programming a service cloud rosa m. badia, jorge ejarque, daniele lezzi, raul sirvent, enric tejedor...

Programming a service Cloud

Rosa M. Badia, Jorge Ejarque, Daniele Lezzi, Raul Sirvent, Enric Tejedor

Grid Computing and Clusters Group Barcelona Supercomputing Center

Cloud Futures Workshop, Redmond, WA, 8-9 April 2010

Cloud Futures Workshop, Redmond , WA, 8-9 April 2010

Outline

• StarSs programming model

• COMPSs framework

• EMOTIVE Cloud

• COMPSs towards SOA and Clouds

• ServiceSs

• Conclusions

2


...for (i=0; i<N; i++){ T1 (data1, data2); T2 (data4, data5); T3 (data2, data5, data6); T4 (data7, data8); T5 (data6, data8, data9);}...

Sequential Application

T10 T20

T30

T40

T50

T11 T21

T31

T41

T51

T12

…

Resource 1

Resource 2

Resource 3

Resource N

.

.

.

Task graph creation

based on data

precedence

Task selection +

parameters direction

(input, output, inout)

Scheduling,

data transfer,

task execution

Synchronization,

results transfer

Parallel Resources(cluster, grid)

Star Superscalar Programming Model

3


StarSs programming model

• GRIDSs, COMPSs

• Tailored for Grids or clusters

• Data dependence analysis based on files

• C/C++, Java

• SMPSs

• Tailored for SMPs or homogeneous multicores

• Altix, JS21 nodes, Power5, Intel-Core2

• C or Fortran

• CellSs / GPUSs

• Tailored for Cell/B.E. processor / for GPUs

• C or Fortran

• NestedSs

• Hybrid approach that combines SMPSs and CellSs


COMPSs

• Componentised runtime

• Each component in charge of a functionality

5

Base technologies:

• Java as programming language

• ProActive:

• Reference implementation of the GCM model

• Used to build the components

• JavaGAT

• API that provides uniform access to different kinds of Grid middleware

• Used for job submission and file transfer


initialize(f1);

for (int i = 0; i < 2; i++) {

genRandom(f2);

add(f1, f2);

}

print(f2);

Java application

COMPSs Programming model – Application + interface

public interface SumItf {

@ClassName(“example.Sum")

@MethodConstraints(OSType = "Linux")

void genRandom(

@ParamMetadata(type = Type.FILE, direction = Direction.OUT)

String f

);

@ClassName(“example.Sum")

...

}

Task constraints

Parametermetadata

Implementation

Java interface

6


Custom Java Class Loader

Java app code

COMPSs runtime

Annotated

interface

Javassist

insertscalls to

Custom Loader

uses

input

C/C++ app code

COMPSs runtime

Interface

insertscalls to

Stubs Generator

input

JNI


Runtime behavior

Custom Loader

Javassist

initialize(f1);

for (int i = 0; i < 2; i++) {

genRandom(f2);

add(f1, f2);

}

print(f2);

Annotated

interface

Java code

T1 T3

T2 T4

Grids Clusters

Files


HMMPfam: sample COMPSs application

•HMMER: set of tools for protein sequence analysis

• Based on statistical Hidden Markov Models (HMMs)

• hmmpfam: tool to compare a sequence against a database of HMMs

(protein families)

• Computationally intensive

• Embarassingly parallel

•HMMPfam: Java application that uses hmmpfam

• Query sequences / database segmentation

• Programmed in a totally sequential fashion

• Selection of remote methods using a separate Java interface

• hmmpfam computation, merging of results

9


HMMPfam – Annotated interface

public interface HMMPfamItf {@ClassName("worker.hmmer.HMMPfamImpl")void hmmpfam(

@ParamMetadata(type = Type.STRING, direction = Direction.IN)String hmmpfamBin,@ParamMetadata(type = Type.STRING, direction = Direction.IN)String commandLineArgs,@ParamMetadata(type = Type.FILE, direction = Direction.IN)String seqFile,@ParamMetadata(type = Type.FILE, direction = Direction.IN)String dbFile,@ParamMetadata(type = Type.FILE, direction = Direction.OUT)String resultFile

);@ClassName("worker.hmmer.HMMPfamImpl")void mergeSameSeq(

@ParamMetadata(type = Type.FILE, direction = Direction.INOUT)String resultFile1,@ParamMetadata(type = Type.FILE, direction = Direction.IN)String resultFile2,@ParamMetadata(type = Type.INT, direction = Direction.IN)int aLimit

);@ClassName("worker.hmmer.HMMPfamImpl")void mergeSameDB(

@ParamMetadata(type = Type.FILE, direction = Direction.INOUT)String resultFile1,@ParamMetadata(type = Type.FILE, direction = Direction.IN)String resultFile2

);}

10


HMMPfam – Main program

public static void main(String args[]) throws Exception { split(fSeq, fDB, seqFrags, dbFrags); // Segment the query sequences file, the database file or both (done sequentially)

for (String dbFrag : dbFrags) { //Launch hmmpfam for each pair of seq - db fragments for (String seqFrag : seqFrags) { HMMPfamImpl.hmmpfam(hmmpfamBin, finalArgs, seqFrag, dbFrag, output); seqNum++; } dbNum++; }

while (outputs.size() > 1) { ListIterator<String> li = outputs.listIterator(); while (li.hasNext()) { String firstOutput = li.next(); String secondOutput = li.hasNext() ? li.next() : null; if (secondOutput == null) break; if (sameSeqFragment(firstOutput, secondOutput)) // Merge output fragments of different db fragments (must take care when merging) HMMPfamImpl.mergeSameSeq(firstOutput, secondOutput, clArgs.getALimit());

else if (sameDBFragment(firstOutput, secondOutput)) // Merge output fragments of different sequence fragments (basically appending one to another) HMMPfamImpl.mergeSameDB(firstOutput, secondOutput);

else // Avoid merging two output fragments of different sequence and db fragmentsli.previous();

