Download :: TOFFEE-Mocha Old Releases

LoRaWAN - Network Optimization via TOFFEE WAN Optimization

If you want to deploy TOFFEE along with a web-proxy cache (such as Squid Proxy) you can deploy the same as shown below. TOFFEE does not cache files. TOFFEE does packet level network optimization. So if you want caching your web content you can use transparent mode web-proxy cache intercepting your WAN links. A web-proxy may reduce amount of data being processed (optimized) within these TOFFEE devices and so reduce the CPU overheads and improve its performance.

I started TOFFEE-Mocha WAN Emulation software development on 1-June-2016. I took the existing TOFFEE components as a base. Although the TOFFEE-Mocha is entirely an independent fresh Open-Source WAN Emulation solution. Ever since I am in the process of defining and inventing features. So far I come up with the most important feature which is expected to be present in any WAN Emulation software is the packet delay option.

When you choose to use your Raspberry Pi device as your IoT based remote weather station or if you are building Linux kernel (like kernel compilation) within the same, you need a good uninterrupted power source (UPS). But if you are using it on site or in some research camping location you can choose to power your Raspberry Pi device with your custom off-grid solar power source.

TrafficSqueezer is an open-source WAN Optimization project. TrafficSqueezer is mainly a research project which is started around mid-2006. It is initially started as a research (or prototype) code even before it is officially registered in Sourceforge.net. But this code is just primitive user-space raw socket modules. This is later refined and a pre-alpha version is created. Followed by which Alpha release. This prototype code is moved from user-space to Linux Kernel (Kernel Space) and then the journey begin in terms of making a serious WAN Optimization solution. Once the pre-beta and beta releases are complete the mainstream series is started.

Here is my second software development update of TOFFEE-Butterscotch. In the previous update (28-Oct-2016) I discussed about the Alerts, etc. Whereas in my first TOFFEE-Butterscotch news update I have introduced about TOFFEE-Butterscotch research, project specifications, use-cases, etc.

In case if you need to deploy TOFFEE along with your existing VPN devices you can deploy the same as shown below. This will allow your VPN devices to encrypt your TOFFEE WAN Optimized network data. NOTE: Make sure about the VPN deployment topology done in the right order. Else TOFFEE (LAN side) may get VPN encrypted packets which may not be possible (and or difficult) to further optimize. Hence always make sure to deploy them in a topology suggested below so that TOFFEE devices are out of VPN tunnel.

Ever since after I launched my new The TOFFEE Project website on 1-May'2016, I can see there is a steep increase in traffic. Soon after the launch when I monitored its Alexa rankings it was reporting about 12 Million or so. But once it is getting more and more traffic the Alexa rankings shot up and now currently it shows around 2 Million (as on 22-July-2016). Alexa is an excellent tool to monitor your overall website global ranking and indirectly its performance. Unlike Google Analytics which is bound one or other way into Google's SEO. Alexa gives you a second opinion about your website's growth.

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.

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).