How to find out the quality of machine translation systems? - google-translate

I know that there are various metrics for measuring the quality of machine translation systems, for example:
Bleu
METEOR
Lepor
Are there somewhere in the public domain metric results for popular translation systems? For example, such as:
Google translate
Yandex Translate
Microsoft translate
Promt
Apertium
Openlogs

Machine translation quality is annually evaluated at the Conference on Machine Translation. Most of the evaluated systems are experimental systems from universities, but most of the systems you mention participate as well. You the results of last year's human evaluation in Table 11 on page 24 of the conference findings.
Most of the systems you mentioned participate anonymously under acronyms online-?, but you can often guess which system is which.

Related

Network Simulation using NS-3

Can I simulate susceptible-infected-susceptible (SIS) model using NS-3?
I'm aiming to model malware flow using SIS and trying to simulate using NS-3.
I'm a newbie to networks, and have been searching for this since hours, going through tens of research papers but can't find anything similar.
SIS implies some sort of discovery mechanism to spread the infection of a network. These discovery mechanisms typically use exploits in network capable daemons. So, to simulate SIS, you want a simulator that works at the application level of the network stack.
ns-3 has some application-level capabilities, but it's mostly intended to be used to simulate the network stack below the application level. The application-level capabilities that ns-3 does have are limited to traffic generation. Discovering the presence of a service on a Node, let alone a compromised version of a daemon, is not supported.
So, it seems like you'll need to find another simulator. I'm not sure what your options are, but depending on how complex of a simulation you want, you could just roll your own by representing the network as a graph, and infecting a susceptible node with probability p.

