Cloud to Cable TV is the platform that makes it easy to send music channels, video channels, video on demand, and any other multimedia streaming content to millions of subscribers. Cloud to Cable enables a friendly distribution system for media owners, content providers, to subscribers in IPTV, Cable TV, OTT, or even satellite operators.
We have developed a technology that sim greatly simplifies multimedia delivery of deliver music & TV content to cable operators and mobile devices, all in one-stop shop. Upload your content to our online drive or cloud storage, and get ready for distribution and monetization.;
Our intellectual property portfolio includes the following assets:
Software and trade secrets
Our patented innovative technology is called: CLOUD to CABLE
In essence, cloud to cable brings value by bringing to you, an easy way to do:
Music broadcasting to Operators, mobile, IPTV, cable TV, or even satellite,
Modern virtualization and cloud technologies integrated into our software,
Broadcasting with fault-tolerance, ready for high-reliability, and great quality of service.
Parallel transcoding meaning you can deliver 5, 10, 50, even 100 music or TV channels depending on the hardware chosen, number of instances, and bandwidth purchased from us,
Web-based approach, yes all is web-based, no funky DIGICIPHER II, MPEG TS, or any of that, all if works on the web, works with Cable TV, Satellite, all and all.
Anvato Acquisition by Google Compared with EGLA CORP
An announcement was found today regarding ANVATO, one of the OTT Platforms in the market. Slideshare brings an interesting one-pager depicting what ANVATO is:
An OTT Platform
Some Patents in encoding and transcoding
Main features include:
Clearly, MEDIAMPLIFY can be compared with ANVATO, indicating that we provides the same amount of features:
Players – Apparently they have they own SDKs – However there are many open source players like FlowPlayer, VideoJS and many others that are compatible with m3u8, rtmp, even the jwPlayer with a $299 license can be resolved.
Reporting : Clearly reporting is key, however Analytics are done mostly via Google and others, but data presentation and reporting is fine.
Live Streaming: EGLA can handle hundreds if not thousands of live streams using Mediamplify.
Video Encoding: Codecs are kings, we offer both on-premice and in the cloud
Cloud Editing: We cannot do edition per-se, maybe CasperCG type of editing.
Syndication: Playback facility works and syndication is available via MediaPlugs
Subscriptions: Payments and authentications is done in the backend
Ad insertion: Likewise, ads can be inserted anytime in the playback facility or in the player.
A few perks from MEVIA/EDIMAPLIFY not offered by ANVATO
We have the MEVIA APP both for IOS and Android
Mediaplugs can generate music channels and provide a Pandora-like Experience
What’s Hadoop? Hadoop is a framework or tools that enable the partition and split of tasks across multiple server and nodes on a network. Hadoop then provides the required framework to MAP and REDUCE a process into multiple chunks or segments.
Hadoop has multiple projects that include:
Hive, Hbase, Chukwa, Tex, Pig, Spark, Tez, and some others that are designed for instance HIVE for a data warehouse that provides data summarization and adhoc querying. HBAse as well is a database that support structured data storage for large tables.
However the common projects are: Common, HDFS, YARN (job scheduling and cluster management), and MapReduce.
As shown in the figure from opensource.com, Hadoop includes a Master Node and Slave Node(s). The Master Node contains a TaslkTracker and a JobTracker that interfaces will all the Slave Nodes.
The MapReduce Layer is the set of applications used to split the process in hand, into several SlaveNodes. Each SlaveNode will then process a piece of the problem and once completed it will be sent over from the process of “Mapping” to “Reducing,”
High Level Architecture of Hadoop
As shown in the figure, the MapReduce logic is shown here.
On the left side,BigData, is a set of files or huge file, a huge log file or a database,
The HDFS refer to the “Hadoop Distributed Filesystem,” which is used to copy part of the data, split it across all the cluster and then later on to be merged with the data
The generated output is then copied over to a destinatary node.
Example of MapReduce
For example,lets say we need to count th number of words in a file, and we will assign a line to each server in the hadoop cluster, we can run the following code. MRWordCounter() does the job of wording each line and mapping all the jobs
from mrjob.job import MRJob
def mapper(self, key, line):
for word in line.split():
yield word, 1
def reducer(self, word, occurrences):
yield word, sum(occurrences)
if __name__ == '__main__':
""" A map-reduce job that calculates the density """
def mapper(self, _, line):
""" The mapper loads a track and yields its density """
t = track.load_track(line)
if t['tempo'] > 0:
density = len(t['segments']) / t['duration']
yield (t['artist_name'], t['title'], t['song_id']), density
As shown here, the mapper will grace a line of file and use the “track.load_track()” function to obtain “tempo”, the number of “segments” and all additional metadata to create a density value.
In this particular case, there is no need to Reduce it, simply it is split across the board of all Hadoop nodes.
As shown in the figure below from cloudier, Hadoop uses HDFS as the lower layer filesystem, then MapReduce resides between the HBase and MapReduce (as HBase can be used by MapReduce, and finally on top of MapReduce we have Pig, Hive, Snoop and many other systems. Including an RDMS running on top of Sqoop, or Bi Reporting on Hive, or any other tool.
