BestHost4web

This blog is our old blog which was built on www.besthost4web.com and for some reasons we rebuilt our blog site on www.besthost4web.com so now we change the old blog site url to blog.besthost4web.com. If you would like to learn more about this please visit http://www.besthost4web.com/2010/02/05/besthost4web-old-blog-data-recovered

We’re glad to see you on this blog, however,this is our old blog site and we’re migrating our old posts to the new blog site. If you would like to contact us or leave your comments on our website please visit www.besthost4web.com

Thanks for your cooperation!

This article is moved to our new blog site at http://www.besthost4web.com/2010/02/08/domain-tasting

Domain tasting is the practice of a domain name registrant using the five-day "grace period" (the Add Grace Period or AGP) at the beginning of the registration of an ICANN-regulated second-level domain to test the marketability of the domain. During this period, when a registration must be fully refunded by the domain name registry, a cost-benefit analysis is conducted by the registrant on the viability of deriving income from advertisements being placed on the domain’s website.

Domains that are deemed "successes" and retained in registrant’s portfolio often represent domains that were previously used and have since expired, misspellings of other popular sites, or generic terms that may receive type-in traffic. These domains are usually still active in search engine and other hyperlinks and therefore receive enough traffic such that advertising revenue exceeds the cost of the registration. The registrant may also derive revenue from eventual sale of the domain, at a premium, to a third party.

In January 2008, ICANN proposed several possible solutions, including that the exemption on transaction costs (US$ 0.20) during the five-day grace period be abandoned, which would effectively make the practice of domain tasting not viable. The ICANN operating plan and budget for Fiscal Year 2009 included a section intended to deal with the problem of Domain Tasting. The transaction fee of $0.20 will be applied to domains deleted in the Add Grace Period where the number of such domains exceeds 10% of the net new registrations or 50 domains, whichever is greater. The "net new registrations" is defined as the number of new registrations less the number of domains deleted in the Add Grace Period. The ICANN operating plan and budget was approved at the ICANN board meeting in Paris, France on 26 June 2008.

Domain tasting should not be confused with domain kiting, which is the process of deleting a domain name during the five-day grace period and immediately re-registering it for another five-day period. This process is repeated any number of times with the end result of having the domain registered without ever actually paying for it.

Controversy

The practice is controversial as practitioners typically register and delete many hundreds of thousands of domain names under this practice, with these temporary registrations far exceeding the number of domain names actually purchased.

In April 2006, out of 35 million registrations, only a little more than 2 million were permanent or actually purchased. By February 2007, the CEO of Go Daddy reported that of 55.1 million domain names registered, 51.5 million were canceled and refunded just before the 5 day grace period expired and only 3.6 million domain names were actually kept.

Some claim domain name registries such as VeriSign and the Public Interest Registry have turned a blind eye to the practice as it has dramatically increased the number of registrations secured and renewed. However, there are proposals by registries to introduce measures that would reduce or eliminate the practice.

In January 2008, Network Solutions was publicly accused of this practice when the company began reserving all domain names searched on their website for five days, a practice known as domain name front running.

Google has recently said that their AdSense program will now look for domain names that are repeatedly registered and dropped. They say they will drop these domains (but they don’t mention banning the users) from the AdSense program.

Domain hijacking is the process by which internet domain names are stolen from the rightful registrant.

Many people confuse domain hijacking with the reregistration of an expired domain by a new party. One is a legal process and one is not. Domain hijacking is theft, while if a name owner does not renew a name he or she is no longer the owner and it is available for someone else to register.

Domain theft

Domain theft is an aggressive form of domain hijacking that usually involves an illegal act. In most cases, identity theft is used to trick the domain registrar into allowing the hijacker to change the registration information to steal control of a domain from the legitimate owner.

Some registrars are quick to set things right when these cases are discovered. However, it is well documented that some registrars will admit no fault in accepting the forged credentials and will refuse to correct the record until forced by legal action. In many of these cases, justice is not done and the hijacker retains control of the domain. The victims of such theft often do not have the resources or willingness to invest the effort necessary to regain control of their domain, which may require a lawsuit or a lengthy and time-consuming arbitration process, especially if the hijacker and victim are in different countries. Hackers that have hijacked a domain can do anything with that name, including putting up their own website or redirecting those who visit the address to another site.