cluster vs Grid vs Cloud

There are two questions:
1) What is the difference between cluster and Grid
2) What is the Cloud
I am not looking for conceptual definitions,
I found a lot of that by googling but the problem is I still do not get it.
so I believe the answer I seek is different. From what I could re-search online I start to think that
many article writers who is trying to explain this either do not understand this deep enough themselves
or not able to explain their knowledge for an average guy like myself (which is common issue with very technical people).
Just to let you know my level: I am a computer programmer, .NET and LAMP, I can do basic admin on both
Linux flavors and Windows, I have hands on experience with Hyper-V and now researching Xen and XCP
to setup a test cloud based on two computers for learning purposes.
Below info you do not have to read, it is just my current understanding of cluster,grid and cloud it
just to support my two questions because I thought it would help to understand
what kind of mess is in my head right now and what answers I am looking for.
Thank you.
Two computers used for reference in my statements are "A" and "B"
specs for A: 2 core intel cpu, 8GB memory , 500gb disk
specs for "B": 2 core intel cpu, 8GB memory , 500gb disk,
Now I would like to look at A and B roles from Cluster, Grid and from Cloud angle.
Common definitions between Grid and Cloud
1) cluster or Grid are 2 or more computers hooked up together, on hardware level
they are hooked up though network cards and on a software level
it is using some kind of program implementing message passing interface
to make it possible to send commands between nodes.
2) cluster or Grid do NOT combine CPU power or memory between nodes, meaning
that in this simulation a FireFox browser running on A still has only one 2 cores cpu,
8GB memory and 500gb available.
Differences between Grid and Cloud:
1) Cluster only provides fail over part, if A node breaks while FireFox is running
the cluster software will re-start FireFox process on node B.
2) Grid however is able to run a software in parallel on multiple nodes at the same time
provided that software is coded with MPI in mind. It can also lunch any software on any node
on demand (even if it is not written for MPI)
3) Grid is also able to combine different type of
nodes, Linux Server, Windows XP, Xbox and Playstation into one Grid.
Cloud definition:
1) Cloud is not a technical term at all, it is just a short convenient word to describe
a computer of unlimited resources, it can aslo be called a Supercomputer, a Beast, an Ocean or Universe but someone
said "Cloud" first and here we are.
2) Cloud can be based on Grids or on Clusters
3) From technical point of view Cloud is a software to combine hardware resources into one,
meaning that if I install Cloud software on Grid or Cluster then it will combine A and B
and I will get one Cloud like this: 4 core CPU, 16gb memory and 1000gb disk.
edited: 2013.04.02
item 3) was a complete nonsense, cloud will NOT combine resources from many nodes into one huge resource, so in this case there will be no 4 core CPU, 16gb memory and 1000gb cloud.
Grid computing is designed to parcel out large workloads to many participating grid members--through software on each member which is expecting to hear that request for computation or for data, and to reply with it's small piece of the overall puzzle. Applications must be written specifically for this approach to problem-solving. It can be heterogeneous because it's not the OS that matters but the software waiting to hear problem-solving requests.
The expectation of a cluster is that it can run the same executable image across any member node--any node can execute that code--which is what drives its requirement for homogeneity. You can write cluster-aware code which distributes workload throughout the cluster, but again you have to write your code to be cluster aware in order to take advantage of more than the redundancy features of a cluster. As most application vendors do not write cluster-aware code, the simple redundancy feature is all that's commonly used in cluster deployments, but that does not limit the architecture. Clusters can and do share their resources, and can collaborate on tasks simultaneously.
Cloud, as it's commonly defined is neither of these, precisely, but it doesn't preclude them, either. Cloud computing assumes the ability to deploy an application without advanced knowledge of it's underlying operating system, or even control of that operating system, coupled with the ability to expand or reduce the processing and memory footprint available to that application without having to destroy and recreate that environment--all done with enough isolation that the application won't know or be able to know what other applications might be installed or running on it's shared infrastructure, unless that access is approved-of by both application managers.
I would like to answer my question before this is closed as a duplicate because I believe it can be very frustrating to find correct info in regards to clusters,grids and clouds and I think this post can save time for many. If someone wants to challenge it please do so, otherwise I will mark it as answer in 1 week.
1) There are many differences and there are none, it really depends on the technical context but
generally you can connect several nodes and call it a Grid or you can call it Cluster. I would say Grid is a Cluster with extended capabilities, such as ability to connect heterogeneous nodes. Both Grid and Cluster will serve as scale-out platform equally good. From Network Engineer and Programmer perspective the difference in implementation or coding will be pretty big if Gird connects heterogeneous nodes.
2) Now the first question was actually a prelude for second one and I believe it is best answered by
Matt Joyce in this post:
https://stackoverflow.com/a/15286488/2230126
I'll take a crack at it. I have been collecting and saving my notes, scripts, and programs since the year 2002 A.D. This is a chop and paste of my statements over the years. Here is a brain friendly memorization list:
The grid is the hardware and hardware specifications.
a. You plug into the router or switch and setup IP addresses and top-level domains over the internet (which is also known as ICANN).
b. This is like OSI level 1, 2, and 3.
The cluster is the kernel (software ring 0 or 1 if its a virtual type thing going on).
a. The kernel is configured (compiled) to run a network stack that can handle sessions, permission, and account authentication.
b. You set up port to port communications usually over TCP/IP (like in the OSI model).
