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+# Named Function Networking (NFN) by using the Lambda Calculus 
+
+The Lambda Calculus is a formal mathematical language to represent logic. It consists of variables v, abstractions @v T and applications T T, where v is a variable and T a term.
+
+The Lambda Calculus can be evaluated by using an abstract machine. 
+
+Already the pure Lambda Calculus is Turing complete and can represent any computation.
+
+## The Lambda Calculus in the Network
+To Simplify computation we extended the pure Lambda Calculus to support integer numbers, ICN names, strings and simple operations.
+A string is always between two 'string'
+
+The operations are: 
+ * add p1 p2
+ * sub p1 p2 
+ * mult p1 p2
+ * ifelse p1 p2 p3
+where pX represent the X parameter of the function.
+
+A user can send an interest packet with an computation inside the name and the NFN network will solve the computation. 
+Thereby it is possible to use these operations as well as the Lambda Calculus itself.
+
+An interest packet that contains a computation consists of either two or more components. 
+The last component is always a marker. This marker tags a packet as NFN packet and by this marker it can be distinguish from a normal ICN packet.
+In the component before the NFN marker there is an arbitrary Lambda expression. 
+Futhermore it is possible to put an ICN name before the lambda expression.
+The entire name can be specified as:
+
+/ <ICN name components> / <Lambda expression> / NFN marker
+where the <ICN name> can consist of an arbitrary number of components.
+
+## The call operation
+The call operation is a special extension to the Lambda calculus.
+It enables a user to perform operation outside of the abstract machine.
+These operations can be written in any high level programming language.
+
+The syntax of the call operation is defined as: 
+ *call X p1 ... pX
+where X is the number of the parameters which are expected by the call operation.
+The first parameter p1 is always the name of the function which should be invoked.
+The following parameters are either a variable, a string or a Lambda expression. 
+
+To enable computations by using the call operation the NFN-scala compute server (https://github.com/cn-uofbasel/nfn-scal://github.com/cn-uofbasel/nfn-scala) can be used. 
+A NFN-relay and the NFN compute server communicate over an ICN protocol. To enable this computation a Face with the prefix "/COMPUTE" from the relay to the compute server has to be setup.
+
+To invoke a service, the service has to be published on the network. You will find more to this topic in the documentation of NFN-scala.
+
+It is not possible to interact with data (content objects) from inside the network without using a Compute server.
+
+## Game of Names
+NFN is designed to optimize the location where a computation is executed.
+To do this NFN handles three different cases:
+ * The data are stored in the cache.
+ * The computation can be executed on the location where the data are stored.
+ * The computation can only be executed on the location where the function is stored (the function cannot be transfered over the network, e.g. for security reason)
+
+Additional only computations on data are forward into the network, since for other computation it usually is faster to compute them locally. 
+
+To forward a computation into the network, the nfn relay chooses a parameter of a call operation and prepends this name by using an abstraction.
+To end up with a routable name at the beginning it is required that the relay only considers ICN names for prepending.
+
+The relay will start trying to forward the computation to input data of the call first (input data are ICN names in the parameter list of the call operation)
+It starts with the last ICN name. If no result is returned the relay will choose the next input name.
+If there is no further ICN name in the parameter list, the relay will try to forward the computation to the function name (also an ICN name).
+If this do not return a result, the relay tries to compute the result locally.
+
+Example:
+
+  *NFN call: call 2 /functions/wordcount /data/dataset1
+
+  *Step one: try to route to a input parameter, which is an ICN name. Apply an abstraction to prepend the ICN name: /data/dataset1 (@x call /functions/wordcount x)
+
+  *Step two: if no result if found, try another input parameter. If there is no futher input parameter try to route to the function, by prepending the function name: /functions/wordcount (@x call x /data/dataset1)
+
+  *Step three: Try to compute the result locally by sending it to the local computeserver (if available): /COMPUTE/call 2 /functions/wordcount /data/dataset1 
+
+The relay expects that the result of an interest is either returned from cache or computed on the fly. 
+If a relay receives an interest for a computation it checks if the result can be returned from cache. 
+If no cached result is available the relay checks whether the prepended name is local available. If it is the relay will compute the result locally.
+Otherwise it will forward the interest according to the FIB entries.
+