Enablingdistributed,compute-intensiveFaaS ontheedgewithCOMPSs

FrancescLordan

14/04/2019 Edge ComputingWorkshop@ASPLOS’19

Outline

• Motivation• Programming Model:COMPSs• DecentralizingtheCOMPSsruntime• UseCase:CagliariAirport• Conclusion

Motivation

IoT/EdgePlatforms

4

3types ofelements composing the system:

Events/DataGenerators (sensors)elementsmonitorizingcertainconditionandinforming about it

Continuosly generating informationà Streams ofdataEventually notifying achangeà Events

Event-reactors/DataConsumers(displays)elements on which the platform realizes actions (changing its state)

Computingelementselementswithintheinfrastructurewithabilitytoprocessandtransforminformation

5

Cloud-EdgeContinuum

Computation on IoT/Edge platforms3purposes for computation:

• Sense-process-actuate:Asensordetects something andtriggers acomputation togive aproper responsetosuchevent.

• Stream processing:Asensorcontinuously provides datathat needs tobeprocessed.

• Batch Jobs:Analyse big amounts ofdatacollected by the sensors

Programming Model:COMPSs

BSCvisiononprogrammingmodels

8

GeneralpurposeTaskbased

Singleaddressspace

Intelligentruntime,parallelization,distribution,

interoperability

Programlogicindependentof

computingplatformApplications

Powertotheruntime

ProgrammingModel:High-level,clean,abstract

MiddlewareAPI

SimpleParallelProgrammingModel• WhatdoIneedtocompute?• WhatdatadoIneedtouse?• ProvideHints

LetthedifficultpartstotheruntimeActonbehalfoftheuser

EnableMonitoringandAnalysisGeneratedatatoevaluatehowapplicationperforms

COMPSuperscalar(COMPSs)• Generalpurposeprogramminglanguage+annotations/hints• SequentialprogrammingwithnoAPIcalls• Agnosticofthecomputinginfrastructure• Task-based:taskistheunitofwork

• Buildsataskgraphatruntimethatexpresspotentialconcurrency• Implicitworkflow

• Exploitationofparallelism• …andofdistantparallelism

public class Matmul {

public static void main (String[] args) {

int[][] A;

int[][] B;

...

int[][] C = multiply(A, B);

...

}

}

public class Simple {

public static int[][] multiply (

int[][] A, int[][] B) {

// Matrix multiplication code

// C = AB

...

return C;

}

}

CEI.javapublic interface CEI {

@Method(declaringClass = ”Simple”)

public int[][] multiply (

@Parameter(direction = IN) int[][] A,

@Parameter(direction = IN) int[][] B

);

}

Matmul.java Simple.java

COMPSs Example - Java

COMPSs Runtime

COMPSs Master

COMPSs runtime

Access Processor

Scheduler

Resources

Loader Bindings-commonsC Python

Applicationmain(args)

Task Task

Task

Decentralizing theCOMPSs runtime

Environment differencesCOMPSs’usualflow:• 1application• Infrastructureisstable• 1node(MASTER):

• Spawnstasks• Orchestratestheexecutionovertheavailableresources(WORKERs)

COMPSsforEdgeComputing• Napplicationsatthesametime• Infrastructureisdynamic• Anyagentmaygeneratenewtasks• Executionorchestrationisasharedresponsibility

COMPSs Agent

COMPSs agent

Access Processor

Scheduler

Resources

Agent InterfaceStart AppRequest• Class• Method• Params• CEI

Task

Application

LoaderBindings-commons

C Python

Agents interaction

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

Computingagents hierarchy

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

Remoteagent

Remoteagent

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

ComputingAgent

Localdevices

Scheduler

AccessProcessor

Remoteagent

Remoteagent

UseCase:Cagliari(IT)airport

SmartFog-Hub ServiceIn2017morethan4billionpassengersconcentratedinairports

• Features• trackthepresenceofpeopleandobjectsinthefield• proximitymarketingservices• suggestionsonbestuseofairportservices• Recommendersystembasedonconsumers’behavior

• Objectives• Reducelatencyandresponsetimes• Capabilitytodistributecomputingincaseofoverloading• Pleasantexperiencefortravelerswhileintheairportfield

Customer Experience

.

Flight AZ626 now boarding, gate B32

Sardinian handcraft

2

3

4

1

App install,topics setting

AndroidApp

Dashboard

UseCasedeployment

(A)

(B)

(C)

(D)

Benchmarkingscenarios

TestResults

Conclusions

Summary• IoT/Edge infrastructures arecomposed ofautonomous nodes with networkandcomputing capabilities

• COMPSs• Developers code being unaware ofthe parallelism andinfrastructure-related concerns• Detects tasks andthe parallelism inherent inthe application• Orchestrates the execution ofthese tasks on the available infrastructure• Supports computation on the three scenarios (batch,stream andsense-process-actuate)

• COMPSs agents• Allows devices toremain autonomous andcomputeinan isolated manner• By interacting with other agents,andagent hasaccess tothe available computing power

• The airport usecaseshowsthe viability andthe benefits ofthe presentedsolution

THANKYOU!

www.bsc.es

francesc.lordan@bsc.es

COMPSs

COMPSs Runtime• COMPSsapplicationsexecutedindistributedmodefollowingthemaster-workerparadigm

• Sequentialexecutionstartsinmasternode• Tasksareoffloadedtoworkernodes• Alldataschedulingdecisionsanddatatransfersareperformedbytheruntime

Task Dependecy Graph Computing infrastructure

COMPSs Runtime ResourceMgmt.