c. You setup iptables, pf, arp, and other OS level applications or shared objects.
d. You can setup ssh, kerberos, ldap, or some other PKI-database and protocol-socket combo.
e. This is like OSI level 4, 5, and 6.
The cloud is user-space applications.
a. The application processes talk to other application-processes within the cluster.
b. You setup process level permissions (via files, cgroups, and/or user-groups).
c. You setup mysql, redis, riak, Message Brokers, hadoop, apache, nginx, cron, java, haskell, erlang, and etcetera.
d. This is like OSI level 7.
The cloud floats over the cluster that grows from the grid. And actually visually think, cloud in the air, cluster in tree, and grid on the ground. Most of us creative types (which make all these technologies) are visual thinkers that can back it up with mathematical data and code. So always see if you can answer the riddle and correlate technological facsimiles to our physical realm here on Earth.
Intro
Grid, Cluster, and Cloud are three different words that mark their specific time in history. Their definitions have intersecting traits and they are modernly interchangeable. You just need to know when to apply the correct or associated word. For example, I was talking to some older M.D.s (medical doctors) and they wanted to know what the cloud was. So I told them that the cloud was a computer cluster that you rent over the internet. And Bingo, they got the idea within 10 seconds.
I will use a little bit of history in chronological prose.
Grid
The term grid is first used to represent one resource that is repeated across terrestrial landscape or space. The term is frequently used during the distribution of telegraphs where repeaters had to be placed on poles every N radii (plural for radius) to amplify the signal. Another example is the electrical grid that Thomas Edison and Nikola Tesla competitively started spreading around the Earth. Computers got really popular and they soon were expanded across The Grid to replace human telegraph (and telephone) operators.
The Grid is now a bunch of computers that can connect and terminate communication channels. The Grid is an infrastructure of computers that function for one goal which is the run assembly (or binary) code.
Cluster
Farseeing the power of computers and actually witnessing computers win wars (Turing's machine), DARPA (or ARPA which is the U.S.A. Military) stepped in.
DARPA started commissioning universities and colleges to utilize the Grid for multi-plexing communication methods (that use baud and protocols). Universities and colleges started making protocols to separate the different tasks that they wanted to carry out over the Grid and target the computers. That started the modern internet. In-house testing clusters were established in laboratories to simulate the grid. Clusters are great for orchestration. A job can be sub-divided over all or some of the slaves within a cluster. The military utilized the college and university's findings and applied the SOFTWARE to the Grid. There were some gotchas with clusters:
Must be same (or near same) hardware
Must have same operating system
The rules were strict because all the instruction-sets had to be the same passing over the CPUs. Clusters usually had a master and slave type relationship. A Cluster usually ran one unic (or unix) job at a time. Clusters had job-schedulers. Then clusters got more complex because hardware manufacturers started making parallel chip architectures (on top of the Von Neumann arch).
Clusters become more powerful. The Clusters inherited more complexity and people were doing more creative things. Cluster could now do different jobs, tasks, processes, asynchronously processes, synchronized processes, and many more interesting things. One box (or computer node) could run more jobs. Now the Grid could be used for multiple purpose. The rate of software updates on clusters was faster than the actual grid. Clusters were deployed locally on campuses. Clusters started superseding the grid because you could directly produce a public facing stack that out-performed the (national) grid.
My Experience
I went to college during the late 1990s and 2000s and cluster was the word for a physical laboratory of multiple computers working as one virtual computer. Clusters were used for testing. Once your software worked on the cluster, then you could mv (move) it to the production grade Grid. Then I witness network worms and computer viruses control zombie computers. These swarm of zombies could be used as one gigantic virtual cluster used to run commands. Well programmers started DIY (do it yourself) protocols and software like bit-torrent and Napster.
So leaping forward into the future, testing cluster softwares are starting to be replaced by Solaris jails, FreeBSD jails, Linux containers, QEMU, hyper-visors, VMWare, VirtualBox, Vagrant, and Docker.
Cloud
Cloud is a marketing term used to umbrella the hardware of different grids and the software of those clusters. Cloud is one big ubiquitous word used to advertise, promote, and profess all that cluster technology for monetary gains. Cloud is also an effort to wrap all those technologies under one singular word. The Cloud allows multi-tenanted processes to share a gigantic grid. The Cloud maximizes efficiency by sub-dividing the electricity, CPU, RAM, DISK, Electricity, and broadband which gets shared and paid for by consumers. A side effect is that those consumer subscriptions and/or pay-rates started producing profit. The Cloud also allows multiple users to install multiple operating systems that run multiple processes all in the software. So now we have acronyms like IaaS, PaaS, and SasS. The Cloud can replace the start-up cost that was once so darn difficult to fund and bootstrap. The Cloud is a great solution for mock testing your software and building a consumer base for your business.
From another perspective, the Cloud triggers the brain of non-programmers to think a certain way. For example, the human resource department can comprehend and isolate what is presented in-front of them.
So if you got the money, then you can purchase your share of the cloud experience and have easy support along with it. But if you have the skill-set, the time, the quick know-how, and the ability to install your own servers at co-locations, then do that because it is cheaper over the long run.
That is my narrative on the Grid vs Cluster vs Cloud.
I think this link well compared the Cluster and Grid.
As I know, there are some exceptions in the case of Clusters. YARN (Yahoo!) tries to handle mutli-tenancy and distributed scheduling. Also Corona (Facebook) has distributed scheduling.

