O projeto TOFFEE
CASADOCUMENTAÇÃOATUALIZAÇÕESVÍDEOSPESQUISADESCARREGARPATROCINADORESCONTATO


DOCUMENTATION 》 TEST CASES :: TEST RESULTS :: TOFFEE-Mocha-1.0.14 Development version

Here are the TOFFEE-Mocha test cases and test results of the upcoming new TOFFEE-Mocha which is still under development. The features of this TOFFEE-Mocha are discussed in the software development update: TOFFEE-Mocha WAN Emulation software development - Update: 1-July-2016

Test case1 :: 999 millisecond constant packet delay: As you can see unlike 40 milliseconds the maximum limit which existed earlier, the new 999 milliseconds delay range allows users to slow down the transfer rates even further.

kiran@HP-ENVY-15:~/temp$ ping 192.168.0.1 -s 1000
PING 192.168.0.1 (192.168.0.1) 1000(1028) bytes of data.
1008 bytes from 192.168.0.1: icmp_seq=1 ttl=64 time=2000 ms
1008 bytes from 192.168.0.1: icmp_seq=2 ttl=64 time=2000 ms
1008 bytes from 192.168.0.1: icmp_seq=3 ttl=64 time=2000 ms
1008 bytes from 192.168.0.1: icmp_seq=4 ttl=64 time=2000 ms
1008 bytes from 192.168.0.1: icmp_seq=5 ttl=64 time=2998 ms
1008 bytes from 192.168.0.1: icmp_seq=6 ttl=64 time=2997 ms
1008 bytes from 192.168.0.1: icmp_seq=7 ttl=64 time=3995 ms
1008 bytes from 192.168.0.1: icmp_seq=8 ttl=64 time=3985 ms
1008 bytes from 192.168.0.1: icmp_seq=9 ttl=64 time=3984 ms
1008 bytes from 192.168.0.1: icmp_seq=10 ttl=64 time=3984 ms
1008 bytes from 192.168.0.1: icmp_seq=11 ttl=64 time=3983 ms
1008 bytes from 192.168.0.1: icmp_seq=12 ttl=64 time=3982 ms
1008 bytes from 192.168.0.1: icmp_seq=13 ttl=64 time=3984 ms
1008 bytes from 192.168.0.1: icmp_seq=14 ttl=64 time=3982 ms
^C
--- 192.168.0.1 ping statistics ---
18 packets transmitted, 14 received, 22% packet loss, time 17007ms
rtt min/avg/max/mdev = 2000.042/3277.214/3995.537/873.965 ms, pipe 4
kiran@HP-ENVY-15:~/temp$

Test case2 :: 500 millisecond constant packet delay: With 500 milliseconds you get roughly double the performance of 999 milliseconds.

kiran@HP-ENVY-15:~/temp$ ping 192.168.0.1 -s 1000
PING 192.168.0.1 (192.168.0.1) 1000(1028) bytes of data.
1008 bytes from 192.168.0.1: icmp_seq=1 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=2 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=3 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=4 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=5 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=6 ttl=64 time=1488 ms
1008 bytes from 192.168.0.1: icmp_seq=7 ttl=64 time=1481 ms
1008 bytes from 192.168.0.1: icmp_seq=8 ttl=64 time=1481 ms
1008 bytes from 192.168.0.1: icmp_seq=9 ttl=64 time=1008 ms
1008 bytes from 192.168.0.1: icmp_seq=10 ttl=64 time=1002 ms
^C
--- 192.168.0.1 ping statistics ---
11 packets transmitted, 10 received, 9% packet loss, time 10017ms
rtt min/avg/max/mdev = 1002.077/1147.151/1488.063/220.133 ms, pipe 2
kiran@HP-ENVY-15:~/temp$

Test case3 :: 500 millisecond constant packet delay + random packet delay: With constant delay (in this case 500 milliseconds) if you enable the new random packet delay feature, it will skip delay randomly few packets. Which can be controlled via random delay factor. In this case the random delay factor value is set to 1. And you can see below few packets are not delayed. Hence their ping response time almost reduced to half (i.e around 500 ms).

