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Towards Aggregate Programming in Scala

The document discusses the concept of aggregate programming in Scala, focusing on the challenges and methodologies for programming collective adaptive systems using decentralized approaches. It outlines the theoretical foundations of aggregate computing, introduces the Scala-based language Scafi for implementing these concepts, and emphasizes the importance of abstraction and resilience in system design. Additionally, it highlights practical applications and issues concerning communication, concurrency, and fault-tolerance within distributed systems.

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Towards Aggregate Programming in Scala Roberto Casadei Mirko Viroli Department of Computer Science and Engineering University of Bologna 1st International Workshop on Programming Models and Languages for Distributed Computing (PMLDC), 2016, Rome R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 1 / 46
Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 2 / 46
Context and Issues Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 3 / 46
Context and Issues Context Environment + (Mobile, Large-scale) Networks of { people + devices } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 4 / 46
Context and Issues The systems of ((((((( tomorrow today... Complex/Collective Adaptive Systems (CASs) • Socio-Technical Systems (STS) • Internet of Things (IoT) • Pervasive systems • Ubiquitous systems • Cyber-Physical Systems (CPS) • Smart-cities/buildings/homes, ... R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 5 / 46
Context and Issues Ecosystems of CASs services The crowd engineering example R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 6 / 46
Context and Issues Gathering local context R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 7 / 46
Context and Issues Sensing global patterns of data R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 8 / 46
Context and Issues Crowd detection R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 9 / 46
Context and Issues Crowd anticipation R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 10 / 46
Context and Issues Crowd-aware steering R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 11 / 46
Context and Issues Crowd dispersal R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 12 / 46
Context and Issues Crowd evacuation upon alerts R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 13 / 46
Context and Issues What? How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
Context and Issues What? How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
Context and Issues What? How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
Aggregate Computing Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 15 / 46
Aggregate Computing The origins: self-organisation patterns [FMSM+ 13] R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 16 / 46
Aggregate Computing The origins: space-time programming [BV15] Space-oriented computation – Thematic areas (a) Intensive computing (b) Computation embedded in space (c) Space computation Decentralised spatial computing Computing “somewhere” [Duc13] • Location-related information • Spatial constraints to communication Space-time programming [BV15] • Computation expressed in terms of properties of the physical time and space in which it occurs • Spatial abstractions R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 17 / 46
Aggregate Computing Discrete system vs. continuous space-time R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 18 / 46
Aggregate Computing Computational fields • (Abstract interpretation) Mapping space-time to computational objects • (Concrete interpretation) Mapping devices to values: φ : δ → • “Distributed” data structure working as the global abstraction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 19 / 46
Aggregate Computing The Computational Field Calculus I Field calculus A calculus of computational fields [DVB16] • Functional • Basis set of operators for field manipulation • Minimal expressive model ≈ Space-time universal Higher-Order Field Calculus (HOFC) [DVPB15] • First-class distributed functions • Code mobility R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 20 / 46
Aggregate Computing The Computational Field Calculus II Syntax e = x | v | eλ(e) | rep(e0){eλ} | nbr{e } Expression v = <standard-values> | λ Value λ = f | o | (x) ⇒ e Functional value F = def f(x){e } Function definition • v includes numbers, booleans, strings, collections, custom ADTs, etc. • f is a user-defined function • o is a built-in functional operator (e.g., pure math or a sensor) A sort of λ-calculus where values are computational fields, plus (i) a construct for field evolution (rep) (ii) a construct for interacting with the neighbourhood (nbr) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 21 / 46
