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A Practical Traffic Management System for Integrated LTE-WiFi Networks Zhuoran Li 11/4/2015.

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Presentation on theme: "A Practical Traffic Management System for Integrated LTE-WiFi Networks Zhuoran Li 11/4/2015."— Presentation transcript:

1 A Practical Traffic Management System for Integrated LTE-WiFi Networks Zhuoran Li 11/4/2015

2 Overview Problem: – With the data traffic increasing fast, operators start to deploy Wi-Fi for additional capacity, therefore how to manage the network interface for user flows is important.

3 Drawbacks of current solution Naïve policies: in most cases, the connection managers are on the user device. (User decides to connect to WiFi AP or basestation.) Static decision: interface selection isn’t adaptive to dynamic conditions of wireless networks. Coarse-grained policies: interface selections based on the user-level. However, application- level based selection will be better.

4 Their Idea: ATOM LTE Core-Network WiFi Gateway Traffic Manager Maps user flows to appropriate network(LTE/WiFi) Centralized management -> Efficient use of network resources Reduces backhaul costs -> Facilitates dynamic traffic mgmt Operates for each LTE cell -> Scalable Standards agnostic -> Easily Deployable PDN-gateway MME Serving-gateway Network Interface Assignment Switching Service

5 Components  Network Interface Assignment Algorithm (NIA) – Goal: Dynamically maps user traffic flows to appropriate LTE basestation or WiFi AP Interface switching service (ISS) – Goal: Switch current user flows from WiFi AP to LTE or vice versa based on decisions from NIA

6 Component 1: NIA 6

7  Consider an LTE cell and multiple WiFi APs in its coverage area Assign basestation/ AP to each flow – Maximize sum of users flows’ QoE QoE captured using “utility” – Weighted PF provides differential QoE Pricing function supports 2 models – Based on data usage – Based on price/byte Problem Formulation 7 Weight Throughput Network Pricing

8 Network Interface Assignment ( NIA) Problem is NP-Hard – Including the simplest topology of an LTE cell and a WiFi AP NIA is a two-step greedy heuristic – Considers each AP-basestation in isolation – Fixes assignment for AP that maximized incremental utility – Iterate till all hotspots are covered – Complexity is O(B 2 N), where N = # flows, B = # APs 8

9 NIA algorithm

10 Component 2: Interface Switching Service

11 Interface Switching Service (ISS)

12 ISS They focus on the HTTP-based traffic flows. – HTTP-based downloads: go through core-network, connecting with the HTTP Proxy in the Core- Network. – HTTP-based video streaming and browsing: for this traffic, they design it go directly to the Internet. – Non HTTP-traffic: go through the core network.

13 Evaluation In WiFi AP1, 4 users steam YouTube videos of average bit- rate 1.5 Mbps while 1 user downloads a large-file. In WiFi AP2, 2 users stream YouTube videos of average bit-rate 1.5 Mbps while 2 users download large-files. Both the LTE users stream YouTube videos of average bit-rate 1.5 Mbps. We compare the throughput of WiFi users for the case with and without ATOM (NO-ATOM). With ATOM, User #3 and user #6 are moved to LTE-basestation.

14 Evaluation There are 4 users streaming YouTube videos of average bit-rate of 1.5 Mbps. Users#1, 2 and 3 are within WiFi coverage while User#4 can only access the eNodeB. We introduce 2WiFi users with background traffic at around 30 seconds into the experiment such that the flows are active for about 40 seconds

15 Benchmarking the ISS Measured the time taken for flows to switch using ISS: HTTP based video streaming flows Hulu (uses HTTP-DASH) v/s Youtube(uses HTTP-PD) Insight: Switching time improves with DASH streaming DASH flows use smaller chunk sizes to ensure adaptive-ness to changing network conditions

16 My understanding Pros: – The most important contribution of this work is that they build a real system. For interface selection, a lot of searcher provides a lot of algorithm, however, this is the first one to build the real system. – To verify their system, they build a small testbed and also did a large-scale simulation. The combination of real testbed and simulation can convince people about their system.

17 My understanding My questions: – Is that possible for a mobile device to send data though both cellular and wifi? If that is possible, why a user have to choose one interface not transferred at both? – They didn’t say clearly if they make the HTTP- based video streaming go directly to the Internet, how the charge this data?

18 Thanks!


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