Prevention

Extensible Provisioning Protocol is used for many TLD registries, and uses an authorization code issued exclusively to the domain registrant as a security measure to prevent unauthorized transfers.

A computer cluster is a group of tightly coupled computers that work together closely so that in many respects they can be viewed as though they are a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area networks. Clusters are usually deployed to improve performance and/or availability over that provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.

Cluster categorizations

High-availability (HA) clusters

High-availability clusters (also known as failover clusters) are implemented primarily for the purpose of improving the availability of services which the cluster provides. They operate by having redundant nodes, which are then used to provide service when system components fail. The most common size for an HA cluster is two nodes, which is the minimum requirement to provide redundancy. HA cluster implementations attempt to manage the redundancy inherent in a cluster to eliminate single points of failure. There are many commercial implementations of High-Availability clusters for many operating systems. The Linux-HA project is one commonly used free software HA package for the Linux OSs.

Load-balancing clusters

Load-balancing clusters operate by having all workload come through one or more load-balancing front ends, which then distribute it to a collection of back end servers. Although they are primarily implemented for improved performance, they commonly include high-availability features as well. Such a cluster of computers is sometimes referred to as a server farm. There are many commercial load balancers available including Platform LSF HPC, Sun Grid Engine, Moab Cluster Suite and Maui Cluster Scheduler. The Linux Virtual Server project provides one commonly used free software package for the Linux OS.

High-performance computing (HPC) clusters

High-performance computing clusters are implemented primarily to provide increased performance by splitting a computational task across many different nodes in the cluster, and are most commonly used in scientific computing. Such clusters commonly run custom programs which have been designed to exploit the parallelism available on HPC clusters. HPCs are optimized for workloads which require jobs or processes happening on the separate cluster computer nodes to communicate actively during the computation. These include computations where intermediate results from one node’s calculations will affect future calculations on other nodes.

One of the most popular HPC implementations is a cluster with nodes running Linux as the OS and free software to implement the parallelism. This configuration is often referred to as a Beowulf cluster.

Microsoft offers Windows Compute Cluster Server as a high-performance computing platform to compete with Linux.

Many software programs running on High-performance computing clusters use libraries such as MPI which are specially designed for writing scientific applications for HPC computers.

Grid computing

Grid computing or grid clusters are a technology closely related to cluster computing. The key differences (by definitions which distinguish the two at all) between grids and traditional clusters are that grids connect collections of computers which do not fully trust each other, or which are geographically dispersed. Grids are thus more like a computing utility than like a single computer. In addition, grids typically support more heterogeneous collections than are commonly supported in clusters.

Grid computing is optimized for workloads which consist of many independent jobs or packets of work, which do not have to share data between the jobs during the computation process. Grids serve to manage the allocation of jobs to computers which will perform the work independently of the rest of the grid cluster. Resources such as storage may be shared by all the nodes, but intermediate results of one job do not affect other jobs in progress on other nodes of the grid.

An example of a very large cluster is the Folding@home project. It is analyzing data that is used by researchers to find cures for diseases such as Alzheimer’s and cancer. Another large project is the SETI@home project, which may be the largest distributed cluster in existence. It uses approximately three million home computers all over the world to analyze data from the Arecibo Observatory radiotelescope, searching for evidence of extraterrestrial intelligence.

Implementations

The TOP500 organization’s semiannual list of the 500 fastest computers usually includes many clusters. TOP500 is a collaboration between the University of Mannheim, the University of Tennessee, and the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. As of November 2006, the top supercomputer is the Department of Energy’s IBM BlueGene/L system with performance of 280.6 TFlops.

Clustering can provide significant performance benefits versus price. The System X supercomputer at Virginia Tech, the 28th most powerful supercomputer on Earth as of June 2006, is a 12.25 TFlops computer cluster of 1100 Apple XServe G5 2.3 GHz dual-processor machines (4 GB RAM, 80 GB SATA HD) running Mac OS X and using InfiniBand interconnect. The cluster initially consisted of Power Mac G5s; the rack-mountable XServes are denser than desktop Macs, reducing the aggregate size of the cluster. The total cost of the previous Power Mac system was $5.2 million, a tenth of the cost of slower mainframe computer supercomputers. (The Power Mac G5s were sold off.)

