A METHOD FOR ESTIMATING THE CAPACITY OF THE BUFFER MEMORY OF A ROUTER IN THE PRIORITY MANAGEMENT OF HETEROGENEOUS TRAFFIC WITH ARBITRARY DISTRIBUTION OF PACKET LENGTHS

2017 ◽  
Author(s):  
L. A. Muravyeva-Vitkovskaya
Keyword(s):  
Heterogeneous Traffic ◽  
Arbitrary Distribution ◽  
Buffer Memory

Variable Precision Depth Encoding for 3D Range Geometry Compression

2020 ◽  
Vol 2020 (17) ◽  
pp. 34-1-34-7
Author(s):  
Matthew G. Finley ◽  
Tyler Bell
Keyword(s):  
Normal Distribution ◽  
Arbitrary Distribution ◽  
File Size ◽  
Geometry Compression ◽  
Variable Precision ◽  
Wide Range ◽  
Novel Method ◽  
Encoding Method ◽  
Rgb Image ◽  
Color Channels

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.

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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