Implementing Network Protocols as Distributed Logic Programs
By Boon Thau Loo, University of Pennsylvania
Declarative networking [1–4] is an application of database query-language and processing techniques to the domain of networking. Declarative networking is based on the observation that network protocols deal at their core with computing and maintaining distributed state (e.g., routes, sessions, performance statistics) according to basic information locally available at each node (e.g., neighbor tables, link measurements, local clocks) while enforcing constraints such as local routing policies. Recursive query languages studied in the deductive database literature  are a natural fit for expressing the relationship between base data, derived data, and the associated constraints. Simple extensions to these languages and their implementations enable the natural expression and efficient execution of network protocols.
Declarative networking aims to accelerate the process of specifying, implementing, experimenting with and evolving designs for network architectures. Declarative networking can reduce program sizes of distributed protocols by orders of magnitude relative to traditional approaches. In addition to serving as a platform for rapid prototyping of network protocols, declarative networking also open up opportunities for automatic protocol optimization and hybridization, program checking and debugging.
This paper presents an introduction to declarative networking using a simple routing protocol example. For more details on declarative networking related projects, refer to the NetDB@Penn website , and the RapidNet  declarative networking engine.
Declarative Networking By Example
The Network Datalog (NDlog) language used in declarative networking is based on extensions to traditional Datalog, a well-known recursive query language designed for querying graph-structured data in a centralized database. NDlog’s integration of networking and logic is unique from the perspectives of both domains. As a network protocol language, it is notable for the absence of any communication primitives like “send” or “receive”; instead, communication is implicit in a simple high-level specification of data partitioning. In comparison to traditional logic languages, it is enhanced to capture typical network realities including distribution, link-layer constraints on communication (and hence deduction), and soft-state semantics.
We introduce Network Datalog (NDlog), the declarative network using an example program shown below that implements the Path-vector protocol, which computes in a distributed fashion, for every node, the shortest paths to all other nodes in a network. The path-vector protocol is used as the base routing protocol for exchanging routes among Internet Service Providers.
sp1 path(@Src,Dest,Path,Cost) :- link(@Src,Dest,Cost), Path = f_init(Src,Dest). sp2 path(@Src,Dest,Path,Cost) :- link(@Src,Nxt,Cost1), path(@Nxt,Dest,Path2,Cost2), Cost = Cost1+Cost2, Path = f_concatPath(Src,Path2). sp3 spCost(@Src,Dest,min<Cost>) :- path(@Src,Dest,Path,Cost). sp4 shortestPath(@Src,Dest,Path,Cost) :- spCost(@Src,Dest,Cost), path(@Src,Dest,Path,Cost). Query shortestPath(@Src,Dest,Path,Cost).
The program has four rules (which for convenience we label sp1-sp4), and takes as input a base (extensional) relation link(Src, Dest, Cost). Rules sp1-sp2 are used to derive “paths” in the graph, represented as tuples in the derived (intensional) relation path(Src,Dest,Path,Cost). The Src and Dest fields represent the source and destination endpoints of the path, Path is the actual path from Src to node Dest. The number and types of fields in relations are inferred from their (consistent) use in the program’s rules.
Since network protocols are typically computations over distributed network state, one of the important requirements of NDlog is the ability to support rules that express distributed computations. NDlog builds upon traditional Datalog by providing control over the storage location of tuples explicitly in the syntax via location specifiers. Each location specifier is a field within a predicate that dictates the partitioning of the table. To illustrate, in the above program, each predicate has an “@” symbol prepended to a single field denoting the location specifier. Each tuple generated is stored at the address determined by its location specifier. For example, each path and link tuple is stored at the address held in its first field @Src.
Rule sp1 produces path tuples directly from existing link tuples, and rule sp2 recursively produces path tuples of increasing cost by matching (joining) the destination fields of existing links to the source fields of previously computed paths. The matching is expressed using the repeated Nxt variable in link(Src,Nxt,Cost1) and path(Nxt,Dest,Path2,Cost2) of rule sp2. Intuitively, rule sp2 says that “if there is a link from node Src to node Nxt, and there is a path from node Nxt to node Dest along a path Path2, then there is a path Path from node Src to node Dest where Path is computed by prepending Src to Path2”. The matching of the common Nxt variable in link and path corresponds to a join operation used in relational databases.