Aggregate Computing The Computational Field Calculus III Global-level semantics, intuitively 0 (x)=>x+1 true t<0,1> ()0 1 + - 1 -1 ef(0,1) ef rep 0 (x)=>x+1 t v0 t v1 .. rep(0){(x)=>x+1} nbr de nbr{e} φd=[d1→v1,..,dn→vn] R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 22 / 46
Aggregate Computing Example: the channel I R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 23 / 46
Aggregate Computing Example: the channel II R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 24 / 46
Aggregate Computing Aggregate functions [DVPB15] Left: branch(c){ f(x) }{ g(x) } Right: (branch(c){ f }{ g })(x) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 25 / 46
Aggregate Computing Aggregate programming [BPV15] Programming (collective adaptive) systems: viewpoints (a) Local viewpoint (device-centric view) (b) Global viewpoint (aggregate view) Aggregate programming: what Goal: programming the collective behaviour of aggregates Global-to-local mapping Aggregate programming: how Prominent approach founded on field calculus and self-org patterns Composable self-organisation • Self-stabilisation • Building blocks for resilient coordination R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 26 / 46
Aggregate Computing Device-centric programming Issues • Local-to-global mapping problem (generally intractable) • Explicit design of adaptation and communication • Mixing of concerns — state management, interaction, adaptation, resiliency, etc. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 27 / 46
Aggregate Computing Device-centric programming Issues • Local-to-global mapping problem (generally intractable) • Explicit design of adaptation and communication • Mixing of concerns — state management, interaction, adaptation, resiliency, etc. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 27 / 46
Aggregate Computing Aggregate-level programming Collective services on the computational fabric available in space • Automatic global-to-local mapping • Implicit adaptation and communication • Composition of loosely coupled services R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 28 / 46
Aggregate Computing Aggregate-level programming Collective services on the computational fabric available in space • Automatic global-to-local mapping • Implicit adaptation and communication • Composition of loosely coupled services R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 28 / 46
Aggregate Computing Aggregate (computing) systems Structure A set of networked devices • Each one is given the same field calculus program Dynamics Each device computes the given program at asyn / partially-sync rounds of execution (1) Retrieve context ⇐ Messages from neighbours ⇐ Sensor values (2) Aggregate program execution ⇒ export (3) Broadcast export to neighbourhood (4) Execute actuators R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 29 / 46
Aggregate Computing Aggregate computing and abstraction (Partial) insensitiveness to system structure and situation details Aggregate behaviours are highly insensitive to: • network size • network density • network topology This makes algorithms intrinsically robust to failures and changes to working conditions. (Partial) insensitiveness to execution strategy Note: network nodes ultimately correspond to components operating in some context via sensors/actuators. Aggregate computations can be carried out in different ways: • Computing nodes and “native” local communication; • Computations performed by a central server; • Computations performed in the cloud; ... Idea: opportunistically exploit the underlying infrastructure R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 30 / 46
Aggregate Computing Aggregate Programming Stack R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 31 / 46
Aggregate Computing Approaching the engineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
Aggregate Computing Approaching the engineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
Aggregate Computing Approaching the engineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
Aggregate Computing Approaching the engineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
Aggregate Computing Approaching the engineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
