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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.

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.

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.

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 TOFFEE can be deployed over existing slow or often outdated old backhaul networks too.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.

I was doing some specific tests in my TOFFEE and TOFFEE-DataCenter (WAN optimization) scenarios such as variable upload and download speeds. And I was also doing some experiments with speedtest.net and I did some of these tests with TOFFEE-Mocha. I realized there is a case that I can introduce asymmetric constant delays so that you can get different download speed and a different upload speed. And in some cases much faster download speeds and relatively slower upload speeds.

As a research experiment I replaced my Intel Core i7 5820K desktop PC with my Intel Celeron 1037U Mini-PC as my everyday desktop system. This is an attempt to reduce my overall monthly power consumption. As well an attempt to do feasibility tests and research to know how far Mini PC will dominate the market in future and to study the real potential of Mini PCs.

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.

TOFFEE-Butterscotch is an open-source software which can be used to save and optimize your Internet/WAN bandwidth. Unlike TOFFEE (and TOFFEE-DataCenter) TOFFEE-Butterscotch is a non peer-to-peer (and asymmetric) network optimization solution. This makes TOFFEE-Butterscotch an ideal tool for all Home and SOHO users.

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.

TOFFEE Optimized Satellite ISP Network: TOFFEE/TOFFEE-DataCenter can be used to optimize Satellite Networks (Satellite based Internet Networks, VoIP, Data, private leased-links) as shown. Ground station transponders can be connected via array of TOFFEE Devices and in the remote CPE can have dedicated or inbuilt TOFFEE with which you can establish a WAN Optimized Satellite Network Tunnel as shown.