kiran@HP-ENVY-15:~/temp$ ping 192.168.0.1 -s 1000
PING 192.168.0.1 (192.168.0.1) 1000(1028) bytes of data.
1008 bytes from 192.168.0.1: icmp_seq=1 ttl=64 time=1503 ms
1008 bytes from 192.168.0.1: icmp_seq=2 ttl=64 time=1497 ms
1008 bytes from 192.168.0.1: icmp_seq=3 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=4 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=5 ttl=64 time=1001 ms
1008 bytes from 192.168.0.1: icmp_seq=6 ttl=64 time=1001 ms
1008 bytes from 192.168.0.1: icmp_seq=7 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=8 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=9 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=10 ttl=64 time=419 ms
1008 bytes from 192.168.0.1: icmp_seq=11 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=12 ttl=64 time=1001 ms
1008 bytes from 192.168.0.1: icmp_seq=13 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=14 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=15 ttl=64 time=1001 ms
1008 bytes from 192.168.0.1: icmp_seq=16 ttl=64 time=502 ms
1008 bytes from 192.168.0.1: icmp_seq=17 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=18 ttl=64 time=502 ms
1008 bytes from 192.168.0.1: icmp_seq=19 ttl=64 time=1002 ms
1008 bytes from 192.168.0.1: icmp_seq=20 ttl=64 time=1001 ms
1008 bytes from 192.168.0.1: icmp_seq=21 ttl=64 time=1002 ms
^C
--- 192.168.0.1 ping statistics ---
22 packets transmitted, 21 received, 4% packet loss, time 21029ms
rtt min/avg/max/mdev = 419.093/974.135/1503.026/250.662 ms, pipe 2
kiran@HP-ENVY-15:~/temp$

Random Packet delay: As discussed in my VLOG/update earlier, the idea of Random packet delay is to introduce the fluctuating, bursty nature of packet flow. So here are various tests done which shows the same in action. These tests below are performed while downloading a large file by enabling random packet delay along with various values of constant packet delay.

Test case4 :: 2 millisecond constant packet delay + random packet delay: With constant delay of 2 millisecond and random packet delay you can notice the blue curve which almost appears constant. The traffic in this case is bursty but it is not that significant to notice in the graph shown below.
TOFFEE_Mocha_2ms_delay_with_random_packet_delay

Test case5 :: 10 millisecond constant packet delay + random packet delay: With constant delay of 10 millisecond and random packet delay you can notice the blue curve which almost appears constant. The traffic in this case is bursty but it is not that significant to notice in the graph shown below. But it appears somewhat fluctuating than the 5 millisecond test case4 above.
TOFFEE_Mocha_10ms_delay_with_random_packet_delay

Test case6 :: 200 millisecond constant packet delay + random packet delay: With constant delay of 200 millisecond and random packet delay you can notice the fluctuating blue curve. With this we can understand the true purpose of random packet delay.
TOFFEE_Mocha_200ms_delay_with_random_packet_delay

Test case7 :: 200 millisecond constant packet delay + WITHOUT random packet delay: With constant delay of 200 millisecond and WITHOUT random packet delay feature enabled you can notice the steady blue curve. This is a direct comparison of a test case with constant packet delay 200 millisecond with and without random packet delay. With random packet delay it makes the network performance choppy, fluctuating and bursty, but without random packet delay feature the network performance appears almost constant.
TOFFEE_Mocha_200ms_delay_without_random_packet_delay

So in my next upcoming TOFFEE-Mocha release I may include all these new features and updated old features. If you are in need of any specific feature (or scenario) you can kindly let know. If plausible and feasible I can support the same and release as a part of my upcoming TOFFEE-Mocha release. Kindly stay tuned !



Tópicos sugeridos:


TOFFEE-Mocha - WAN Emulator


Categories

💎 TOFFEE-MOCHA new bootable ISO: Download
💎 TOFFEE Data-Center Big picture and Overview: Download PDF


Tópicos recomendados:

Introducing TOFFEE-DataCenter ↗
Saturday' 13-Mar-2021
TOFFEE TOFFEE Data-Center is specifically meant for Data Center, Cluster Computing, HPC applications. TOFFEE is built in Linux Kernel core. This makes it inflexible to adapt according to the hardware configuration. It does sequential packet processing and does not scale up well in large multi-core CPU based systems (such as Intel Xeon servers, Core i7 Extreme Desktop systems,etc). Apart from this since it is kernel based, if there is an issue in kernel, it may crash entire system. This becomes a challenge for any carrier grade equipment (CGE) hardware build.

TOFFEE-DataCenter :: Features Supported ↗
Saturday' 13-Mar-2021
Here is a list of TOFFEE-DataCenter features supported. TOFFEE-DataCenter currently supports some of the important features such as loss-less network data compression, Packet Deduplication (protocols/applications supported), Application Acceleration, TCP Acceleration, dynamic MTU optimization, data packaging, hardware offload support, etc.

Setting up a WAN Emulator within VirtualBox ↗
Saturday' 13-Mar-2021

First TOFFEE Code Release ↗
Saturday' 13-Mar-2021
I started working on the new TOFFEE project (which is the fork of my earlier TrafficSqueezer open-source project) starting from 1st January 2016 onwards. Ever since I was busy in research and altering certain old features so that it is more minimal than TrafficSqueezer, a more focused agenda, deliver refined code and a broader vision. I have lined up more things to follow in the upcoming months. I want to focus about all aspects of WAN communication technologies not just on core WAN Optimization research and technology.