} }

}

11


HMMPfam – Tasks

public static void hmmpfam(String hmmpfamBin, String commandLineArgs, String seqFile, String dbFile, String resultFile) throws

Exception { String cmd = hmmpfamBin + " " + commandLineArgs + " “ + dbFile + " " + seqFile; // Execute command line Process hmmpfamProc = Runtime.getRuntime().exec(cmd);

// Check the proper finalization of the process int exitValue = hmmpfamProc.waitFor(); if (exitValue != 0) {

throw new Exception(“Exit value for hmmpfam is “ + exitValue); } }

public static void mergeSameSeq(String resultFile1, String resultFile2, int aLimit) throws Exception {...}

12


HMMPfam

13


HMMPfam performance

14


HMMPfam – EBI runs

•European Bioinformatics Institute used HMMPfam in

productions runs

•ELIXIR project, tests on the MareNostrum supercomputer

• 7.500.000 protein sequences, divided in 150 files with 50.000 sequences

each

• TIGRFAM database, containing 3418 models (HMMs)

• 150 jobs submitted (i.e. COMPSs-HMMPfam executions), one for each

input sequences file

• 12 hours of execution time per job, approximately

• 64 worker processors per job + 4 processors for the master

15


EMOTIVE CLOUD

• EMOTIVE CLOUD – Barcelona

Elastic Management of Tasks for Virtualized Environments in the

CLOUD

– is an open-source software infrastructure for implementing 'cloud computing'

on clusters. (recently released v 1.0)

– is an open source collaborative software development project dedicated to

providing an extensible, standards-based platform to address a broad range of

needs in the resource management development space


EMOTIVE Cloud

• Scheduler

• Selects where to execute a task

• Virtualized Resource Management and Monitoring

• VM lifecycle management

• Creation of VMs

• VM monitoring

• VM destruction

• Data management

• Migration

• Checkpointing

• Data infrastructure

• Distributed file system

•EMOTIVE architecture: three different layers


SERA scheduler: SRLM and ERA

•SRLM• Receives customer requests: job execution• Negotiates the allocation with the resource agents

• Selects the resources which match with the job requests • Receives from ERA scheduling proposals to selected resources• Decides which is the best proposal

• Manages Execution Lifecycle• Monitorizes the execution, recovers in case of failure, tries to improve the

execution

•ERA• Perform scheduling proposals

• Find schedules for the job requests using the semantic information of the resource descriptions and the provider rules

• Interacts with the different resources

• Resources reservation• Creates VM for the execution• Submits the jobs on the selected resources

ERA

SRLM

Semantic SchedulerResource Manager

Resources


Integration in a Service-Oriented and Cloud infrastructure

•Goal: moving the COMPSs runtime from the client side to a server

SOA platform

•Characteristics of this environment:

• Execution of application tasks offered as services

• N applications can be served simultaneously

• Several COMPSs can be deployed, to serve the tasks from one or more

applications

• Resource provisioning brought by a Cloud

19


COMPSs and EMOTIVE Cloud – Step 1

VM1VM1 VMnVMnVM2VM2

1. Existing pool of EMOTIVE VMs

2. COMPSs executes tasks on these VMS


COMPSs and EMOTIVE Cloud – Step 2

VM1VM1 VMnVMnVM2VM2

1. The Task Scheduler requests SERA a pool of VMs

2. COMPSs executes tasks on these VMS

3. COMPSs requests the creation of more or “bigger” VMs (memory, CPU, etc)

VMn+1VMn+1


ServiceSs envisioned architecture

22

JavaApp

WS

Co

nta

iner

Ru

nti

me

Man

ag

er

COMPSsruntime

instance 1

CloudScheduler

COMPSsruntime instance N

WorkerVM 1

WorkerVM 1

WorkerVM 1

WorkerVM M

COMPSsApplication

Side

JavaApp

JavaApp

Cloud

WS

Co

nta

iner

UserSide

App

App

User

WS

Co

nta

iner

WS

Co

nta

iner

WorkerVM 1


Conclusions

•COMPSs is platform unaware programming model that simplifies

the development of applications in distributed environments

• Transparent data managemet, task execution

• Parallelization at task level

• Independent of platform: clusters, grids, clouds

•COMPSs evolution on top of SERA and EMOTIVE cloud will enable

the execution on federated clouds

• SERA is already able to submit jobs to EC2

•Further evolution of COMPSs towards ServiceSs to enable the

composition of services

• Graphical IDE to help deployment of services and development of applications

• Evolved runtime to support new features


• www.bsc.es/grid

• www.emotivecloud.net

programming a service cloud rosa m. badia, jorge ejarque, daniele lezzi, raul sirvent, enric tejedor...

Documents

db fragments li

string secondoutput

listiterator li

string seqfrag

string dbfrag

service cloud rosa

output seqnum

string firstoutput