The central concept of a Beowulf cluster is the use of commercial off-the-shelf computers to produce a cost-effective alternative to a traditional supercomputer. One project that took this to an extreme was the Stone Soupercomputer.

However it is worth noting that FLOPs (floating point operations per second), aren’t always the best metric for supercomputer speed. Clusters can have very high FLOPs, but they cannot access all data the cluster as a whole has at once. Therefore clusters are excellent for parallel computation, but much poorer than traditional supercomputers at non-parallel computation.

JavaSpaces is a specification from Sun Microsystems that enables clustering computers via a distributed shared memory.

History

The history of cluster computing is best captured by a footnote in Greg Pfister’s In Search of Clusters: "Virtually every press release from DEC mentioning clusters says ‘DEC, who invented clusters…’. IBM did not invent them either. Customers invented clusters, as soon as they could not fit all their work on one computer, or needed a backup. The date of the first is unknown, but it would be surprising if it was not in the 1960s, or even late 1950s."

The formal engineering basis of cluster computing as a means of doing parallel work of any sort was arguably invented by Gene Amdahl of IBM, who in 1967 published what has come to be regarded as the seminal paper on parallel processing: Amdahl’s Law. Amdahl’s Law describes mathematically the speedup one can expect from parallelizing any given otherwise serially performed task on a parallel architecture. This article defined the engineering basis for both multiprocessor computing and cluster computing, where the primary differentiator is whether or not the interprocessor communications are supported "inside" the computer (on for example a customized internal communications bus or network) or "outside" the computer on a commodity network.

Consequently the history of early computer clusters is more or less directly tied into the history of early networks, as one of the primary motivation for the development of a network was to link computing resources, creating a de facto computer cluster. Packet switching networks were conceptually invented by the RAND corporation in 1962. Using the concept of a packet switched network, the ARPANET project succeeded in creating in 1969 what was arguably the world’s first commodity-network based computer cluster by linking four different computer centers (each of which was something of a "cluster" in its own right, but probably not a commodity cluster). The ARPANET project grew into the Internet — which can be thought of as "the mother of all computer clusters" (as the union of nearly all of the compute resources, including clusters, that happen to be connected). It also established the paradigm in use by all computer clusters in the world today — the use of packet-switched networks to perform interprocessor communications between processor (sets) located in otherwise disconnected frames.

The development of customer-built and research clusters proceeded hand in hand with that of both networks and the Unix operating system from the early 1970s, as both TCP/IP and the Xerox PARC project created and formalized protocols for network-based communications. The Hydra operating system was built for a cluster of DEC PDP-11 minicomputers called C.mmp at C-MU in 1971. However, it was not until circa 1983 that the protocols and tools for easily doing remote job distribution and file sharing were defined (largely within the context of BSD Unix, as implemented by Sun Microsystems) and hence became generally available commercially, along with a shared filesystem.

The first commercial clustering product was ARCnet, developed by Datapoint in 1977. ARCnet was not a commercial success and clustering per se did not really take off until DEC released their VAXcluster product in the 1984 for the VAX/VMS operating system. The ARCnet and VAXcluster products not only supported parallel computing, but also shared file systems and peripheral devices. They were supposed to give you the advantage of parallel processing, while maintaining data reliability and uniqueness. VAXcluster, now VMScluster, is still available on OpenVMS systems from HP running on Alpha and Itanium systems.

Two other noteworthy early commercial clusters were the Tandem Himalaya (a circa 1994 high-availability product) and the IBM S/390 Parallel Sysplex (also circa 1994, primarily for business use).

No history of commodity computer clusters would be complete without noting the pivotal role played by the development of Parallel Virtual Machine (PVM) software in 1989. This open source software based on TCP/IP communications enabled the instant creation of a virtual supercomputer — a high performance compute cluster — made out of any TCP/IP connected systems. Free form heterogeneous clusters built on top of this model rapidly achieved total throughput in FLOPS that greatly exceeded that available even with the most expensive "big iron" supercomputers. PVM and the advent of inexpensive networked PCs led, in 1993, to a NASA project to build supercomputers out of commodity clusters. In 1995 the invention of the "beowulf"-style cluster — a compute cluster built on top of a commodity network for the specific purpose of "being a supercomputer" capable of performing tightly coupled parallel HPC computations. This in turn spurred the independent development of Grid computing as a named entity, although Grid-style clustering had been around at least as long as the Unix operating system and the Arpanet, whether or not it, or the clusters that used it, were named.