TaskExecutionSchedulingTask

AnalysisDataMgmt.

Monitoring

Files, objects

Tasks

Annotatedapplication

code

Resource 2

1. Identify tasks

main program {

}

2. Select tasks

taskA(...);

taskB(...);

(C++/Java)taskselectioninterface{

}(Python)@taskdecorator

taskA

taskBTask

Unitofparallelism

. . .

taskA

Asynchrony

taskB

Resource 1 Resource N

ProgrammingSteps

Programming Model Examplefor (i = 0; i < 3; i++) {

increment(counter1);increment(counter2);increment(counter3);

}printCounters(counter1, counter2, counter3);

counter1 counter2 counter3

1st iteration

2nd iteration

3rd iteration

public interface SimpleItf {

@Method(declaringClass = “SimpleImpl")void increment(

@Parameter(type = FILE, direction = INOUT)String counterFile

);

}Parametermetadata

Implementation

AdvancedFeatures

• Constraintstosupportheterogeneoustasks• @Constraints(…)

• Versioning• @Implements(…)

• Combinationofbinaryexecution• @Binary(…)

• IntegrationwithProgrammingModels• @MPI(..)@Decaf(…)• COMPSs+OmpSs

• Nested• @COMPSs(…)

ExecutionEnvironments• InteractiveNodes

• Clouds

• Clusters

• Containers

SchedulingPolicies

RuntimeExtensions

Runtime System

Resource Management

Job Submission & Data Transfer

Scheduling

Task Analysis Data Access & Locality

Monitoring& Tracing

ResourceProviders

Comm. Protocols

Persistent Objects

project.xml resources.xml

Executioncommands:• runcompss (interactive&cloud)• enqueue_compss (clusters)• Runcompss_docker (socker clusters)

mF2C

Background – OpenFog

Thearchitecture isanextensionofthetraditionalcloudcomputingmodel

• Processesaremovedfromthecloudtotheedgeofthenetwork,inFognodes

• Deploymentscanresideonmultiplelayersofanetworktopology,

• Deploymentsretainallthebenefitsofcloudcomputing,suchascontainerization,virtualization,orchestration, resource-efficientmanagement

FogNodespeculiarities

• Autonomousprocessing

• LocalstorageandIPcommunications

• Hostedinopen(evenhostile)fields

• Capableofactinginmobility

mF2Carchitecture

Usecasearchitecture• Cloud – basedonaOpenStackinstance,wiredconnectedwiththefoglayers,providingscalablecomputingpowerforMachineLearningalgorithms

• EdgeFog– withafogaggregatorbasedonNuvlabox with8GB,providingreal-timecomputingandstorageresourcesand6rPIwith1GBhubprovidingsessionmanagementandfastresponsetotheedgedevices

• EdgeIoT – Androidsmartphonesconnectedtotheedgenodesthroughwifi,andusinganAndroidapptointeractwiththesystem

• Proximity processing• Client – call arequest for alist ofnearby POIs using geographic position• Server– calculates the POIs inproximity andreturns aJSONarray

• ClientSmartphone XIAOMI with Android 5.0.2, running app calling a Rest HTTP API

• Server• AVMruns adockerized image with proximity calculation1. rPI3with 1GBRAM2. VMrunningon apublic cloud (4-coreprocessor and4GBRAM)

Testbed

Interaction withIoT devices

Interaction with sensors

• Everysensorisattachedtoonedevicewithcomputingcapabilities

• Events:the device trigger afunction execution on any COMPSs agent

• Streams:the device publishes the stream ofdatadirectly

• Through aController class• Handles all the communication with the device• Offers asimpleAPItointeract with the device

• Each instance ofthis controller class interacts with one device• Device canbecontrolled by any agent if the object is transferred

public void task(Streetlamp sl) {

…sl.on();…

}

Interaction with displays/devices

class Streetlamp {

public void on() {…

}

public void off() {…

}}

• Using the storage framework• Controller class is aPersistent Object• One Controller instance is made persistent for each device using auniversalID• Any agent able caninteract with any registered resource by using an ID

public void task() {

…Streetlamp sl;sl = Storage.getByAlias(“light21758”);sl.on();…

}

Interaction with displays/devices

class Streetlampextends StorageObject {

public void on() {…

}

public void off() {…

}}

Streetlamp sl = new Streetlamp();sl.makePersistent(“light21758”);

StorageFramework

COMPSs task

Device Manager