In computer science, macroprogramming is a programming paradigm aimed at expressing the macroscopic, global behaviour of an entire system of agents or computing devices.^[1] In macroprogramming, the local programs for the individual components of a distributed system are compiled or interpreted from a macro-program typically expressed by a system-level perspective or in terms of the intended global goal.^[1] The aim of macroprogramming approaches is to support expressing the macroscopic interactive behaviour of a whole distributed system of computing devices or agents in a single program, or, similarly, to promote their collective intelligence.^[2] It is not to be confused with macros, the mechanism often found in programming languages (like C or Scala) to express substitution rules for program pieces.

Macroprogramming originated in the context of wireless sensor network programming^[3][4][5] and found renewed interest in the context of the Internet of Things^[6] and swarm robotics.^[7][1]

Macroprogramming shares similar goals (related to programming a system by a global perspective) with multitier programming, choreographic programming, and aggregate computing.

Context and motivation

Programming distributed systems, multi-agent systems, and collectives of software agents (e.g., robotic swarms) is difficult, for many issues (like communication, concurrency, and failure) have to be properly considered. In particular, a general recurrent problem is how to induce the intended global behaviour by defining the behaviour of the individual components or agents involved. The problem can be addressed through learning approaches, such as multi-agent reinforcement learning, or by manually defining the control program driving each component. However, addressing the problem by a fully individual (or single-node) perspective may be error-prone, because it is generally difficult to foresee the overall behaviour emerging from complex networks of activities and interactions (cf. complex systems and emergence). Therefore, researchers have started investigated ways to raise the abstraction level, promoting programming of distributed systems by a more global perspective or in terms of the overall goal to be collectively attained.

Examples

class SelfContainedChannel extends AggregateProgram with SensorDefinitions {
  def isObstacle = sense[Boolean]("obstacle")
  def isSource = sense[Boolean]("source")
  def isDestination = sense[Boolean]("target")
  
  override def main(): Boolean = 
  branch(isObstacle){ false }{ channel(isSource, isDestination, 5) }

  def channel(src: Boolean, dest: Boolean, width: Double): Boolean =
    dilate(distanceTo(src) + distanceTo(dest) <= distanceBetween(src,dest), width)

  type OB[T] = Builtins.Bounded[T]
  def G[V:OB](src: Boolean, field: V, acc: V=>V, metric: =>Double): V =
    rep( (Double.MaxValue, field) ){ dv =>
      mux(src) { (0.0, field) } {
        minHoodPlus {
          val (d, v) = nbr { (dv._1, dv._2) }
          (d + metric, acc(v))
        } } }._2

  def distanceTo(source: Boolean): Double =
    G[Double](source, 0, _ + nbrRange(), nbrRange())

  def broadcast[V:OB](source: Boolean, field: V): V =
    G[V](source, field, x=>x, nbrRange())

  def distanceBetween(source: Boolean, target: Boolean): Double =
    broadcast(source, gradient(target))

  def dilate(region: Boolean, width: Double): Boolean =
    gradient(region) < width
}

% function definition
doSum :: float (float, int) -> (float, int);
doSum(temperature, (sum, count)) { (sum+temperature, count+1) }

% functional reactive program logic
temperatureRegion = rmap(fun(node){ sense("temperature", node) }, world);
sumSignal = rfold(doSum, (0.0, 0), temperatureRegion)
avgSignal = smap(fun((sum,count)){ sum / count }, sumSignal)

BASE <- avgSignal % move such information to the base station

temperatures = Resource.objects.filter(title="temp")
results = [temp.GET() for temp in temperatures]
avg = sum(results) / len(results)
TEMP_THRESHOLD = 24
if avg > TEMP_THRESHOLD:
    Resource.objects.get(title="fan").PUT("on")

SELECT nodeId , temperature WHERE temperature > k FROM sensors SAMPLE PERIOD 5 minutes

See also

• Multitier programming
• Choreographic programming
• Aggregate computing
• Distributed computing

References