Aggregate Computing Aggregate Programming Toolchain Up to a few months ago.. Protelis [PVB15] + Alchemist simulator [PMV13] • External DSL • Focus on simulation Technological gap: there is no aggregate programming framework, aimed at the construction of real-world applications, integrated in a ≈mainstream language ⇒ SCAFI (scala with computational fields) • Scala-internal DSL for expressing aggregate computations • Distributed platform for execution of aggregate systems SCAFI: where? http://scafi.apice.unibo.it • A more serious release soon :) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 33 / 46
Aggregate Programming in Scala Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 34 / 46
Aggregate Programming in Scala SCAFI: Scala with Computational Fields Goal The goal is to provide an integrated framework for building systems with aggregate programming • Scala-internal DSL for expressing aggregate computations • Linguistic support + execution support (interpreter/VM) • Correct, complete, efficient implementation of the HOFC semantics • Distributed platform for execution of aggregate systems • Support for multiple architectural styles and system configurations • Support for code mobility Issues • Implementation of a language for the field calculus within Scala • Constructs as method calls • Integration with the Scala type system • Development of a distributed middleware • Typical issues from distribution (concurrency, communication, fault-tolerance) ⇒ Akka actor framework R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 35 / 46
Aggregate Programming in Scala SCAFI: language trait Constructs { def rep[A](init: A) (fun: (A) => A): A def nbr[A](expr: => A): A def foldhood[A](init: => A) (acc: (A,A)=>A) (expr: => A): A def branch[A](cond: => Boolean) (th: => A) (el: => A): A def aggregate[A](f: => A): A def sense[A](name: LSNS): A def nbrvar[A](name: NSNS): A } Where are computational fields? R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 36 / 46
Aggregate Programming in Scala Example: the channel I def channel(src: Boolean, dest: Boolean, width: Double) = distanceTo(src) + distanceTo(dest) <= distBetween(src, dest) + width R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 37 / 46
Aggregate Programming in Scala Example: the channel (full code) def channel(src: Boolean, dest: Boolean, width: Double) = distanceTo(src) + distanceTo(dest) <= distBetween(src, dest) + width def nbrRange(): Double = nbrvar[Double](NBR_RANGE_NAME) def G[V:OB](src: Boolean, field: V, acc: V=>V, metric: =>Double): V = rep( (Double.MaxValue, field) ){ dv => mux(src) { (0.0, field) } { minHoodMinus { val (d, v) = nbr { (dv._1, dv._2) } (d + metric, acc(v)) } } }._2 def broadcast[V:OB](source: Boolean, field: V): V = G[V](source, field, x=>x, nbrRange()) def distanceTo(source: Boolean): Double = G[Double](source, 0, _ + nbrRange(), nbrRange()) def distBetween(source: Boolean, target: Boolean): Double = broadcast(source, distanceTo(target)) def isSource = sense[Boolean]("source") def isObstacle = sense[Boolean]("obstacle") R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 38 / 46
Aggregate Programming in Scala P2p, ad-hoc net // STEP 1: CHOOSE INCARNATION import it.unibo.scafi.incarnations.{ BasicActorP2P => Platform } // STEP 2: DEFINE AGGREGATE PROGRAM SCHEMA class Demo0C_AggregateProgram extends Platform.AggregateProgram { override def main(): Any = foldhood(0){_ + _}(1) } // STEP 3: DEFINE MAIN PROGRAM object Demo0C_MainProgram extends Platform.CmdLineMain 1) Demo_MainProgram -h 127.0.0.1 -p 9000 -e 1:2,4,5;2;3 --subsystems 127.0.0.1:9500:4:5 --program "demos.Demo_AggregateProgram" 2) Demo_MainProgram -h 127.0.0.1 -p 9500 -e 4;5:4 --program "demos.Demo_AggregateProgram" R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 39 / 46
Aggregate Programming in Scala Server-based, mobile spatial net I import it.unibo.scafi.distrib.actor.server.{SpatialPlatform => SpatialServerBasedActorPlatform} import it.unibo.scafi.incarnations.BasicAbstractActorIncarnation import it.unibo.scafi.space.{Point2D, BasicSpatialAbstraction} object Demo3_Platform extends BasicAbstractActorIncarnation with SpatialServerBasedActorPlatform with BasicSpatialAbstraction with Serializable { override val LocationSensorName: String = "LOCATION_SENSOR" override type P = Point2D override def buildNewSpace[E](elems: Iterable[(E,P)]): SPACE[E] = new Basic3DSpace(elems.toMap) { override val proximityThreshold = 1.1 } } // STEP 2: DEFINE AGGREGATE PROGRAM SCHEMA class Demo3_AggregateProgram extends Demo3_Platform.AggregateProgram { def hopGradient(source: Boolean): Double = { rep(Double.PositiveInfinity){ hops => { mux(source) { 0.0 } { 1+minHood(nbr{ hops }) } } } } def main() = hopGradient(sense("source")) } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 40 / 46
