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Impact of Protocols on Traffic Burstiness At Large Timescales in Wireless Multi-Hop Networks

Type: 
Conference PaperInvited and refereed articles in conference proceedings
Authored by:
Jain, Kaustubh., Baras, John S.
Conference date:
June 24-26, 2013
Conference:
IEEE 2013 The 12th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp. 61-68
Full Text Paper: 
Abstract: 

We investigate the impact of the protocol stack on traffic burstiness at large time-scales in wireless multi-hop network traffic. Origins of traffic burstiness at large scales (like its LRD nature) have been mostly attributed to the heavy-tails in traffic sources. In wired networks, protocol dynamics have little impact on large time-scale dynamics. However, given the nature wireless networks, the MAC and routing layers together can lead to route flapping or oscillations even in a static network.Hence, we explore whether these dynamics can lead to trafficburstiness and LRD. Using network simulations, we analyze traffic for two MANET routing protocols - OLSR and AODV. By varying the routing protocol parameters, we analyze their role in inducing or preventing route oscillations, and study their impact on traffic LRD. We find that, losses in OLSR control packets, due to congestion at the MAC, can lead to route oscillations and trafficburstiness at large timescales. By tuning the parameters, route oscillations and traffic LRD can be avoided. AODV dynamics show little evidence for traffic LRD, even though we cannot rule out this possibility. We also show that the route oscillations can have heavier body and tail than exponential distribution, and that the Markovian framework for route oscillations is inadequate to explain the observed traffic scaling. Lastly, we give a model that captures the MAC and OLSR routing protocol interactions and depending upon chosen protocol parameters and input load,correctly predicts the presence of traffic LRD. Thus, we use this model to design appropriate choice of protocol parameters to mitigate traffic burstiness at large-timescales.