NEWS & UPDATES 》 Internet optimization through TOFFEE-DataCenter WAN Optimization Demo

Internet optimization through TOFFEE-DataCenter WAN Optimization Demo

TOFFEE Optimized Satellite ISP Network: TOFFEE/TOFFEE-DataCenter can be used to optimize Satellite Networks (Satellite based Internet Networks, VoIP, Data, private leased-links) as shown. Ground station transponders can be connected via array of TOFFEE Devices and in the remote CPE can have dedicated or inbuilt TOFFEE with which you can establish a WAN Optimized Satellite Network Tunnel as shown.

Here is the TOFFEE WAN Optimization benchmarks of the TOFFEE version: TOFFEE-1.1.28. This is the current TOFFEE development version till date (2-Jul-2016). This is a HPC TOFFEE variant meant for high-end custom build servers and high-end desktops (i.e High Performance Computing a.k.a HPC). TOFFEE built this way often needs customized kernel compilation and build such as processor specific and hardware specific tune-ups since it is highly CPU intensive (if not offloaded via Hardware Accelerator Cards).

Download TOFFEE-MOCHA-2.0.3-0-10-nov-2018-x86-64.iso via Google Drive share: Live bootable x86-64 Debian Stretch 9.5 with light-weight LXDE UI ISO (includes source-code): TOFFEE-MOCHA-2.0.3-0-10-nov-2018-x86-64.iso You can find the source tar-ball in the /root folder. To know more about the project kindly refer TOFFEE- Mocha: News and Updates - Documentation. To know more about current specific release, objectives, features, release notes/updates, quick demo and future road-map, you can watch my video below.

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.

Today I refined the first page consolidated report graphs. TOFFEE-Mocha (unlike TOFFEE) is a WAN Emulator, so the graphs are supposed to highlight this purpose and should display the overall network activity. Unlike TOFFEE, the TOFFEE-Mocha report should contain in general what is received versus what is sent across the wire. In case if the packet drop feature is enabled, you should see few missing bytes and packets. Similarly in future I may support packet duplication feature, in that case you may see more packets/bytes sent versus the packets/bytes actually received.

Assume you have two sites (such as Site-A and Site-B) connected via slow/critical WAN link as shown below. You can optimize this link by saving the bandwidth as well possibly improve the speed. However, the WAN speed can be optimized only if the WAN link speeds are below that of the processing latency of your TOFFEE installed hardware. Assume your WAN link is 12Mbps, and assume the maximum WAN optimization speed/capacity of Raspberry Pi is 20Mbps, then your link will get speed optimization too. And in another case, assume your WAN link is 50Mbps, then using the Raspberry Pi as WAN Optimization device will actually increase the latency (i.e slows the WAN link). But in all the cases the bandwidth savings should be the same irrespective of the WAN link speed. In other words, if you want to cut down the WAN link costs via this WAN Optimization set up, you can always get it since it reduces the overall bandwidth in almost all the cases (including encrypted and pre-compressed data).

WAN Network data can be optimized at various levels depending upon the network applications, protocols, topology and use-cases. So the amount of data you can optimize will depend on the strategy you choose to optimize. Such as: Network Packet level optimization, Session level optimization, File level optimization, etc.