Aggregate Programming in Scala Server-based, mobile spatial net II // STEP 3: DEFINE MAIN PROGRAMS object Demo3_ServerMain extends Demo3_Platform.ServerCmdLineMain { override def refineSettings(s: Demo3_Platform.Settings) = { s.copy(profile = s.profile.copy( serverGuiActorProps = tm => Some(ServerGUIActor.props(Demo3_Platform, tm )) )) } } object Demo4_MainProgram extends Demo3_Platform.CmdLineMain { override def refineSettings(s: Demo3_Platform.Settings) = { s.copy(profile = s.profile.copy( devGuiActorProps = ref => Some(DevGUIActor.props(Demo3_Platform, ref)) )) } override def onDeviceStarted(dm: Demo3_Platform.DeviceManager, sys: Demo3_Platform.SystemFacade) = { dm.addSensorValue(Demo3_Platform.LocationSensorName, Point2D(dm.selfId%5,( dm.selfId/5.0).floor)) dm.addSensorValue("source", dm.selfId==4) dm.start } } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 41 / 46
Aggregate Programming in Scala Server-based, mobile spatial net III R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 42 / 46
Summary Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 43 / 46
Summary Summary: key ideas and future directions Aggregate programming • An ((((((( programmingengineering approach to CASs • Formally grounded in the Field Calculus • Allows to compose “emergent” phenomena • Provides layers of building blocks (proven to self-stabilise!) A Scala framework for aggregate programming is out! • Supports the construction of aggregate systems • Integrated in Scala No need to learn ad-hoc external DSLs Easier to get started and play • Better release (e.g., to Maven Central) soon, I promise :) Many open problems [VB] (want to join?) • Space-time universality • Fields vs. objects/agents/processes/worflows • Better resilience properties than self-stabilisation • Adapting the execution strategy to available infrastructure (e.g., cloud, fog, ...) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 44 / 46
Appendix References References I [BPV15] Jacob Beal, Danilo Pianini, and Mirko Viroli. Aggregate Programming for the Internet of Things. IEEE Computer, 2015. [BV15] Jacob Beal and Mirko Viroli. Space–time programming. Phil. Trans. R. Soc. A, 373(2046):20140220, 2015. [Duc13] Matt Duckham. Decentralized Spatial Computing - Foundations of Geosensor Networks. Springer, 2013. [DVB16] Ferruccio Damiani, Mirko Viroli, and Jacob Beal. A type-sound calculus of computational fields. Science of Computer Programming, 117:17 – 44, 2016. [DVPB15] Ferruccio Damiani, Mirko Viroli, Danilo Pianini, and Jacob Beal. Code mobility meets self-organisation: A higher-order calculus of computational fields. volume 9039 of Lecture Notes in Computer Science, pages 113–128. Springer International Publishing, 2015. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 45 / 46
Appendix References References II [FMSM+ 13] Jose Luis Fernandez-Marquez, Giovanna Di Marzo Serugendo, Sara Montagna, Mirko Viroli, and Josep Lluís Arcos. Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, 12(1):43–67, 2013. [PMV13] Danilo Pianini, Sara Montagna, and Mirko Viroli. Chemical-oriented simulation of computational systems with alchemist. Journal of Simulation, 7(3):202–215, 2013. [PVB15] Danilo Pianini, Mirko Viroli, and Jacob Beal. Protelis: practical aggregate programming. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 1846–1853. ACM, 2015. [VB] Mirko Viroli and Jacob Beal. Resiliency with Aggregate Computing : State of the Art and Roadmap. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 46 / 46

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Towards Aggregate Programming in Scala

  • 1.
    Towards Aggregate Programmingin Scala Roberto Casadei Mirko Viroli Department of Computer Science and Engineering University of Bologna 1st International Workshop on Programming Models and Languages for Distributed Computing (PMLDC), 2016, Rome R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 1 / 46
  • 2.
    Outline 1 Context andIssues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 2 / 46
  • 3.
    Context and Issues Outline 1Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 3 / 46
  • 4.
    Context and Issues Context Environment+ (Mobile, Large-scale) Networks of { people + devices } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 4 / 46
  • 5.
    Context and Issues Thesystems of ((((((( tomorrow today... Complex/Collective Adaptive Systems (CASs) • Socio-Technical Systems (STS) • Internet of Things (IoT) • Pervasive systems • Ubiquitous systems • Cyber-Physical Systems (CPS) • Smart-cities/buildings/homes, ... R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 5 / 46
  • 6.
