No abstract

No abstract

No abstract

This paper presents a comparative study between JET (Just Enough Time) and a number of new preemptive channel scheduling algorithms in Optical Burst Switching (OBS) with QoS (Quality of Service) requirement. In terms of performance criteria, loss rate in byte, access delay and end-toend delay are considered. A 2-state MMPP (Markov Modulated Poisson Process) traffic generator on a 14-node mesh network topology illustrating NSFNET is designed as the testbed. NS2 Network Simulation tool is used for our tests. Bursts are created using a hybrid model that takes into account both timeout and maximum length threshold mechanisms. In core and edge nodes, in order to satisfy QoS requirements two mechanisms such as Regulative Wavelength Grouping (RWG) and priority based queuing are used. RWG mechanism, where the number of wavelengths for each traffic class is arranged for adjusting the burst drop probability of traffic classes under a specific threshold value, is used for providing priority levels in core nodes. In priority based queuing, bursts are sent according to their priority order, in edge nodes. Simulation works have shown that our preemptive channel scheduling algorithms give better results compared to the other studied algorithms while considering QoS.

Recent advances in the automotive industry enabled us to build fast, reliable, and comfortable vehicles with lots of safety features. Also, roads are designed and made safer than ever before. However, traffic accidents remain one of the major causes of death. Intelligent transport systems are expected to reduce if not prevent accidents with interconnected vehicles and infrastructures. These vehicular ad hoc networks are highly dynamic and fragile. Although the standardization efforts are mature enough, the non-emergency/service channel selection mechanisms are not explicitly defined. In this paper, a novel cross-layer prediction-based algorithm is proposed to select the best possible service channel to decrease collisions beforehand. Theoretical analysis regarding the mean squared error prediction performance is established. It is shown that the proposed method outperforms the general Markovian-based prediction schemes under various traffic load scenarios.