Technologies

MPI is a widely-available communications library that enables parallel programs to be written in C, Fortran, Python, OCaml, and many other programming languages.

The GNU/Linux world sports various cluster software; for application clustering, there is Beowulf, distcc, and MPICH. Linux Virtual Server, Linux-HA – director-based clusters that allow incoming requests for services to be distributed across multiple cluster nodes. MOSIX, openMosix, Kerrighed, OpenSSI are full-blown clusters integrated into the kernel that provide for automatic process migration among homogeneous nodes. OpenSSI, openMosix and Kerrighed are single-system image implementations.

Microsoft Windows Compute Cluster Server 2003 based on the Windows Server platform provides pieces for High Performance Computing like the Job Scheduler, MSMPI library and management tools. NCSA’s recently installed Lincoln is a cluster of 450 Dell PowerEdge™ 1855 blade servers running Windows Compute Cluster Server 2003. This cluster debuted at #130 on the Top500 list in June 2006.

DragonFly BSD, a recent fork of FreeBSD 4.8 is being redesigned at its core to enable native clustering capabilities. It also aims to achieve single-system image capabilities.

Clustered hosting technology is designed to eliminate the problems inherent with typical shared hosting infrastructures. This technology provides customers with a "clustered" handling of security, load balancing, and necessary website resources.

A clustered hosting platform is data-driven, which means that no human interaction is needed to provision a new account to the platform.

Clustered hosting "virtualizes" the resources beyond the limits of one physical server, and as a result, a website is not limited to one server. They share the processing power of many servers and their applications are distributed in real-time. This means that they can purchase as much computing power as they want from a virtually inexhaustible source, since even the largest customer never consumes more than a fraction of a percent of the total server pool. Customer account changes (to add new resources or change settings) are propagated immediately to every server in the cluster. This is different from typical shared hosting architectures that usually require changes to a configuration file that becomes live after the server is rebooted during off hours, or are pushed on a cyclic basis every few hours.

…

This post is moved to our new blog site already. To read more of this article please simply visit http://www.besthost4web.com/2010/02/05/what-is-clustered-hosting

This post is already moved to our new blog site. Please check it here:
http://www.besthost4web.com/2010/02/05/what-is-bandwidth

Bandwidth is the difference between the upper and lower cutoff frequencies of for example a filter, a communication channel or a signal spectrum, and is typically measured in hertz. Bandwidth in Hertz is a central concept in many fields, including electronics, information theory, radio communications, signal processing, and spectroscopy.

In computer networking literature, digital bandwidth often refers to data rate measured in bit/s, for example channel capacity (digital bandwidth capacity) or throughput (digital bandwidth consumption). The reason for this usage is that the channel capacity in bit/s is proportional to the analogue bandwidth in Hertz according to Hartley’s law.

Meaning of bandwidth in web hosting

In website hosting, the term "bandwidth" is often used metaphorically, to describe the amount of data that can be transferred to or from the website or server, measured in bytes transferred over a prescribed period of time. This can be more accurately described as "Monthly Data Transfer."

Web hosting companies often quote a monthly bandwidth limit for a website, for example 500 gigabytes per month. If visitors to the website download a total greater than 500 gigabytes in one month, the bandwidth limit will have been exceeded.

Overview

Bandwidth is a key concept in many applications. In radio communications, for example, bandwidth is the range of frequencies occupied by a modulated carrier wave, whereas in optics it is the width of an individual spectral line or the entire spectral range.

There is no single universal precise definition of bandwidth, as it is vaguely understood to be a measure of how wide a function is in the frequency domain.

For different applications there are different precise definitions. For example, one definition of bandwidth could be the range of frequencies beyond which the frequency function is zero. This would correspond to the mathematical notion of the support of a function (i.e., the total "length" of values for which the function is nonzero). A less strict and more practically useful definition will refer to the frequencies where the frequency function is small. Small could mean less than 3 dB below (i.e., less than half of) the maximum value, or more rarely 10 dB, or it could mean below a certain absolute value. As with any definition of the width of a function, many definitions are suitable for different purposes.