Flow based routing and openflow

This may not be the typical stackoverflow question.
A colleague of mine has been speculating that flow-based routing is going to be the next big thing in networking. Openflow provides the technology to use low cost switches in large application, IT data-centers, etc; replacing Cisco, HP, etc switch and routers. The theory is that you can create a hierarchy these openflow switches with simple configuration, eg. no spanning tree. Open flow will route each flow to the appropriate switch/switch-port, using only the knowledge of the hierarchy of switches (no routers). The solution is suppose to save enterprises money and simplify networking.
Q. He is speculating that this may dramatically change enterprise networking. For many reasons, I am skeptical. I would like to hear your thoughts.
OpenFlow is a research project from Stanford University led by professor Nick McKeown. In the original OpenFlow research paper, the goal of OpenFlow was to give researchers a way "to run experimental protocols in the networks they use every day." For years networking researchers have had an almost impossible task deploying and evaluating their ideas on real networks with real Ethernet switches and IP routers. The difficultly is that real switches and routers from companies like Cisco, HP, and others, are all closed, proprietary boxes that implement standard "protocols", like Ethernet spanning tree, and OSPF. There are business reasons why Cisco and HP won't let you run software on their switches and routers; there is no technical reason. OpenFlow was invented to solve a people problem: if Cisco is not willing to let you run code on their switch, maybe they can at least provide a very narrow interface to let you remotely configure their switch, and that narrow interface is called OpenFlow.
To my knowledge more than a dozen companies are currently implementing OpenFlow support for their switches. Some like HP are only providing the OpenFlow software for research purposes. Others like NEC are actually offering commercial support.
For academic researchers that want to evaluate new routing protocols in real networks, OpenFlow is a huge win. For switch vendors, it is less clear if OpenFlow support will help, hurt, or have no effect in the long run. After all, the academic research market is very small.
The reason why OpenFlow is most often discussed in the context of enterprise networks is that OpenFlow grew out of a previous research project called Ethane that used OpenFlow's mechanism of remotely programming switches in an enterprise network in order to centralize a security policy. Ethane, and by extension OpenFlow, has led directly to two startup companies: Nicira, founded by Martin Casado, and Big Switch Networks, founded by Guido Appenzeller. It would be easier to implement an Ethane-like system if all of the switches in the network supported OpenFlow.
Closely related to enterprise networks are data center networks, the networks that interconnect thousands to tens of thousands of servers in companies such as Google, Facebook, Microsoft, Amazon.com, and Yahoo!. One problem with Ethernet is that it does not scale to this many servers on the same Layer 2 network. We attempted to solve this problem in a research project called PortLand. We used OpenFlow to facilitate programming the switches from a central controller, which we called a Fabric Manager. We released the PortLand source code as open source.
However, we also found a limitation to OpenFlow's functionality. In another data center networking research project called Helios, we were not able to use OpenFlow because it did not provide a mechanism for bonding multiple switch ports into a Link Aggregation Group (LAG). Presumably one could extend the OpenFlow specification indefinitely until it all possible switch features become exposed.
There are other networks as well such as the Internet access networks, Internet backbones, home networks, wireless networks, cellular networks, etc. Researchers are trying to see where OpenFlow fits into all of these markets. What it really comes down to is the question, "what problem does OpenFlow solve?" Ethane makes a case for enterprise networks but I have not yet seen a compelling case for any other type of network. OpenFlow might be the next big thing, or it might end up being a case of "don't solve a people problem with a technical solution."
In order to assess the future of flow-based networking and OpenFlow, here’s the way to think about it.
It starts with the silicon trends: Moore’s Law (2X transistors per 18-24 months), and a correlated but slower improvement in the I/O bandwidth available on a single chip (roughly 2X every 30-36 months). You can now buy full-featured 10GbE single chip switches with 64 ports, and chips which have a mix of 40GbE and 10GbE ports with comparable total I/O bandwidth.