    Context and Issues Ecosystemsof CASs services The crowd engineering example R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 6 / 46
  • 7.
    Context and Issues Gatheringlocal context R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 7 / 46
  • 8.
    Context and Issues Sensingglobal patterns of data R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 8 / 46
  • 9.
    Context and Issues Crowddetection R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 9 / 46
  • 10.
    Context and Issues Crowdanticipation R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 10 / 46
  • 11.
    Context and Issues Crowd-awaresteering R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 11 / 46
  • 12.
    Context and Issues Crowddispersal R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 12 / 46
  • 13.
    Context and Issues Crowdevacuation upon alerts R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 13 / 46
  • 14.
    Context and Issues What?How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
  • 15.
    Context and Issues What?How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
  • 16.
    Context and Issues What?How? The problem • Large-scale • Complex dynamics • Substantial unpredictability Approach • Decentralisation • Adaptation • To occasional disruptions • To on-going perturbation • To device distribution • To available infrastructure Design issues • Abstraction gap — i.e., the distance from the problem to the solution • Capturing complex behaviours in a simple way • Supporting modularity and reusability • Guiding engineering for resilience R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 14 / 46
  • 17.
    Aggregate Computing Outline 1 Contextand Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 15 / 46
  • 18.
    Aggregate Computing The origins:self-organisation patterns [FMSM+ 13] R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 16 / 46
  • 19.
    Aggregate Computing The origins:space-time programming [BV15] Space-oriented computation – Thematic areas (a) Intensive computing (b) Computation embedded in space (c) Space computation Decentralised spatial computing Computing “somewhere” [Duc13] • Location-related information • Spatial constraints to communication Space-time programming [BV15] • Computation expressed in terms of properties of the physical time and space in which it occurs • Spatial abstractions R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 17 / 46
  • 20.
    Aggregate Computing Discrete systemvs. continuous space-time R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 18 / 46
  • 21.
    Aggregate Computing Computational fields •(Abstract interpretation) Mapping space-time to computational objects • (Concrete interpretation) Mapping devices to values: φ : δ → • “Distributed” data structure working as the global abstraction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 19 / 46
  • 22.
    Aggregate Computing The ComputationalField Calculus I Field calculus A calculus of computational fields [DVB16] • Functional • Basis set of operators for field manipulation • Minimal expressive model ≈ Space-time universal Higher-Order Field Calculus (HOFC) [DVPB15] • First-class distributed functions • Code mobility R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 20 / 46
  • 23.
    Aggregate Computing The ComputationalField Calculus II Syntax e = x | v | eλ(e) | rep(e0){eλ} | nbr{e } Expression v = <standard-values> | λ Value λ = f | o | (x) ⇒ e Functional value F = def f(x){e } Function definition • v includes numbers, booleans, strings, collections, custom ADTs, etc. • f is a user-defined function • o is a built-in functional operator (e.g., pure math or a sensor) A sort of λ-calculus where values are computational fields, plus (i) a construct for field evolution (rep) (ii) a construct for interacting with the neighbourhood (nbr) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 21 / 46
  • 24.
    Aggregate Computing The ComputationalField Calculus III Global-level semantics, intuitively 0 (x)=>x+1 true t<0,1> ()0 1 + - 1 -1 ef(0,1) ef rep 0 (x)=>x+1 t v0 t v1 .. rep(0){(x)=>x+1} nbr de nbr{e} φd=[d1→v1,..,dn→vn] R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 22 / 46
  • 25.
    Aggregate Computing Example: thechannel I R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 23 / 46
  • 26.
    Aggregate Computing Example: thechannel II R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 24 / 46
  • 27.
    Aggregate Computing Aggregate functions[DVPB15] Left: branch(c){ f(x) }{ g(x) } Right: (branch(c){ f }{ g })(x) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 25 / 46
  • 28.
