Jianliang XU, Dik L. Lee, and Bo Li Dept. of Computer Science Hong Kong Univ. of Science & Technology April 2002 On Bandwidth Allocation for Data Dissemination in Mobile Cellular Networks

7/15/20152 Outline Introduction Problem formulation Bandwidth allocation techniques Performance evaluation Wrap up

7/15/20153 On-Demand Access –A client sends data requests uplink to the server, and the server returns the results to the client individually –Fast response for a light-load system Wireless Data Dissemination Data Broadcast –The server periodically broadcasts info to the entire client population, and the clients monitor the broadcast channel to retrieve the data of their interest –Scale to an unlimited client population

7/15/20154 Bandwidth Allocation – Previous Work Data dissemination –Band allocation among data items [Acharya et al., SIGMOD’95; Hameed and Vaidya, ACM WINET] –Band allocation between on-demand access and data broadcast [Acharya et al., SIGMOD’97; Lee et al., ACM MONET] –Confined to a single-cell environment Voice communications –Pretty many studies [Oh & Tcha, IEEE ToC; Zhang & Yum, IEEE ToVT; Li et al., ACM WINET; etc.] –Minimize call blocking/dropping prob. or improve carried traffic while ensuring QoS –Different objective with data dissemination (access delay) Our study: data dissemination & multi-cell

7/15/20155 A Motivating Example Cellular: 2 cells; 6 Kbps shared bandwidth Database: 4 items, each of 1K bits Data access rate: Cell A: 1 Cell B: 4 Flat broadcast Band Alloc(Kbps)Exp. Latency(s)Overall Exp. Latency(s) Cell ACell BCell ACell B Uniform Proportional Best

7/15/20156 Bandwidth Allocation Problem Input parameters Traffic pattern for each cell (long-term steady state) Total amount of bandwidth Frequency reuse pattern Problem: how much bandwidth is assigned to each cell Objective: to minimize the overall access latency for a multi-cell wireless data dissemination system

7/15/20157 Definitions Min reuse distance: the min. distance at which frequencies can be reused at acceptable interference Interference cluster: a maximal subset of cells which are within the distance of mutual interference R

7/15/20158 Cost Model Latency for on-demand access Latency for data broadcast Overall expected latency Notations b i amount of band to cell i data access rate for cell i l i average item size for cell i l i,j size of item j in cell i s i, j space distance between instances of item j in cell i l i, j size of item j in cell i p i, j access prob for item j in cell i M i num of items in cell i’s db N num of cells in the system N b num of cells using broadcast

7/15/20159 Problem Formulation Optimal bandwidth allocation problem Find out (b 1, b 2, …b N ) Minimize Subject to for any interference cluster Q

7/15/ An interference cluster: N c cells Problem formulation Find out (b 1, b 2, …b c ) Minimize Subject to A constrained-minimum problem Optimal Allocation for Interference Cluster

7/15/ Optimal Allocation for Interference Cluster (cont’d) Solved the optimization problem using the Lagrange multiplier theorem Theorem 1 The min overall expected latency is achieved when the bandwidth allocated to cell i, is given by

7/15/ Numerical Result: Broadcast 3 cells: 1, base_rate, base_rate×base_rate Zipf access pattern over data items

7/15/ Numerical Result: On-Demand Access

7/15/ Allocation Techniques for Cellular Networks Challenges –Frequency reuse –non-uniform traffic loads Heuristic allocation techniques –Compact allocation –Cluster-step allocation

7/15/ Compact Allocation Equivalence class: the cells at min reuse distance Assign the same bandwidth to all the cells in an equivalence class Reduced problem: bandwidth allocation for a cluster consisting of the cells with one from each equivalence class –Take the average parameter values –Apply the optimal allocation technique –Favor homogeneous loads for the cells in an equivalence class

7/15/ Cluster-Step Allocation For all possible interference clusters, assign bandwidth one cluster after one depending on their importance –Importance is determined by the aggregate deserved allocation factor ( for broadcast and for on- demand access) –For each cluster only unassigned cells are considered: the total bandwidth is a min of B 1 and B 2 B 1 : remaining available bandwidth for the cluster B 2 : deserved allocated bandwidth for the unassigned cells –Apply the optimal allocation technique for each cluster

7/15/ Simulation Setup Database size: 1,000 data items Total available bandwidth: 672 Kbps Mobile cellular network: 7 × 7 cells Min reuse distance: Access rates RAND: uniformly distributed between 1 and 50 HOMO: for the cells in equivalence class i, uniformly distributed between 1 and 2 i+1 HETERO: uniformly distributed between 1 and 10 except some have 100

7/15/ Numerical Result – Data Broadcast

7/15/ Numerical Result – On-Demand Access

7/15/ Numerical Result – Hybrid Access

7/15/ Limitations - On-demand access with an M/M/1 model - Bandwidth allocation granularity Formulated the band allocation problem for a wireless data dissemination system Analyzed the optimal bandwidth allocation technique for an interference cluster Proposed two heuristics for bandwidth allocation in a mobile cellular network Numerical results showed the superiority of the proposed solutions Wrap Up …