There are a variety of ways physically connect these in a mesh (ignoring the loop-free constraints of spanning tree and the way Ethernet learns MAC addresses). In the high performance computing (HPC) world, a lot of work has been done building clusters with InfiniBand and other protocols using meshes of small switches to network the compute servers. This is now being applied to Ethernet meshes. The geometry of a CLOS or fat-tree topology enables a two stage mesh with a large number of ports. The math is thus: Where n is the # of ports per chip, the number of devices you can connect in a two-stage mesh is (n*2)/2, and the number you can connect in a three-stage mesh is (n*3)/4. While with standard spanning tree and learning, the spanning tree protocol will disable the multi-path links to the second stage, most of the Ethernet switch vendors have some sort of multi-chassis Link Aggregation protocol which gets around the multi-pathing limitation. There is also standards work in this area. Although it might not be obvious, the vast majority of Link Aggregation schemes allocate traffic so all the frames of any given flow take the same path. This is done in order to minimize out-of-order frames so they don’t get dropped by some higher level protocol. They could have chosen to call this “flow based multiplexing” but instead they call it “link aggregation”.
Although the devil is in the details, there are a variety of data center operators and vendors that have concluded they don’t need to have large multi-slot chassis switches in the aggregation/core layer for server connect, instead using meshes of inexpensive 1U or 2U switches.
People have also concluded that eventually you need some kind of management station to set up the configuration of all the switches. Again, drawing from the experience with HPC and InfiniBand, they use what is called an InfiniBand Controller. In the telecom world, most telecom networks have evolved to separate the management and part of the control plane from the boxes that carry the data traffic.
Summarizing the points above, meshes of Ethernet switches with an external management plane with multipath traffic where flows are kept in order is evolutionary, not revolutionary, and is likely to become mainstream. At least one major company, Juniper, has made a big public statement about their endorsement of this approach. I'd call all of these "flow-based routing".
Juniper and other vendors’ proprietary approaches notwithstanding, this is an area that cries out for standards. The Open Networking Foundation (ONF), was founded to promote standards in this area, starting with OpenFlow. Within a couple of months, the sixty+ members of ONF will be celebrating their first year anniversary. Each member has, I am led to believe, paid tens of thousands of dollars to join. While the OpenFlow protocol has a ways to go before it is widely adopted, it has real momentum.
#Nathan: OpenFlow 1.1 actually adds some primitives that enable the use of multiple links via the Multipath Proposal.
An excellent view of OpenFlow by Simon Crosby
http://community.citrix.com/display/ocb/2011/03/21/The+Rise+of+the+Software+Defined+Network
More context on SDN which discusses IETF's SDN initiative and ONF's Openflow. Working in conjuction is a powerful combination http://bit.ly/A8xYso
Nathan, Excellent historical account and overview of openflow. Thanks!
You've hit on the points that I've been wrapping my head around as to why Openflow might not be widely adopted. Since it was designed to be open to allow researcher the ability to run experimental protocols and not necessarily be "compatible with" the big players Cisco/HP/etc. it puts itself into niche (although potentially big) market, more on this later. And as you've stated it's recieved some adoption in the "cloud data centers (CDC)" e.g. google, facebook, etc because they need to exploit experimental protocols to gain a competitive advantage or optimize for their application.
As you've stated some switch vendors have added openflow capability to capitalize on the niche need in academia and potentially sell into the CDC; google, facebook. This is potentially a big market (or bubble if you're pessimistic).
The problem that I see is that the majority of the market (80% or more) is enterprise IT data centers. The requirements here is for stable, compatible networking. Open and less expensive would be nice, but not at the cost of the former.
One could think of a day where corporate IT is partially or completely cloud-sourced where QoS is maintained by the cloud provider. In this case, experimental protocols could be leveraged to provide a competitive advantaged for speed or QoS. In which case; openflow could play a more dominant roll. I personally think this scenario is many years off.
So, the conclusion I come to is that other than in research and perhaps CDCs (google, facebook), the market is pretty small. I suppose that if researchers use openflow to come up with a better protocol for say link aggregation, or congestion management, then eventually Cisco and HP will provide those in their standard offering because their customers will demand it. So openflow could be a market influencer (via the research community), but it would not be a market disruptor.
Do you agree with my conclusions? Thanks for your input.

