Black Spot Identification and Audit Analysis for Heterogeneous Traffic Conditions in Hyderabad City � a Case Study

2015 ◽  
Vol 4 (3) ◽  
pp. 34-42
Author(s):  
T. Sri Lakshmi Sowmya ◽  
◽  
A. Ramesh ◽  
B.N.M. Rao ◽  
M. Kumar ◽  
...  
Keyword(s):  
Black Spot ◽  
Heterogeneous Traffic ◽  
Traffic Conditions

Dynamic Models of Heterogeneous Traffic Flow on Highways

2020 ◽  
Vol 81 (8) ◽  
pp. 1486-1498
Author(s):  
M.A. Fedotkin ◽  
A.M. Fedotkin ◽  
E.V. Kudryavtsev
Keyword(s):  
Traffic Flow ◽  
Dynamic Models ◽  
Heterogeneous Traffic

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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