    Aggregate Computing Aggregate programming[BPV15] Programming (collective adaptive) systems: viewpoints (a) Local viewpoint (device-centric view) (b) Global viewpoint (aggregate view) Aggregate programming: what Goal: programming the collective behaviour of aggregates Global-to-local mapping Aggregate programming: how Prominent approach founded on field calculus and self-org patterns Composable self-organisation • Self-stabilisation • Building blocks for resilient coordination R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 26 / 46
  • 29.
    Aggregate Computing Device-centric programming Issues •Local-to-global mapping problem (generally intractable) • Explicit design of adaptation and communication • Mixing of concerns — state management, interaction, adaptation, resiliency, etc. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 27 / 46
  • 30.
    Aggregate Computing Device-centric programming Issues •Local-to-global mapping problem (generally intractable) • Explicit design of adaptation and communication • Mixing of concerns — state management, interaction, adaptation, resiliency, etc. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 27 / 46
  • 31.
    Aggregate Computing Aggregate-level programming Collectiveservices on the computational fabric available in space • Automatic global-to-local mapping • Implicit adaptation and communication • Composition of loosely coupled services R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 28 / 46
  • 32.
    Aggregate Computing Aggregate-level programming Collectiveservices on the computational fabric available in space • Automatic global-to-local mapping • Implicit adaptation and communication • Composition of loosely coupled services R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 28 / 46
  • 33.
    Aggregate Computing Aggregate (computing)systems Structure A set of networked devices • Each one is given the same field calculus program Dynamics Each device computes the given program at asyn / partially-sync rounds of execution (1) Retrieve context ⇐ Messages from neighbours ⇐ Sensor values (2) Aggregate program execution ⇒ export (3) Broadcast export to neighbourhood (4) Execute actuators R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 29 / 46
  • 34.
    Aggregate Computing Aggregate computingand abstraction (Partial) insensitiveness to system structure and situation details Aggregate behaviours are highly insensitive to: • network size • network density • network topology This makes algorithms intrinsically robust to failures and changes to working conditions. (Partial) insensitiveness to execution strategy Note: network nodes ultimately correspond to components operating in some context via sensors/actuators. Aggregate computations can be carried out in different ways: • Computing nodes and “native” local communication; • Computations performed by a central server; • Computations performed in the cloud; ... Idea: opportunistically exploit the underlying infrastructure R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 30 / 46
  • 35.
    Aggregate Computing Aggregate ProgrammingStack R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 31 / 46
  • 36.
    Aggregate Computing Approaching theengineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
  • 37.
    Aggregate Computing Approaching theengineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
  • 38.
    Aggregate Computing Approaching theengineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
  • 39.
    Aggregate Computing Approaching theengineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
  • 40.
    Aggregate Computing Approaching theengineering of CASs Approaching the aforementioned issues • Abstraction gap — i.e., the distance from the problem to the solution =⇒ Abstractions close to problem domain =⇒ Declarative approach – from how to what =⇒ Layered approach – raising abstraction incrementally • Capturing complex behaviours in a simple way =⇒ Instrumental assumptions + effective building blocks =⇒ Balance of top-down and bottom-up problem solving • Supporting modularity and reusability =⇒ Compositional approach • Guiding engineering for resilience =⇒ Adaptation by construction R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 32 / 46
  • 41.
    Aggregate Computing Aggregate ProgrammingToolchain Up to a few months ago.. Protelis [PVB15] + Alchemist simulator [PMV13] • External DSL • Focus on simulation Technological gap: there is no aggregate programming framework, aimed at the construction of real-world applications, integrated in a ≈mainstream language ⇒ SCAFI (scala with computational fields) • Scala-internal DSL for expressing aggregate computations • Distributed platform for execution of aggregate systems SCAFI: where? http://scafi.apice.unibo.it • A more serious release soon :) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 33 / 46
  • 42.
    Aggregate Programming inScala Outline 1 Context and Issues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 34 / 46
  • 43.
