Research

TOFFEE is an open-source WAN Optimization (Network Performance Optimization) software which can be used to optimize your critical networks.

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.

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.

The TOFFEE Project :: TOFFEE-Mocha :: Linux Open-Source WAN Emulator

When you build a WAN Optimization device with TOFFEE the entire packet processing (data optimization) takes place in software layer or in other words more precisely Operating System kernel space. However if you have any compression or encryption hardware accelerator hardware card the parts of the TOFFEE packet processing modules can be offloaded to hardware layer and thus improving its efficiency.

PiPG is a powerful and yet simple Raspberry Pi Network Packet Generator. With PiPG you can now fabricate custom network packets and send via any Network Interface. Supports all kinds of standard Network Ports (Linux Kernel driver generated) such as Physical Network Interface ports, and an array of virtual ports such as loopback, tun/tap, bridge, etc. indispensable tool for: Network Debugging, Testing and Performance analysis Network Administrators Students Network R&D Protocol Analysis and Study Network Software Development Compliance Testing Ethical Hackers you can generate the following test traffic: L2-Bridging/Slow protocols: STP, LACP, OAM, LLDP, EAP, etc Routing protocols: RIPv1, RIPv2, IGMPv1, IGMPv2, OSPF, IS-IS, EIGRP, HSRP, VRRP, etc Proprietary protocols: CISCO, etc Generic: IPv4 TCP/UDP, etc Malformed random packets

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.

My 500GB Seagate Barracuda 7200RPM hard-drive suddenly started making mild clicking noise. I found this happening since morning. I was suspicious that something wrong in this drive and when I opened the Linux Disks app, I can find the cause of this issue. The disk is increasingly getting read errors. Besides I can see various other parameters such as Power-On Hours, Temperature, Head flying hours, etc.

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.