Given the path relation, rule sp3 derives the relation spCost(Src,Dest,Cost) that computes the minimum cost Cost for each source and destination for all input paths. Rule sp4 takes as input spCost and path tuples and then finds shortestPath(Src,Dest,Path,Cost) tuples that contain the shortest path Path from Src to Dest with cost Cost. Last, as denoted by the Query label, the shortestPath table is the output of interest.
Shortest Path Execution Example
We step through an execution of the shortest-path NDlog program above to illustrate derivation and communication of tuples as the program is computed. We make use of the example network in Figure 1. Our discussion is necessarily informal since we have not yet presented our distributed implementation strategies; in the next section, we show in greater detail the steps required to generate the execution plan. Here, we focus on a high-level understanding of the data movement in the network during query processing.
For ease of exposition, we will describe communication in synchronized iterations, where at each iteration, each network node generates paths of increasing hop count, and then propagates these paths to neighbor nodes along links. We show only the derived paths communicated along the solid lines. In actual query execution, derived tuples can be sent along the bidirectional network links (dashed links).
In the 1st iteration, all nodes initialize their local path tables to 1-hop paths using rule sp1. In the 2nd iteration, using rule sp2, each node takes the input paths generated in the previous iteration, and computes 2-hop paths, which are then propagated to its neighbors. For example, path(@a,d,[a,b,d],6) is generated at node b using path(@b,d,[b,d],1) from the 1st iteration, and propagated to node a. In fact, many network protocols propagate only the nextHop and avoid sending the entire path vector.
As paths are computed, the shortest one is incrementally updated. For example, node a computes the cost of the shortest path from a to b as 5 with rule sp3, and then finds the corresponding shortest path [a,b] with rule sp4. In the next iteration, node a receives path(@a,b,[a,c,b],2) from node c, which has lower cost compared to the previous lowest cost of 5, and hence shortestPath(@a,b,[a,c,b],2) replaces the previous tuple (the first two fields of source and destination are the primary key of this relation).
Interestingly, while NDlog is a language to describe networks, there are no explicit communication primitives. All communication is implicitly generated during rule execution as a result of data placement specifications. For example, in rule sp2, the path and link predicates have different location specifiers, and in order to execute the rule body of sp2 based on their matching fields, link and path tuples have to be shipped in the network. It is the movement of these tuples that generates the messages for the resulting network protocol.
 B. T. Loo, T. Condie, M. Garofalakis, D. E. Gay, J. M. Hellerstein, P. Maniatis, R. Ramakrishnan, T. Roscoe, and I. Stoica. Declarative Networking: Language, Execution and Optimization. In Proceedings of ACM SIGMOD International Conference on Management of Data, 2006.
 B. T. Loo, T. Condie, M. Garofalakis, D. E. Gay, J. M. Hellerstein, P. Maniatis, R. Ramakrishnan, T. Roscoe, and I. Stoica. Declarative Networking. In Communications of the ACM (CACM), 2009.
 B. T. Loo, T. Condie, J. M. Hellerstein, P. Maniatis, T. Roscoe, and I. Stoica. Implementing Declarative Overlays. In Proceedings of ACM Symposium on Operating Systems Principles, 2005.
 B. T. Loo, J. M. Hellerstein, I. Stoica, and R. Ramakrishnan. Declarative Routing: Extensible Routing with Declarative Queries. In Proceedings of ACM SIGCOMM Conference on Data Communication, 2005.
 NetDB@Penn. http://netdb.cis.upenn.edu/.
 R. Ramakrishnan and J. D. Ullman. A Survey of Research on Deductive Database Systems. Journal of Logic Programming, 23(2):125–149, 1993.
 RapidNet Declarative Networking Engine. http://netdb.cis.upenn.edu/rapidnet/.