The TOFFEE Project :: TOFFEE-Mocha :: WAN Emulator ↗
Saturday' 13-Mar-2021
The TOFFEE Project :: TOFFEE-Mocha :: Linux Open-Source WAN Emulator

TOFFEE-Mocha WAN Emulation software development - Update: 1-July-2016 ↗
Saturday' 13-Mar-2021
Today I got a feature request from Jonathan Withers. Jonathan is from a company called MultiWave Australia. He said he is able to get the TOFFEE-Mocha Raspberry Pi setup up and with that he is able to emulate geostationary satellite link. But he requested me is there a way to extend the constant packet delay from 40mS to 500mS. So as a part of his request I supported the same in the current ongoing development version of TOFFEE-Mocha.



TOFFEE (and TOFFEE-DataCenter) optimized Mobile Wireless Backhaul Networks ↗
Saturday' 13-Mar-2021
TOFFEE can be used to optimize expensive Wireless backhaul network infrastructure. TOFFEE can be deployed over existing slow or often outdated old backhaul networks too. This will leverage mobile ISPs and network service providers to reduce their bulk IT CapEx and OpEx Costs.

TOFFEE-Mocha WAN Emulation software development - Update: 18-June-2016 ↗
Saturday' 13-Mar-2021
In the previous update (17-Jun-2016) I discussed about the upcoming new Random Packet drop feature along with other completed features. Now I completed the entire TOFFEE-Mocha Random packet drop feature. I completed all the kernel components and the UI support of the same. And to make GUI settings more organized I split the earlier Basic-Settings page into two separate pages namely: Packet Drop and Packet Delay. So this way it is simple to understand settings according to their functionality.

Introducing TOFFEE-Fudge - Network Packet Generator ↗
Saturday' 13-Mar-2021
TOFFEE Fudge is a simple intuitive Network Packet Generator which can be used to create custom test synthetic Network Packets and can be used in various applications such as networking research, network infrastructure troubleshooting, ethical hacking, as a network software development tool and so on.

CDN Content Delivery Networks - Types ↗
Saturday' 13-Mar-2021



Featured Educational Video:
Assista no Youtube - [89//1] B.E and M.E Final Year Projects - Form your Team ↗

Tweaking Network Latency - Live Demo - via TOFFEE-DataCenter ↗
Saturday' 13-Mar-2021

A study on Deep Space Networks (DSN) ↗
Saturday' 13-Mar-2021
When you are dealing Deep Space Networks (DSN) one among the most challenging parts is the Interplanetary distances and communicating data across such vast distances. This is where we are not dealing with common Internet type traffic such as HTTP/FTP/VoIP/etc but it is completely different when it comes to DSN so far. So optimizing data in DSN becomes mandatory. For example if you think one of the Mars Rovers, they have used LZO lossless compression.

TOFFEE-DataCenter WAN Optimization software development - Update: 13-Aug-2016 ↗
Saturday' 13-Mar-2021
Earlier the TOFFEE is intended to work on IoT devices, Satellite Networks, branch office/SOHO deployments. In most cases the users may deploy just one or couple of TOFFEE devices per site. But in the case of TOFFEE-DataCenter, users can scale-up deploying the same in multiple servers in a sort of distributed cluster computing scenario. Besides the core TOFFEE-DataCenter components (such as packet processing engine/framework), I need to do lot of changes in its Graphical User Interface (GUI) too to address these new requirements.

The TOFFEE Project :: TOFFEE :: WAN Optimization ↗
Saturday' 13-Mar-2021
TOFFEE is an open-source WAN Optimization (Network Performance Optimization) software which can be used to optimize your critical networks.



Assista no Youtube - [466//1] 158 VLOG - TOFFEE WAN Optimization Software Development live update - 6-Nov-2016 ↗

Live demo - Data Transfer - High bandwidth to Low bandwidth ↗
Saturday' 13-Mar-2021
I always wanted to do some real experiments and research on packet flow patterns from High-bandwidth to Low-bandwidth networks via networking devices. This is something can be analyzed via capturing Network stack buffer data and other parameters, bench-marking, and so on. But eventually the data-transfer nature and other aspects is often contaminated due to the underlying OS and the way Network stack is implemented. So to understand the nature of packet flow from Higher to Lower bandwidth and vice-versa such as Lower to higher bandwidth, I thought I experiment with various tools and things which physically we can observe this phenomena.



Research :: Optimization of network data (WAN Optimization) at various levels:
Network File level network data WAN Optimization


Learn Linux Systems Software and Kernel Programming:
Linux, Kernel, Networking and Systems-Software online classes


Hardware Compression and Decompression Accelerator Cards:
TOFFEE Architecture with Compression and Decompression Accelerator Card


TOFFEE-DataCenter on a Dell Server - Intel Xeon E5645 CPU:
TOFFEE-DataCenter screenshots on a Dual CPU - Intel(R) Xeon(R) CPU E5645 @ 2.40GHz - Dell Server