The net is a fertile place where new ideas/products surface quite often.
We have already come across many innovative ideas such as Peer-to-Peer file sharing,
distributed computing etc. Parasitic computing is a new in this category. Reliable
communication on the Internet is guaranteed by a standard set of protocols, used
by all computers. The Notre Dame computer scientist showed that these protocols
could be exploited to compute with the communication infrastructure, transforming
the Internet into a distributed computer in which servers unwittingly perform
computation on behalf of a remote node.
this model, known as "parasitic computing", one machine forces target
computers to solve a piece of a complex computational problem merely by engaging
them in standard communication. Consequently, the target computers are unaware
that they have performed computation for the benefit of a commanding node. As
experimental evidence of the principle of parasitic computing, the scientists
harnessed the power of several web servers across the globe, which-unknown to
them-work together to solve an NP complete problem.
Sending a message through the Internet is a sophisticated process regulated by
layers of complex protocols. For example, when a user selects a URL (uniform resource
locator), requesting a web page, the browser opens a transmission control protocol
(TCP) connection to a web server. It then issues a hyper-text transmission protocol
(HTTP) request over the TCP connection. The TCP message is carried via the Internet
protocol (IP), which might break the message into several packages, which navigate
numerous routers between
source and destination. When an HTTP request reaches its target web server, a
response is returned via the same TCP connection to the user's browser. The original
message is reconstructed through a series of consecutive steps, involving IP and
TCP; it is finally interpreted at the HTTP level, eliciting the appropriate response
(such as sending the requested web page). Thus, even a seemingly simple request
for a web page involves a significant amount of computation in the network and
at the computers at the end points.
a `parasitic computer' is a realization of an abstract machine for a distributed
computer that is built upon standard Internet communication protocols. We use
a parasitic computer to solve the well known NP-complete satisfiability problem,
by engaging various web servers physically located in North America, Europe, and
Asia, each of which unknowingly participated in the experiment. Like the SETI@home
project, parasitic computing decomposes a complex problem into computations that
can be evaluated independently and solved by computers connected to the Internet;
unlike the SETI project, however, it does so without the knowledge of the participating
servers. Unlike `cracking' (breaking into a computer) or computer viruses, however,
parasitic computing does not compromise the security of the targeted servers,
and accesses only those parts of the servers that have been made explicitly available
for Internet communication.
A problem is assigned to the NP
(nondeterministic polynomial time) class if it is verifiable in polynomial time
by a Nondeterministic Turing Machine (A nondeterministic Turing Machine is a "parallel"
Turing Machine which can take many computational paths simultaneously, with the
restriction that the parallel Turing machines cannot communicate.). A problem
is NP-hard if an algorithm for solving it can be translated into one for solving
any other NP-problem. NP-hard therefore means "at least as hard as any NP-problem",
although it might, in fact, be harder. A problem which is both NP and NP-hard
is said to be an NP-Complete problem. Examples of NP-Complete problems are the
traveling salesman problem and the satisfiability problem.
`satisfiability' (or SAT) problem involves finding a solution to a Boolean equation
that satisfies a number of logical clauses. For example, (x1 XOR x2) AND (x2 AND
x3) in principle has 23 potential solutions, but it is satisfied only by the solution
x1 = 1, x2 = 0, and x3 = 1. This is called a 2-SAT problem because each clause,
shown in parentheses, involves two variables. The more difficult 3-SAT problem
is known to be NP complete, which in practice means that there is no known polynomial-time
algorithm which solves it. Indeed, the best known algorithm for an n-variable
SAT problem scales exponentially with n . Here we follow a brute-force approach
by instructing target computers to evaluate, in a distributed fashion, each of
the 2n potential solutions.
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