Layered Backpressure Scheduling for Delay Reduction in Ad Hoc Networks Leandros Maglaras Dimitrios Katsaros (presentation) Dept. of Computer & Communications Engineering University of Thessaly, Volos, Greece WoWMoM Symposium, Lucca (Italy), 21-23/June/2011

Wireless Ad Hoc Networks Wireless Ad Hoc Networks features Dispersed network Self-organized All nodes acts as routers No wired infrastructure Potential multihop routes

Modern battlefield: Delay does matter

Packet scheduling based on Back-pressure: the LayBP protocol Our proposal … Packet scheduling based on Back-pressure: the LayBP protocol Creates clusters based on dense connectivity Forwards packets on a cluster-by-cluster basis, until the packet enters the ‘destination’ cluster Reap the benefits of both worlds: Cluster-based backpressure Shortest-path backpressure

The back-pressure protocol Each node: Maintains one queue for every other node c a e d = Optimal Commodity for link (e,d) on slot t (maximizes diff. backlog)

Backpressure: pros-and-cons throughput-optimal routing algorithm robust to topology changes Based on solid mathematical background (Lyapunov drift theory), thus, ability to study stability properties Backpressure ↓: Its performance deteriorates in conditions of low, and even of moderate, load in the network, since the packets “circulate” in the network: the backpressure stabilizes the system using all possible paths throughout the network The net effect of this mechanism is to increase delay (and thus energy consumption)

Delay-aware backpressure: Cluster-based @ INFOCOM08 Motivation: Reduce the number of queues How? Define arbitrary clusters Intercluster communication via gateways Maintain one queue per gateway at each relay node which still leads to an excessive number of gateways Delay increases with increasing clusters number! Why? Too many gateways

Delay-aware backpressure: Shortest-path @ INFOCOM09 & IEEE Trans. on Net 11 Motivation: Delay can be reduced via shortest-path routing How? Precomputation of all pairwise-node distances application of the backpressure notion on the shortest path(s) between source and destination Achieves the lower-bound of delay Removal of a single link keeps delay high! Why? Shortest-path recalculation is needed

Can we combine the two? ‘Relax’ the demand for precomputation of shortest-paths Robustness to topology changes Maintain the idea of ‘pushing the packets to the preferred direction’ Exploit the clustering idea, but Scale-up to the number of clusters and/or gateways Smart clustering

Our proposal: Layered Backpressure LayBP ideas: [1st] Split the network into layers according to the connectivity among them usually implies geographic proximity, as well [2nd] Assign layer ID respecting geo-proximity Breadth-first or fractal curve (Hilbert) numbering [3rd] Forward packets according to the destination layer ID The layers play the role of attractors, that attract the packets destined for them and then “disallow” the packets to leave the layer

Layered Backpressure: Packet scheduling Each node n maintains a separate queue of packets for each destination The length of such queue is denoted as Qdn[t] For every queue Qdn[t] the node computes the parameter Dleveldn which represents the absolute difference between current and destination node’s layer number Dleveldn = |Layer(n) − Layer(d)|

LayBP operation

Throughput optimality Following the work: M. J. Neely, E. Modiano, and C. E. Rohrs. Dynamic power allocation and routing for time varying wireless networks. IEEE JSAC, 23(1):89–103, 2005 Since, parameter Anmd is bounded (for LayBP it is a constant), the policy is throughput optimal

Evaluation setting LayBP compared to: Backpressure (BP), Shadow-Queue backpressure (SQ-BP), Cluster-based backpressure (CB-BP), Shortest-path backpressure (SP-BP) Performance measures: Delay & Throughput Network topologies:

Parameter tuning A=2 is OK

Few gateways: More rapid convergence Delay performance Many gateways grid Few gateways Few gateways: More rapid convergence

Throughput performance All policies are throughput-optimal

Summary Wireless Ad Hoc Networks New Packet Scheduling protocol, LayBP LayBP evaluation and comparison to the state-of-the-art protocols LayBP: throughput-optimal tradeoff between delay and calculations robust to topology changes

Relevant references L. Tassiulas and A. Ephremides. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Trans. Automatic Control, 37(12):1936–1948, 1992. L. Ying, R. Srikant, and D. F. Towsley. Cluster-based backpressure routing algorithm. In Proc. INFOCOM, pages 484–492, 2008. L. Ying, S. Shakkotai, and A. Reddy. On combining shortest-path and back-pressure routing over multihop wireless networks. In Proc. INFOCOM, 2009. And IEEE/ACM Trans. On Networking, 19(3): 841–854, 2011.

Thank you for your attention! Questions?

Topology change Inactive one of the two intercommunity links

Impact of topology change

Better layer naming: Hilbert curves

For 3-D wireless sensornets: Underwater

Clustering: max-min d-cluster formation @ INFOCOM04 &

Original backpressure