Traffic Offloading and Wireless Edge Networks: Theory and Novel - PowerPoint PPT Presentation

Traffic Offloading and Wireless Edge Networks: Theory and Novel Realizations Leandros Tassiulas Yale University WiOpt, Paris, May 2017.

A New Era in Wireless Networking • Recent developments: • Mobile data traffic growth, new services & advanced devices, 5G vision. • Challenges for cellular networks: • Accommodate the growing traffic. • Support emerging 5G services. • Traditional network expansion methods: • Upgrading technology, acquiring new spectrum, deploying more cells, ... ... are costly and time-consuming solutions. • Our approach : • Explore methods that aim to fully utilize (i) existing spectrum allocations and (ii) idle user-owned wireless infrastructure .

Outline • Mobile data offloading . • Use Wi-Fi capacity to serve cellular traffic. • User Provided Networks (UPNs) . • Facilitate multi hop wireless access through exchange of wireless resources. • Prototype realization based on mobile SDN. • Resource exchange markets in networks . • An Arrow-Debreu type formulation for networks.

Mobile Data Offloading Offloading can be realized over femtocells or Wi-Fi access points. • Goal: reduce network costs (OpEx) & accommodate more traffic. • The wireless spectrum or the wired link are not owned by the operator.

Potential and Key Question • Mobile network operators have adopted such solutions: • AT & T had deployed 32,000 Wi-Fi hotspots by 2012. • T-Mobile and other operators, collaborate with FON. • Republic Wireless, Google Fi, etc. • Offloading benefits depend on the availability of APs’. • How to increase this availability? • Our proposal for operators: lease idle user-owned Wi-Fi APs. • Residential Wi-Fi APs are often underutilized. • On-demand & low-cost network capacity expansion. • Fully aligned with 5G design principles.

Mobile Data Offloading Markets • Related Publications: • G. Iosifidis, L. Gao, J. Huang, L. Tassiulas, An Iterative Double Auction for Mobile Data Offloading , IEEE WiOpt, 2013, ( Best paper Award ), IEEE/ACM Trans. on Networking, 23(5), 2015. • K. Poularakis, G. Iosifidis, L. Tassiulas, Deploying Carrier-grade WiFi: Offload Traffic, Not Money , ACM Mobihoc, 2016. • L. Gao, G. Iosifidis, J. Huang, L. Tassiulas, D. Li, Bargaining-Based Mobile Data Offloading , IEEE JSAC, SI on 5G, 32(6), 2014. • A. Apostolaras, G. Iosifidis, K. Chounos, T. Korakis, L. Tassiulas, A Mechanism for Mobile Data Offloading to Wireless Mesh Networks , IEEE Trans. on Wireless Comm., 15(9), 2016. • K. Poularakis, G. Iosifidis, I. Pefkianakis, L. Tassiulas, Mobile Data Offloading through Caching in Residential 802.11 Wireless Networks , IEEE Trans. on Network Services & Management, 13(1), 2016.

Data Offloading Marketplace • A set of network operators: • Each operator owns many base stations. • Each BS had different load. • A set of access points: • Each AP has different Internet capacity. • AP owners have communication needs. • A Broker • Goal & Key questions: • Efficiency: maximize BSs’ benefits, minimize APs’ costs. • How much traffic from each BS should be offloaded to each AP? • How much each AP owner should be reimbursed for serving this traffic? • Technical Issues: • Offloading Benefits are AP-specific and interdependent. • Offloading Capacities of the APs are coupled.

Data Offloading Marketplace • A set of network operators: • Each operator owns many base stations. • Each BS serves different amount of traffic. • A set of access points: • Each AP has different Internet capacity. • AP owners have communication needs. • A Broker • Economic Issues: • Multiple buyers & multiple sellers with conflicting goals. • Information asymmetry about the needs. • Solution approach: • Use an auction to elicit hidden information. • Traditional auctions, e.g., VCG and McAfee, cannot be used. • Design a new auction algorithm. G. Iosifidis, L. Gao, J. Huang, L. Tassiulas, A Double Auction Mechanism for Mobile Data Offloading Markets , IEEE/ACM Trans. on Networking, vol. 23, no. 5, 2015.

Data Offloading Marketplace • A set of network operators: • Each operator owns many base stations. • Each BS serves different amount of traffic. • A set of access points: • Each AP has different Internet capacity. • AP owners have communication needs. • A Broker • Economic Issues: • Multiple buyers & multiple sellers with conflicting goals. • Information asymmetry about the needs. • Solution approach: • Use an auction to elicit hidden information. • Traditional auctions, e.g., VCG and McAfee, cannot be used. • Design a new auction algorithm. G. Iosifidis, L. Gao, J. Huang, L. Tassiulas, A Double Auction Mechanism for Mobile Data Offloading Markets , IEEE/ACM Trans. on Networking, vol. 23, no. 5, 2015.

Data Offloading Marketplace • A set of network operators: • Each operator owns many base stations. • Each BS serves different amount of traffic. • A set of access points: • Each AP has different Internet capacity. • AP owners have communication needs. • A Broker • Economic Issues: • Multiple buyers & multiple sellers with conflicting goals. • Information asymmetry about the needs. • Solution approach: • Use an auction to elicit hidden information. • Traditional auctions, e.g., VCG and McAfee, cannot be used. • Design a new auction algorithm. G. Iosifidis, L. Gao, J. Huang, L. Tassiulas, A Double Auction Mechanism for Mobile Data Offloading Markets , IEEE/ACM Trans. on Networking, vol. 23, no. 5, 2015.

