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

Earlier the TOFFEE is intended to work on IoT devices, Satellite Networks, branch office/SOHO deployments. In most cases the users may deploy just one or couple of TOFFEE devices per site. But in the case of TOFFEE-DataCenter, users can scale-up deploying the same in multiple servers in a sort of distributed cluster computing scenario. Besides the core TOFFEE-DataCenter components (such as packet processing engine/framework), I need to do lot of changes in its Graphical User Interface (GUI) too to address these new requirements.

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.

One of the suggestions Google PageSpeed Insights tool suggested for The TOFFEE Project website is to minify the css and java script files. Minify Script files: When you read online about minification of your web script files, often they highlight file size savings and thus resulting faster download time and better website performance.

TOFFEE TOFFEE Data-Center is specifically meant for Data Center, Cluster Computing, HPC applications. TOFFEE is built in Linux Kernel core. This makes it inflexible to adapt according to the hardware configuration. It does sequential packet processing and does not scale up well in large multi-core CPU based systems (such as Intel Xeon servers, Core i7 Extreme Desktop systems,etc). Apart from this since it is kernel based, if there is an issue in kernel, it may crash entire system. This becomes a challenge for any carrier grade equipment (CGE) hardware build.

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).

I have a huge repository (or collection) of sample Wireshark packet capture files for reference. I use them extensively for research and development of TOFFEE as well to understand various protocol PDUs and protocol standards. I personally collected various test captures via Wireshark during my test and experimental research setup during the course of TOFFEE development. Say if you are a student and learning Networking and or say VoIP data and VoIP packets, you can analyse my VoIP sample Wireshark captures. Or in other case assume you are doing some quick research (or development) and want to refer few handful of VoIP packets then you can download and analyse my sample packet capture files.

This is my next software development update of TOFFEE-DataCenter which I am working since past few weeks. I was very busy in implementing the core TOFFEE-DataCenter components along with prototyping, benchmarking, implementing and testing the same. However today is the first time ever I did a fresh new CLI interface for the upcoming new TOFFEE-DataCenter.

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 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.

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).

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.

TOFFEE can be used to optimize expensive Wireless backhaul network infrastructure. TOFFEE can be deployed over existing slow or often outdated old backhaul networks too. This will leverage mobile ISPs and network service providers to reduce their bulk IT CapEx and OpEx Costs.