancer Advantages Low Cost Reduces capital and operational expenditures Flexibility Network functions can be chained dynamically Scalability Network functions are quickly scaled up and down

LTE – NFV: A Leading Use Case of NFV A large variety of proprietary LTE NFs negatively impact efficiency  NFV moves away from propriety boxes and improves efficiency Launching services requires another variety of box to be integrated  Launching service in NFV is as easy as upgrading a software Operations are slow and expensive  NFV provides automated and agile solutions to scale network services

Virtualization Platform Traditional Way of NFV Multiple NF instances are created to meet greater subscribers demands NFV dynamically selects NF for packet processing: Dynamically selects NF instance, and Dynamically routes the network packets Virtualization Platform

Problem: Traditional Way of NFV in LTE LTE NFs are not Internet Middleboxes Subscriber Runtime NF Delay Reroute Affinity Selection Sensitive Flows Internet Middleboxes LTE NFs

Achieving: LTE Way of NFV Different LTE events require different treatment  Treat LTE events on their merit LTE signaling packets drive performance  Prioritize delay sensitive signaling messages  Parallelize LTE signaling messages Must meet standardized conformance and interoperability requirements  Ensure in-order execution of signaling messages LTE NFs are logically separated  Combine logic of alike NFs

LTE Way of NFV: How We Do ? + Logic based NF decomposition instead of instance based NF decomposition Instance based NFV (traditional way) LTE Serving Gateway Packet Dedicated Commodity boxes boxes Logic based NFV (LTE way) Dedicated Commodity boxes boxes Event 1 Logic LTE Serving Gateway Packet + Event 2 Logic

LTE Way of NFV: How We Do ? A number of signaling messages exchange between distributed LTE NFs Logic based NFV (LTE way) MME VNF SGW VNF PGW VNF Event Input LTE Core Network Chaining for normal event MME: Mobility Management Entity VNF: Virtualized NF SGW: Serving Gateway PGW: Packet Gateway

LTE Way of NFV: How We Do ? Delegating LTE event execution to Fat-Proxy Mission critical events are delegated to Fat-Proxy Logic based NFV (LTE way) MME VNF SGW VNF PGW VNF HoM VNF SP VNF Check Event Input LTE Core Network Fat-Proxy Tier Chaining for normal event MME: Mobility Management Entity VNF: Virtualized NF SGW: Serving Gateway PGW: Packet Gateway HoM: Handover Management event SP: Service provisioning event Chaining for mission-critical event

LTE Way of NFV: How We Do ? Parallelizing mutual exclusive signaling messages MME (LTE NF) communication with base station and serving gateway is mutually exclusive Logic based NFV (LTE way) MME Core Function GTP S1-AP SGW LTE base station Mobility Management Entity (MME) GTP: GPRS Tunneling protocol S1-AP: S1 Application Protocol

Challenge 1: Functional Decomposition Fat-Proxy implements event specific logic The NF source code is shared among a number of LTE events. Challenge 1-1: Determine what software functions are shared among multiple events ? Challenge 1-2: Determine indirect dependencies

Solution C1-1: Functional Decomposition Decomposing event specific functions from the source code Generating function call graph to determine functional dependency Different software functions chain differently based on the event logic: Modify Bearer Req. Create Session Req. Location Update procedure Modify Bearer Resp. Create Session Resp. Handover procedure

Solution C1-2: Functional Decomposition Global variable usage as a reason for functional dependency Example below: Variables ‘bearer’ and ‘imsi’ values are modified by some other functions PathSwitchRequest() is dependent on MapIMSI.find() PathSwitchRequest() is dependent on GetEPSBearer() function PathSwitchRequest (enbUeS1Id, mmeUeS1Id) { // get IP address of UE by removing header … // find corresponding UeInfo address imsi = MapIMSI.find (ueAddr); //get UE corresponding eps bearer bearer = GetEPSBearer(); }

Challenge 2: Event Logic Extraction Extract critical event execution logic from LTE core NFs Combine extracted logic as that event’s Fat-Proxy Challenge 2-1: Resolving logic and data dependencies Challenge 2-2: Resolving event execution dependencies

Solution 2-1: Event Logic Extraction Resolving event execution dependencies Identifying logic dependencies through Common Subgraph Isomorphism Offline process through improved back-tracking algorithm Handover procedure Location Update procedure Handover Required Authentication Req. Create Session Req. Modify Bearer Req./Resp Authentication Resp. Create Session Resp. Context Ack. FWD Relocation Create Session Req. Create Session Resp. Dependent Modify Bearer Req.

Solution 2-2: Event Logic Extraction Resolving event execution dependencies Device Powers on Location Req. initiated Locat. Upd. accepted/ rejected Locat. Upd. requested Registered initiated Registered Attach accepted Srvc Req. initiated Service Req. accepted/failed Service Req initiated

Challenge 3: Logic-based Partitioning Speed-up event execution by executing some messages in parallel Challenge 3-1: By design serial execution of signaling messages at LTE core GTP: GPRS Tunneling protocol S1-AP: S1 Application Protocol

Solution 3-1: Logic-based Partitioning Partition the mutually exclusive logic of different protocols Only execute those messages in parallel which: Belong to two different protocols Are mutual exclusive Base station Source-MME Target-MME Source-SGW Target-SGW Handover required FWD Allocation Handover Request Create Session Req. Handover ACK Session Response FWD Allocation Resp. Handover Command Create Indirect data FWD tunnel eNodeB Status Transfer FWD tunnel resp. GTP: GPRS Tunneling protocol S1-AP: S1 Application Protocol Messages executed in sequence S1AP Protocol messages executed in parallel GTP Protocol messages executed in parallel

Implementation and Evaluation LTE base station: (nanoLTE Access Point) LTE Core network: OpenEPC software platform Device: Samsung S6 smartphones Virtualization: VmWare’s vSpehere Intel Xeon E5 - 2650 v3 processors at 2.3Ghz

Event Execution Time Event execution being local to Fat-Proxy instance speeds-up different events Paging event: Most packets are exchanged between LTE core and LTE base station Not much improvement in Paging event through Fat-Proxy

Event Execution Time Event execution is diverted to Fat-Proxy Less number of packets exchange at LTE core Service Request event: Bearer modifications at actual SGW and PGW which relatively increases LTE core signaling Not much improvement in Paging event through Fat-Proxy

Conclusion Made the first effort in providing logic based decomposition in Virtualized EPC Design leverages LTE domain knowledge to extract the event logic By using domain-specific knowledge in LTE, our design does not require any LTE standard violation We seek for plug and play solution to work with any carrier network In future work, we will focus on service availability (fault tolerance), LTE core platform (for centralized control) and security issues in LTE–NFV