While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore,...While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.展开更多
The general problem faced in the field of Wireless Multimedia Sensor Networks (WMSNs) is congestion. The most common method in the area of WMSNs to minimize congestion is traffic control. Quality Of Service (QOS) is w...The general problem faced in the field of Wireless Multimedia Sensor Networks (WMSNs) is congestion. The most common method in the area of WMSNs to minimize congestion is traffic control. Quality Of Service (QOS) is widely used in WMSNs to guarantee preferential service for critical applications by controlling end-to-end delay, reducing data loss and by providing adequate bandwidth. The present work is on Probabilistic QOS Aware Congestion Control (PQACC) which employs probabilistic method based congestion prediction and priority based data transmission rate adjustment, where inelastic real-time traffic and elastic non-real-time traffic are treated separately. Using the present PQACC approach, average throughput, average source-to-sink delay and average packet loss probability are improved by 9%, 10.33% and 16.03% compared to EWPBRC and achieved 5.97%, 7.05% and 11.69% improvement compared to FEWPBRC. Simulation result reveals that, congestion is effectively predicted, controlled and provides necessary level of QOS in terms of delay, throughput and packet loss, hence making this approach possible in mission critical applications.展开更多
文摘While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.
文摘The general problem faced in the field of Wireless Multimedia Sensor Networks (WMSNs) is congestion. The most common method in the area of WMSNs to minimize congestion is traffic control. Quality Of Service (QOS) is widely used in WMSNs to guarantee preferential service for critical applications by controlling end-to-end delay, reducing data loss and by providing adequate bandwidth. The present work is on Probabilistic QOS Aware Congestion Control (PQACC) which employs probabilistic method based congestion prediction and priority based data transmission rate adjustment, where inelastic real-time traffic and elastic non-real-time traffic are treated separately. Using the present PQACC approach, average throughput, average source-to-sink delay and average packet loss probability are improved by 9%, 10.33% and 16.03% compared to EWPBRC and achieved 5.97%, 7.05% and 11.69% improvement compared to FEWPBRC. Simulation result reveals that, congestion is effectively predicted, controlled and provides necessary level of QOS in terms of delay, throughput and packet loss, hence making this approach possible in mission critical applications.