Virtualization with Docker and XenServer has been occurring for several years . According to google search, Virtualization consists on:
” Virtualization is the creation of a virtual (rather than actual) version of something, such as an operating system, a server, a storage device or network resources.”
In general, virtualization is nothing more than using the software of the original operating system, e.g. Linux in let’s say an 8-core intel processor, and virtualize a machine with a 2-core machine with code for ARM that runs on an x86 machine. This process of generating a virtual machine that runs on top of other machine is called virtualization. I think we became familiar with qemu and other projects like that to emulate mobile devices and other processors, back then they were called “Emulators.” However, virtualization is nothing different in terms of the same effect, it might differ in terms of kernel and driver usage, which is called in general as a HyperVisor. In simple words, a HyperVisor is nothing more than an Operating System designed to run virtual machines in an efficient manner. As such we have VMWare, Hyper-V and XenServer or even VirtualBox (Hypervisor lists)
We use XenServer in our cloud, and has shown to be efficient and minimal overhead. We also use VMWare but due to its price, we limit the number of instances. and we have used VirtualBox in our laptops.
We can then run Windows over Mac, Mac over Windows, and many other combinations that enable a wide flexibility of software packages that with a good machine, can clearly be of use.
Obviously there are limitations in each hypervisor, from scalability to what type of network cards are exposed to the Virtual Machines (VMs) or how those resources are virtualized. As an example, Hyper-V can handle 1024 VMs per host and 320 processors, whereas XenServer only 160 processors and 50 – 130 hosts. There are also limitations on the amount of memory per VM that can be handled by the Hyper-V. A good paper Identified containing interesting http://ijicse.in/wp-content/uploads/2015/07/v2i3-14.pdf that concludes:
” Our results indicate that Xen Hypervisor, which uses Para- virtualization, was not able to outperform ESXi, which uses full-virtualization. VMware ESXi Server is far better to meet the demand of an enterprise than the Xen hypervisor.”
Hence I will go on detail on Virtualization with Docker and XenServer.
Docker comes to change many ways we do and see virtualization. By using a default linux baseline, you can run a docker image, a docker image is nothing or a file what contains all the required components to run a virtual server. The great thing about docker is that this file, or image, called a “linux container” which includes:
A docker container is nothing but a file, just as the hypervisor runs a VMWare or XenServer image. In this case, the Operating System, let’s say Ubuntu will handle all the context switching and management of the Docker behavior as a process.
XenServer is a hypervisor, which I am very familiar with. XenServer can be installed in almost any hardware and the VMs can be moved and ported over each XenServer instance. As you connect with the XenServer box, you may be able to launch or start the VM and have access to its console. The process of connecting to the XenServer is used by using the standard VNC prfocol with usually ports 5900 and beyond.
Docker over XenServer Virtualization
An overkil seeksml in many cases, as you may have for example a XenServer machine running one or several virtual computers. Let’s say you decide to load Ubuntu 14 LTS on XenServer. The ubuntu machine is ready to go after a while, and then you run a docker container on top of this configuration.
However, Citrix understands this situation and has created a supplementary pack for Docker.
mount: xscontainer-6.5.0-100205c.iso is write-protected, mounting read-only
Installing 'XenServer Container Management'...
Preparing... ########################################### [100%]
1:guest-templates ########################################### [ 50%]
Waiting for xapi to signal init complete
Removing any existing built-in templates
Regenerating built-in templates
2:xscontainer ########################################### [100%]
Pack installation successful. - See more at: http://xenserver.org/discuss-virtualization/virtualization-blog/entry/preview-of-xenserver-support-for-docker-and-container-management.html#sthash.vSWFSUaD.dpuf
Once you install this supplemental pack, XenServer is aware of a container managed VM. As shown in this capture, The machine hp-d385-1, has a virtual machine called CoreOS-817 and includes a hadoop container, tomcat, mysql, apache, that can be launched from the XenServer user interface.
You may think? XenSserver->Ubuntu->Docker ? Will this be too much overhead? I have not done the bechmarking comparing XenServer with Docker. However, a paper was presented showing a performance comparison.
While the performance calculation shows a similar result between a KVM, Docker, and just the bare metal. More studies are required to confirm performance. Further analysis is required to really determine if a Docker container running on a native machine shows a higher performance than a KVM with a docker instance.
A shown in the figures latency from Docker is lower than KVM, storage as well, using EXT4 filesystem and KVM shows that Docker depicted a CDF (%) better than KWM and as good as Native. Obviously, Docker is just running as a process in the native. In fact these researches conclude what I initially stated, that using a Hyper-visor with a VM is not a good practice:
” We also question the practice of deploying containers inside VMs, since this imposes the performance overheads of VMs while giving no benefit compared to deploying containers directly on non-virtualized Linux. If one must use a VM, running it inside a container can create an extra layer of security since an attacker who can exploit QEMU would still be inside the container.”
Docker simply comes to solve a problem using a native environment, using a hypervisor is just unnecessary and not required unless you really need to use an image that was built, tested, and validated for a particular Hyper-V Or you believe you have a special hardware that the Hyper-V can handle or arbitrate better than a version of Linux you may have. One special case is running Windows container on Linux. Apparently Windows showed in 2015 how to run a docker container on Windows, however the opposite seems to be a problem.