    Aggregate Programming inScala SCAFI: Scala with Computational Fields Goal The goal is to provide an integrated framework for building systems with aggregate programming • Scala-internal DSL for expressing aggregate computations • Linguistic support + execution support (interpreter/VM) • Correct, complete, efficient implementation of the HOFC semantics • Distributed platform for execution of aggregate systems • Support for multiple architectural styles and system configurations • Support for code mobility Issues • Implementation of a language for the field calculus within Scala • Constructs as method calls • Integration with the Scala type system • Development of a distributed middleware • Typical issues from distribution (concurrency, communication, fault-tolerance) ⇒ Akka actor framework R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 35 / 46
  • 44.
    Aggregate Programming inScala SCAFI: language trait Constructs { def rep[A](init: A) (fun: (A) => A): A def nbr[A](expr: => A): A def foldhood[A](init: => A) (acc: (A,A)=>A) (expr: => A): A def branch[A](cond: => Boolean) (th: => A) (el: => A): A def aggregate[A](f: => A): A def sense[A](name: LSNS): A def nbrvar[A](name: NSNS): A } Where are computational fields? R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 36 / 46
  • 45.
    Aggregate Programming inScala Example: the channel I def channel(src: Boolean, dest: Boolean, width: Double) = distanceTo(src) + distanceTo(dest) <= distBetween(src, dest) + width R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 37 / 46
  • 46.
    Aggregate Programming inScala Example: the channel (full code) def channel(src: Boolean, dest: Boolean, width: Double) = distanceTo(src) + distanceTo(dest) <= distBetween(src, dest) + width def nbrRange(): Double = nbrvar[Double](NBR_RANGE_NAME) def G[V:OB](src: Boolean, field: V, acc: V=>V, metric: =>Double): V = rep( (Double.MaxValue, field) ){ dv => mux(src) { (0.0, field) } { minHoodMinus { val (d, v) = nbr { (dv._1, dv._2) } (d + metric, acc(v)) } } }._2 def broadcast[V:OB](source: Boolean, field: V): V = G[V](source, field, x=>x, nbrRange()) def distanceTo(source: Boolean): Double = G[Double](source, 0, _ + nbrRange(), nbrRange()) def distBetween(source: Boolean, target: Boolean): Double = broadcast(source, distanceTo(target)) def isSource = sense[Boolean]("source") def isObstacle = sense[Boolean]("obstacle") R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 38 / 46
  • 47.
    Aggregate Programming inScala P2p, ad-hoc net // STEP 1: CHOOSE INCARNATION import it.unibo.scafi.incarnations.{ BasicActorP2P => Platform } // STEP 2: DEFINE AGGREGATE PROGRAM SCHEMA class Demo0C_AggregateProgram extends Platform.AggregateProgram { override def main(): Any = foldhood(0){_ + _}(1) } // STEP 3: DEFINE MAIN PROGRAM object Demo0C_MainProgram extends Platform.CmdLineMain 1) Demo_MainProgram -h 127.0.0.1 -p 9000 -e 1:2,4,5;2;3 --subsystems 127.0.0.1:9500:4:5 --program "demos.Demo_AggregateProgram" 2) Demo_MainProgram -h 127.0.0.1 -p 9500 -e 4;5:4 --program "demos.Demo_AggregateProgram" R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 39 / 46
  • 48.
    Aggregate Programming inScala Server-based, mobile spatial net I import it.unibo.scafi.distrib.actor.server.{SpatialPlatform => SpatialServerBasedActorPlatform} import it.unibo.scafi.incarnations.BasicAbstractActorIncarnation import it.unibo.scafi.space.{Point2D, BasicSpatialAbstraction} object Demo3_Platform extends BasicAbstractActorIncarnation with SpatialServerBasedActorPlatform with BasicSpatialAbstraction with Serializable { override val LocationSensorName: String = "LOCATION_SENSOR" override type P = Point2D override def buildNewSpace[E](elems: Iterable[(E,P)]): SPACE[E] = new Basic3DSpace(elems.toMap) { override val proximityThreshold = 1.1 } } // STEP 2: DEFINE AGGREGATE PROGRAM SCHEMA class Demo3_AggregateProgram extends Demo3_Platform.AggregateProgram { def hopGradient(source: Boolean): Double = { rep(Double.PositiveInfinity){ hops => { mux(source) { 0.0 } { 1+minHood(nbr{ hops }) } } } } def main() = hopGradient(sense("source")) } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 40 / 46
  • 49.
