DOCUMENTATION 》 TOFFEE (and TOFFEE-DataCenter) optimized Wireless Mesh-Networks - B.A.T.M.A.N [open-mesh.org (Open Mesh)]

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.

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.

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.

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).

I was doing some specific tests in my TOFFEE and TOFFEE-DataCenter (WAN optimization) scenarios such as variable upload and download speeds. And I was also doing some experiments with speedtest.net and I did some of these tests with TOFFEE-Mocha. I realized there is a case that I can introduce asymmetric constant delays so that you can get different download speed and a different upload speed. And in some cases much faster download speeds and relatively slower upload speeds.

Large Infrastructure or ISP setup: In case if you are an ISP and interested in deploying a large customer WAN Optimized network or an add-on enhanced (WAN Optimized) network for select few customers, then you can deploy something as shown below. Although this case is not meant for hobby/DIY users. This is a feasible solution for high-end professional application and the same can be deployed.

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.

Today I completed doing all the changes which are meant for the new upcoming TOFFEE-Mocha release. I have increased the resolution and the range of all factor variables. Instead 1 to 10 range now they have a range of 1 to 30. Unlike before the value 1 means it is lot more intense (or in some cases less intense) and the uppermost value 30 means lot less intense (or in some cases lot intense).

Today I have done a test setup so that I can able to connect my Android Samsung Tab via TOFFEE DataCenter. Below is my complete test topology of my setup. For demo (and research/development) context I configured TOFFEE DataCenter in engineering debug mode. So I do not need two devices for this purpose.

Here is my power-consumption measurements of various devices deployed within my home lab. I measured via my kill-a-watt sort of power-meter which is fairly reliable and accurate. I checked its accuracy with various standard load such as Philips LED laps and other constant power-consuming devices to make sure that the power-meter is precise.