NEWS & UPDATES 》 Internet optimization through TOFFEE-DataCenter WAN Optimization Demo

Demo TOFFEE_DataCenter WAN Optimization VM (in VirtualBox) Test Setup

This is my first TOFFEE-Mocha combined x86-64 and i386 (Intel x86 64-bit and 32-bit) code release.

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).

Here is a quick demo of TOFFEE WAN Optimization optimizing MySQL Data transfers of a MySQL Client to Server Remote Access.

Network MTU research and optimization of WAN Links

Here is my first software development update of TOFFEE-Butterscotch. In my first TOFFEE-Butterscotch news update I have introduced about TOFFEE-Butterscotch research, project specifications, use-cases, etc. Introducing TOFFEE-Butterscotch Alerts: These are simple packet counters which corresponds to the filter type. For example if the incoming TCP-SYN packets are blocked then its corresponding alert counter will increment whenever such a packet arrives and gets filtered (dropped).

TOFFEE/TOFFEE-DataCenter can be used to optimize Ad-Hoc Mobile Wireless Mesh-Networks. To learn more about the same here are some references: B.A.T.M.A.N. - https://en.wikipedia.org/wiki/B.A.T.M.A.N. Mobile ad hoc network (MANET) - https://en.wikipedia.org/wiki/Mobile_ad_hoc_network Wireless ad hoc network (WANET) - https://en.wikipedia.org/wiki/Wireless_ad_hoc_network open-mesh.org (Open Mesh) Wiki - https://www.open-mesh.org/projects/open-mesh/wiki

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 finally completed building my own private CDN. As I discussed so far in my earlier topics (Building my own CDN), I want to custom build the same step-by-step from scratch. And I don't want to for now use/buy third-party CDN subscriptions from Akamai, CloudFlare, Limelight, etc as I discussed earlier.

Here is my VLOG Youtube video of the same which includes details about version release notes, future road-map and so on. The TOFFEE release is highly optimized and customized for hardware platforms such as x86-64 based Intel NUC and other Intel mobile computing platforms such as laptops and so on. This version (or release) is not suited and so not recommended to be used for high-end desktop and server hardware platform.

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.

Raspberry Pi is often used as a single board computer for applications such as IoT, hobby projects, DIY, education aid, research and prototyping device. But apart from these applications Raspberry Pi can be used for real-world applications such as in making a full-fledged networking devices. Raspberry Pi is a single board ARM based hardware which is why it is also classified as ARM based SoC. Since it is ARM based it is highly efficient, tiny form-factor and lower in power consumption with moderate computational power. This will allow it to work several hours on emergency battery backup power supply such as low-cost domestic UPS and or some renewable energy source, which is a prerequisite for a typical networking device.