    Aggregate Programming inScala Server-based, mobile spatial net II // STEP 3: DEFINE MAIN PROGRAMS object Demo3_ServerMain extends Demo3_Platform.ServerCmdLineMain { override def refineSettings(s: Demo3_Platform.Settings) = { s.copy(profile = s.profile.copy( serverGuiActorProps = tm => Some(ServerGUIActor.props(Demo3_Platform, tm )) )) } } object Demo4_MainProgram extends Demo3_Platform.CmdLineMain { override def refineSettings(s: Demo3_Platform.Settings) = { s.copy(profile = s.profile.copy( devGuiActorProps = ref => Some(DevGUIActor.props(Demo3_Platform, ref)) )) } override def onDeviceStarted(dm: Demo3_Platform.DeviceManager, sys: Demo3_Platform.SystemFacade) = { dm.addSensorValue(Demo3_Platform.LocationSensorName, Point2D(dm.selfId%5,( dm.selfId/5.0).floor)) dm.addSensorValue("source", dm.selfId==4) dm.start } } R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 41 / 46
  • 50.
    Aggregate Programming inScala Server-based, mobile spatial net III R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 42 / 46
  • 51.
    Summary Outline 1 Context andIssues 2 Aggregate Computing 3 Aggregate Programming in Scala 4 Summary R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 43 / 46
  • 52.
    Summary Summary: key ideasand future directions Aggregate programming • An ((((((( programmingengineering approach to CASs • Formally grounded in the Field Calculus • Allows to compose “emergent” phenomena • Provides layers of building blocks (proven to self-stabilise!) A Scala framework for aggregate programming is out! • Supports the construction of aggregate systems • Integrated in Scala No need to learn ad-hoc external DSLs Easier to get started and play • Better release (e.g., to Maven Central) soon, I promise :) Many open problems [VB] (want to join?) • Space-time universality • Fields vs. objects/agents/processes/worflows • Better resilience properties than self-stabilisation • Adapting the execution strategy to available infrastructure (e.g., cloud, fog, ...) R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 44 / 46
  • 53.
    Appendix References References I [BPV15]Jacob Beal, Danilo Pianini, and Mirko Viroli. Aggregate Programming for the Internet of Things. IEEE Computer, 2015. [BV15] Jacob Beal and Mirko Viroli. Space–time programming. Phil. Trans. R. Soc. A, 373(2046):20140220, 2015. [Duc13] Matt Duckham. Decentralized Spatial Computing - Foundations of Geosensor Networks. Springer, 2013. [DVB16] Ferruccio Damiani, Mirko Viroli, and Jacob Beal. A type-sound calculus of computational fields. Science of Computer Programming, 117:17 – 44, 2016. [DVPB15] Ferruccio Damiani, Mirko Viroli, Danilo Pianini, and Jacob Beal. Code mobility meets self-organisation: A higher-order calculus of computational fields. volume 9039 of Lecture Notes in Computer Science, pages 113–128. Springer International Publishing, 2015. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 45 / 46
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    Appendix References References II [FMSM+ 13]Jose Luis Fernandez-Marquez, Giovanna Di Marzo Serugendo, Sara Montagna, Mirko Viroli, and Josep Lluís Arcos. Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, 12(1):43–67, 2013. [PMV13] Danilo Pianini, Sara Montagna, and Mirko Viroli. Chemical-oriented simulation of computational systems with alchemist. Journal of Simulation, 7(3):202–215, 2013. [PVB15] Danilo Pianini, Mirko Viroli, and Jacob Beal. Protelis: practical aggregate programming. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 1846–1853. ACM, 2015. [VB] Mirko Viroli and Jacob Beal. Resiliency with Aggregate Computing : State of the Art and Roadmap. R. Casadei (Università di Bologna) Towards Aggregate Programming in Scala PMLDC 2016 46 / 46