Hardware-level anti-piracy protection

My friend has a company that sells hardware products (like a finger print reader) to customers. In each of their machine, they embed an "encryption chip", which is like a dongle (but embedded into their product, not a usb stick): the software on machine queries certain information from the chip through encryption (both the software and dongle share the encryption key) to continue execution. This is mainly to prevent piracy (making a copy of the machine and software).
The problem is, these embedded dongles are purchased from third-party security companies. So there are pirates out there that purchase an embedded dongle from the same security company, then use a microscope to read the chip's actual content and that way they can fairly easily manage to get the encryption key, thus rendering the dongle embedded in the customer's product useless.
To resolve this issue, seems there are 2 ways:
1) Find a very reputable and professional security company that specializes in embedded dongles that can prevent this kind of very sophisticated "physical hacking"?
2) Use a type of CPU (or any other hardware component) with a unique serial id that can be read by the software, this way it binds each software to a unique machine. Similar to how ISP's bind the MAC address of your NIC to their server to prevent multiple PC's using the Interent (but those are easily broken by MAC spoofers that can CHANGE the MAC of a NIC).
Any other suggestions/guidance? Thanks!
Rule 1: Piracy cannot be prevented. There are no technical means possible that magically allow some person to use the product and some person not to use the product.
It's impossible to tell valid people from invalid people. They're all people. They always seem to know the passwords, have the dongles, be running on the approved CPU, have the right IP address, have the right shoe size.
Even fingerprints can be spoofed (watch Mythbusters for proof.)
This is a matter of trust, not technology. How does a machine know whom to trust?
In fact, it cannot know whom to trust. Only a person establishes a trusting relationship with another person.
Machines are just machines. Any time you allow one person to use the machine, another person can (and eventually will) gain access.
I've worked on a number of products with software and hardware measures to try to block piracy. Ultimately, you can't block pirates, because if they can't spoof the "key" they can just hack your code to skip the check for the key -- even if your code is compiled.
What you do achieve by using stronger anti-piracy measures is to inconvenience all your law-abiding customers and make it hard to use your product.
The best anti-piracy solution I know is to make a product whose value is access that the software gives to data or services not installed on the customer's machine. For instance, a license on an internet site that you control, so you can monitor customer's use of their license and disable it if you detect them trying to use their license illicitly.
Here is a very good DRM article:
http://www.authorama.com/microsoft-research-drm-talk-2.html
In DRM, the attacker is also the
recipient. It’s not Alice and Bob and
Carol, it’s just Alice and Bob. Alice
sells Bob a DVD. She sells Bob a DVD
player. The DVD has a movie on it –
say, Pirates of the Caribbean – and
it’s enciphered with an algorithm
called CSS – Content Scrambling
System. The DVD player has a CSS
un-scrambler.
Now, let’s take stock of what’s a
secret here: the cipher is well-known.
The ciphertext is most assuredly in
enemy hands, arrr. So what? As long as
the key is secret from the attacker,
we’re golden.
But there’s the rub. Alice wants Bob
to buy Pirates of the Caribbean from
her. Bob will only buy Pirates of the
Caribbean if he can descramble the
CSS-encrypted VOB – video object – on
his DVD player. Otherwise, the disc is
only useful to Bob as a
drinks-coaster. So Alice has to
provide Bob – the attacker – with the
key, the cipher and the ciphertext.
Hilarity ensues.
Just about the only way to limit pircay of your software is to obfuscate the activation/regiustration algorithm as much as possible. Here is how the big boys do it:
http://en.wikibooks.org/wiki/X86_Disassembly/Code_Obfuscation
DRM is bad for your customers and bad for you, and in the end, if your product is good enough, the pirates are going to get it.
So, if you can't win, why not join them?
Stop DRM. Watermark all copies individually and record who you sell what to. Don't tell them you did. Give them a license key.
Wait until the pirates start their operation.
Check the pirated copies, sue the company(ies) selling or leaking the code to the pirates for big cash. Unlike individuals, if they sold it, they must keep a record.

Compare Quagga to XORP

What do you think of Quagga compared to XORP as a dynamic software routing engine? What are the technical merits of each engine comparatively? Additionally, what do most people think of them from a programming view. Who has manipulated networks using these enginers? I was wondering from an OSPF, routing, BGP protocol user's perpspective.
The following does not answer your question completely, but the Vyatta open source routers and the OpenSolaris customer gateway software for Amazon VPC both use quagga to implement BGP support.
From the wikipedia entry for XORP,
"The software suite was selected
commercially as the routing platform
for the Vyatta line of products in its
early releases, but later has been
replaced with quagga.
What do you think of Quagga compared to XORP as a dynamic software routing engine?
It is one of many options, but not particularly of very much use to you based upon your questions/information that you posted here. Have you tried looking into some of the alternatives such as (nothing comes to mind)?
What are the technical merits of each engine comparatively?
Small, fast, oddly placed, optimized, super-heroic and more filler for a resume.
Additionally, what do most people think of them from a programming view.
I can't speak for most people, but I for myself do not give it much credit or merit, or well... you know what I mean.
Who has manipulated networks using these enginers?
I could not find specific references, but I do remember reading that both Disney and the 'famous' YUV corporation of South Africa both played with this notion before. I believe Disney abandoned it with the fall of Michael Eisner.
I was wondering from an OSPF, routing, BGP protocol user's perpspective.
I am a BGP protocol user's prospective. Hopefully we hear from OSPF and routing user's perspectives shortly.
Good question.

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