Traffic Forecasting Medium Access TRANSFORMA Vladislav Petkov Katia Obraczka 1

Motivation Goals of a good wireless MAC protocol – High channel utilization – Predictable performance under heavy load – Energy efficiency – Low latency Schedule based MACs provide first three at expense of the fourth We want to address that problem Using per-flow traffic forecasting: – Can determine rate of service flow needs – Allocate just right amount of resources to each flow 2

Related work Other schedule based approaches: – Traffic Adaptive Medium Access (TRAMA) – Flow Aware Medium Access (FLAMA) – Dynamic Multi-Channel Medium Access (DYNAMMA) 3

TRANSFORMA design Time slotted channel 2-hop neighborhood information propagated Distributed medium scheduling 4

Control Plane A flow is defined by: Source IP, source port Dest. IP, dest port Each flow has own: Queue Traffic forecaster Transmission opportunities 5

Traffic forecaster For every packet arrival traffic forecaster runs following algorithm: 1.Computes the latest packet interval, τ 2.Calculates loss of each expert, x i 3.Reduce weights of bad experts 4.Share some of remaining weights 5.Calculate new forecaster output 6

Flow selection algorithm Rate Monotonic Algorithm approximated to select flow for each slot – Flow f i, with interval t i, pseudorandomly chooses one slot in each t i interval – Ties are broken by flow and node ID hash – Flows persist (try for every slot) if they fail to win their chosen slot Flows with interval below τ min are considered best-effort We empirically determine best τ min value (currently 10ms) TRANSFORMA guarantees collision free medium access under all conditions 7

Performance evaluation We use Qualnet Network Simulator PHY is a at 6.0Mbps Radio range is ≈400m 2 experiments: – Heterogeneous flows Variable number of CBR flows of different rates – Real-time vs. best-effort 3 real-time flows with increasing amount of background traffic Hotspot layout 8

Expt 1 results: Heterogeneous Flows TRANSFORMA DYNAMMA (a schedule based protocol) 9

Expt 2 results: Real-time vs best-effort Foreground application delay Foreground & background goodput 10

Conclusion and future work TRANSFORMA has predictable performance, even at high load TRANSFORMA delivers lower delays to delay sensitive applications than DYNAMMA and even under high load Future work: – Implementation based experimentation with more applications – Ways of adding routing awareness in scheduling 11