Model • A market of multiple BSs and multiple APs, studied for a period T : • M , { 1 , ..., M } : the set of BSs; I , { 1 , ..., I } : the set of involved APs. • Base station m : • x m , { x m 1 , ..., x mI } : offload request vector. • J m ( x m ) : offloading benefit. • Access Point i : • C i : Internet access capacity. • y i , { y i 1 , ..., y iM } : offload admission vector. • V i ( y i ) : offloading cost. • Broker’s objective: Efficiency Maximization X X maximize J m ( x m ) � V i ( y i ) Efficiency x m , y i , 8 m , 8 i m 2 M i 2 I P m 2 M y im  C i , 8 i 2 I , Capacity constraint subject to (i) (iii) x mi = y im , 8 m 2 M , i 2 I . Feasibility

Iterative Double Auction – IDA • An auction mechanism includes: • An allocation rule & a pricing rule. • Bidders’ Bidding Problems • BS Bids: p m = ( p mi : i = 1 , . . . , I ) . P m : maximize J m ( x m ( p m )) � h m ( p m ) , for every BS m ; p mi � 0 , 8 i • AP Bids: α i = ( α im : m = 1 , . . . , M ) . P i : maximize � V i ( y i ( α i )) + l i ( α i ) , for every AP i . α im � 0 , 8 m • Broker’s Allocation Problem p mi log x mi � α im ⇣ ⌘ X X 2 y 2 maximize im x m , y i , 8 m , 8 i m 2 M i 2 I P m 2 M y im  C i , 8 i 2 I , subject to (i) (ii) x mi = y im , 8 m 2 M , i 2 I .

Iterative Double Auction – IDA • The KKT conditions for the efficiency maximization problem: ∂ J m ( x � mi , ( A 2 ) : ∂ V i ( y � m ) i ) = µ � = µ � mi � λ � ( A 1 ) : i , ∂ x mi ∂ y im M ⇣ ⌘ λ � X y � = 0 , ( A 4 ) : x � mi = y � ( A 3 ) : i · im � C i im , m = 1 µ � mi · ( y � im � x � mi ) = 0 , ( A 6 ) : x � mi , y � im , λ � ( A 5 ) : i � 0 . • The KKT conditions for the broker problem: im = µ ⇤ mi � λ ⇤ mi = p mi ( B 1 ) : x ⇤ , ( B 2 ) : y ⇤ i ( B 3 ) � ( B 6 ) = ( A 3 ) � ( A 6 ) , µ ⇤ α im mi • If APs and BSs submit: mi · ∂ J m ( x ⇤ · ∂ V i ( y ⇤ m ) , α im = 1 i ) p mi = x ⇤ ∂ x mi y ⇤ ∂ y im im ... the solutions coincide.

Iterative Double Auction – IDA • The KKT conditions for the efficiency maximization problem: ∂ J m ( x � mi , ( A 2 ) : ∂ V i ( y � m ) i ) = µ � = µ � mi � λ � ( A 1 ) : i , ∂ x mi ∂ y im M ⇣ ⌘ λ � X y � = 0 , ( A 4 ) : x � mi = y � ( A 3 ) : i · im � C i im , m = 1 µ � mi · ( y � im � x � mi ) = 0 , ( A 6 ) : x � mi , y � im , λ � ( A 5 ) : i � 0 . • The KKT conditions for the broker problem: im = µ ⇤ mi � λ ⇤ mi = p mi ( B 1 ) : x ⇤ , ( B 2 ) : y ⇤ i ( B 3 ) � ( B 6 ) = ( A 3 ) � ( A 6 ) , µ ⇤ α im mi • If APs and BSs submit: mi · ∂ J m ( x ⇤ · ∂ V i ( y ⇤ m ) , α im = 1 i ) p mi = x ⇤ ∂ x mi y ⇤ ∂ y im im ... the solutions coincide.

Iterative Double Auction – IDA • The payment and reimbursement rules we employ are: I X h m ( p m ) = p mi , m = 1 , . . . , M − i = 1 M X l i ( α i ) = y im ( λ i � µ mi ) , i = 1 , . . . , I m = 1 − 0.4 0.2 0 − 0.2 Gap y − x BS 1, AP 1: y 11 − x 11 − 0.4 BS 1, AP 2: y 21 − x 12 BS 2, AP 1: y 12 − x 21 − 0.6 BS 2, AP 2: y 22 − x 22 − 0.8 − 1 − 1.2 0 20 40 60 80 100 120 Step − t

Iterative Double Auction – IDA 1 The broker announces the pricing signals BS 1 BS 2 λ i , µ mi , i 2 I , m 2 M . BROKER 1 Broker announces pricing signals (Lagrange Multipliers) AP 1 AP 2 AP 3

Iterative Double Auction – IDA 1 The broker announces the pricing signals 2 λ i , µ mi , i 2 I , m 2 M . Each BS finds its currently optimal bid vector 2 Each AP i and BS m updates its bids using the new Lagrange multipliers. BS 1 BS 2 BROKER AP 1 AP 2 AP 3 2 Each AP find its currently optimal bid vector