NEWS & UPDATES 》 Introducing TOFFEE-Fudge - Network Packet Generator

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.

VPN Networks may degrade network performance due to various packet processing overheads such as encryption and by adding extra network protocol header(s) (such as IPv4/IPv6, IPSec, etc). This may inflate near MTU sized packets and causes excessive packet fragmentation. Here are the few examples of packet processing involved in a VPN (or a VPN like) Tunnel. With TOFFEE you can optimize these packets even before they get processed on to a VPN device. TOFFEE optimizes packet contents (application payload and transport headers) so that these TOFFEE optimized packets when they get processed by VPN devices (or VPN software stack) they may never need further packet fragmentation. Here is a deployment scenario of TOFFEE with VPN devices.

TOFFEE-Butterscotch Documentation :: TOFFEE-Butterscotch-1.0.11-rpi2-23-nov-2016

Here is my video article on Network Latency in WAN Networks (such as long distance Satellite links, etc) and how you can optimize the same to achieve better network performance.

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.

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.

You can build a basic TOFFEE WAN Optimization hardware completely in software layer (i.e its networking data-plane and control-plane). And if you are a product manufacturer you can make commercial WAN Optimization products with TOFFEE with software layer alone. And if you choose to improve its performance, you can use any third-party PCIe Compression Accelerator cards.

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 that I do not need two devices for this purpose.

Here is a step-by-step DIY to build your own Intel based Mini PC WAN Optimization Device with TOFFEE. I chose this below Intel Celeron Mini PC since it is fan-less aluminium case and as well it has 2 dedicated inbuilt Gigabit Ethernet ports. You can use one for LAN Network and one for WAN Network.

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.

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.

Internet optimization through TOFFEE-DataCenter WAN Optimization Demo