According to the Shannon-Hartley theorem, the data rate of reliable communication is directly proportional to the frequency range of the signal used for the communication. In this context, the word bandwidth can refer to either the data rate or the frequency range of the communication system (or both).

Analog systems

For analog signals, which can be mathematically viewed as functions of time, bandwidth is the width, measured in hertz, of the frequency range in which the signal’s Fourier transform is nonzero. Because this range of non-zero amplitude may be very broad, this definition is often relaxed so that the bandwidth is defined as the range of frequencies where the signal’s Fourier transform has a power above a certain amplitude threshold, commonly half the maximum value. Bandwidth of a signal is a measure of how rapidly its parameters (e.g. amplitude and phase) fluctuate with respect to time. Hence, the greater the bandwidth, the faster the variation in the signal parameters may be. The wordbandwidth applies to signals as described above, but it could also apply to systems. In the latter case, to say that a system has a certain bandwidth means that the system can process signals of that bandwidth.

A baseband bandwidth is a specification of only the highest frequency limit of a signal. A non-baseband bandwidth is a difference between highest and lowest frequencies.

A commonly used quantity is fractional bandwidth. This is the bandwidth of a device divided by its center frequency. E.g., a device that has a bandwidth of 2 MHz with center frequency 10 MHz will have a fractional bandwidth of 2/10, or 20%.

The fact that real baseband systems have both negative and positive frequencies can lead to confusion about bandwidth, since they are sometimes referred to only by the positive half, and one will occasionally see expressions such as B = 2W, where B is the total bandwidth, and W is the positive bandwidth. For instance, this signal would require a lowpass filter with cutoff frequency of at least W to stay intact.

The 3-dB bandwidth of an electronic filter is the part of the filter’s frequency response that lies within 3 dB of the response at its peak, which is typically at or near its center frequency.

In signal processing and control theory the bandwidth is the frequency at which the closed-loop system gain drops 3 dB below peak.

In photonics, the term bandwidth occurs in a variety of meanings:

• the bandwidth of the output of some light source, e.g., an ASE source or a laser; the bandwidth of ultrashort optical pulses can be particularly large
• the width of the frequency range that can be transmitted by some element, e.g. an optical fiber
• the gain bandwidth of an optical amplifier
• the width of the range of some other phenomenon (e.g., a reflection, the phase matching of a nonlinear process, or some resonance)
• the maximum modulation frequency (or range of modulation frequencies) of an optical modulator
• the range of frequencies in which some measurement apparatus (e.g., a powermeter) can operate
• the data rate (e.g., in Gbit/s) achieved in an optical communication system

Digital systems

When used to discuss digital communication, the meaning of "bandwidth" is clouded by metaphorical use. Technicians sometimes use it as slang for Baud, the rate at which symbols may be transmitted through the system. It is also used more colloquially to describe channel capacity, the rate at which bits may be transmitted through the system (see Shannon Limit). Hence, a digital data bus with a bit rate of 66 Mbit/s on each of 32 separate data lines may properly be said to have a bandwidth of 33 MHz and a capacity of 2.1 Gbit/s – but it would not be surprising to hear such a bus described as having a "bandwidth of 2.1 Gbit/s." Similar confusion exists for voiceband modems, where each symbol carries multiple bits of information so that a modem may transmit 56 kbit/s of information over a phone line with a bandwidth of only 4 kHz and a symbol rate of 8 Kbaud. A related metric which is used to measure the aggregated bandwidth of a whole network is bisection bandwidth.

Bandwidth is also used in the sense of commodity, referring to something limited or something costing money. Thus, communication costs bandwidth, and improper use of someone else’s bandwidth may be called bandwidth theft.

In discrete time systems and digital signal processing, bandwidth is related to sampling rate according to the Nyquist-Shannon sampling theorem.

When Additive white Gaussian noise is present in a digital communication channel, the Shannon-Hartley theorem gives the relationship between the channel’s bandwidth, the channel’s capacity, and the Signal-to-noise ratio (SNR) ratio of the system.