MOCCA – A Distributed CCA Framework based on H2O

Institute of Computer Science AGH

MOCCA – A Distributed CCA Framework based on

H2O

Maciej Malawski, Dawid Kurzyniec, Vaidy Sunderam

Distributed Computing Laboratory in the Dept. of Math and Computer Science, Emory University, AtlantaInstitute of Computer Science AGH, Krakow, Poland

CCA Meeting, Atlanta, Jan 2005 Institute of Computer Science AGH2

Outline• CCA and H2O: a good match

• H2O as underlying platform

• Goals of MOCCA (Metacomputing Oriented CCA framework)

• MOCCA technical overview

• Initial performance results

• Towards Babel compatibility

• Future directions and research perspectives


Why CCA and H2OCCA• Component standard for HPC• Uses and provides ports described in

SIDL• Support for scientific data types• Existing tightly coupled (CCAFFEINE)

and loosely coupled, distributed (XCAT) frameworks

H2O• Distributed resource sharing platform• Providers setup H2O kernel

(container)• Allowed parties can deploy pluglets

(components)• Separation of roles: decoupling

– Providers from deployers– Providers from each other

• RMIX: efficient multiprotocol RMI extension

ContainerProvider host

Deploy Lookup& use

Provider Client

<<create>>

B

A

Provider

<<create>>

A

B

Container

Lookup& use

Client

DeployProvider,

Client,or Reseller

Provider host

Traditional model

Proposed model

ContainerProvider host

Deploy Lookup& use

Provider Client

<<create>>

BB

AA

Provider

<<create>>

AA

BB

Container

Lookup& use

Client

DeployProvider,

Client,or Reseller

Provider host

Traditional model

Proposed model


H2O Resource sharing platform

• Providers own resources• They independently share

them over the network– access control policies

• Clients discover, locate, and utilize resources

• Resources configurable via plugins

• Aggregation and reselling: cascading pairwise relationships

Network

Providers

Clients

Network

Providers

Clients


H2O Component Model• Nomenclature

– container = kernel

– component = pluglet

• Pluglet = remotely accessible object– implements Pluglet interface, used by

kernel to signal/trigger pluglet state changes

• Remote access: based on the RMI model– Pluglets export functional remote interfaces

Pluglet

Pluglet

Functionalinterfaces

Kernel

Clients

(e.g. Hello)

tutorial/step1/srv/Hello.java

public interface Hello extends Remote { String hello() throws RemoteException;}

Interface Pluglet { void init(RuntimeContext cxt); void start(); void stop(); void destroy();}


Example scenarios of H2O

1. Provider = deployer

e.g. resource = legacy application

2. Reseller:= developer = deployer

e.g. computational service offered within a grid system

3. Client = deployer

e.g. client runs custom distributed application on shared resources

Deploy

B

A

LegacyApp

DeployProvider

AClient

Repository

A BReseller

C

Deploy

Anativecode

ProviderClient

Repository

ABDeveloper

C

ProviderClient

B

A

...

Registration and Discovery e-mail,phone, ...JNDIUDDI LDAP DNS GIS ...

B

Publish Find

Provider


RMIX Communication Substrate

• Extensible framework

• Remote Method Invocations paradigm

• Pluggable protocol providers

• Multiple protocols supported– JRMPX, ONC-RPC, SOAP

• Request-Response and Asynchronous calls

• Combines simplicity, flexibility, and performance

ONC-RPCWeb Services

SOAP clients

Myrinet

RMIX

RMIXXSOAP

RMIXRPCX

RMIX MYRI

RMIXJRMPX

Java

Service


RMIX: multiple protocols• Protocol switching

• Protocol negotiation

• Various protocol stacks for different situations– SOAP: interoperability

– SSL: security

– ARPC, custom (Myrinet, Quadrics): efficiency

Harness Kernel

Internet

security

firewall

efficiency

efficiency

H2O Kernel

H2O Kernel

H2O Kernel H2O Kernel

H2O Kernel


H2O: Current research areas

• (More) Interoperability– Support for Globus proxy credentials

– Naming services: JNDI bridge to multiple implementations

• Peer-to-peer grids– JXTA transport provider for RMIX; enables RMI over JXTA

sockets

– JXTA-JNDI naming provider: peer-to-peer resource sharing and discovery

• Fault-tolerant MPI across shared resources– FT-MPI & H2O: staging, sharing, heterogeneity, scalability


H2O: Feature Summary• Resource sharing

– Deploying components on shared resources• Component isolation and hot-swapping

– Pluglets deployed in separate class loaders• Performance

– Pluglets run in a single process; efficient local bindings– Distributed communication: efficient binary protocols– Support for asynchronous calls

• Security– SSL transport, JAAS authentication, Java sandbox model,

security policies• Interoperability

– Multiple RMI protocols: SOAP, JRMP, RPC, …– API support for native code: resource staging, linking dynamic

libraries


Requirements for a Metacomputing-oriented CCA

Framework• Facilitated deployment - provide easy mechanisms for

creation of components on distributed shared resources;

• Efficient communication - both for distributed and local components;

• Flexible - allow flexible configuration of components and various application scenarios;

• Support native components, i.e. components written in non-Java programming languages and compiled for specific architecture.

• Interoperable with Grid standards (Web services)


Existing CCA Frameworks• CCAFFEINE

– Tightly coupled– Support for Babel– MPI support

• XCAT– Loosely coupled– Globus-compatible– Java-based

• DCA– MPI based– MxN problems

• SCIRun2– Metacomponent model

• LegionCCA– Based on Legion Metacomputing system


MOCCA implementation in H2O

ComponentPlugletComponent

Pluglet

CCAComponent

ComponentPluglet

CCAComponent

BuilderPluglet

H2O Kernel

BuilderService

Invoke

Manage

Builder

CCACCA

Pluglet Pluglet

Builder Builder

CCACCA

Pluglet Pluglet

BuilderBuilder

CCACCA

Pluglet Pluglet

Builder

MoccaMainBuilder

MoccaMainBuilder

• Each component running in separate pluglet • Thanks to H2O kernel security mechanisms, multiple

components may run without interfering• Two-level builder hierarchy• ComponentID: pluglet URI• MOCCA_Light: pure Java implementation (no SIDL)


Remote Port Call

Component Pluglet

CCAComponent

MOCCAServices

1. getPort 2. create

3. call

Component Pluglet

CCAComponent

DynamicPortProxy

4. RMIX call

5. call

User Side Provider Side


How to use MOCCA(step by step)

• Implement component code extending CCA interfaces (cca.Port, cca.Component)

• Compile component classes into JAR file• Publish application JARs on HTTP server• Use the Java client API or write a Jython script to assemble

application from components– Specify components and their connections– Specify locations of H2O kernels where to instantiate

components

• Running the script automatically deploys necessary pluglets into H2O kernels and spawns application


Example scriptbuilder = MoccaMainBuilder()uriKernel1 = URI.create("http://emily.mathcs.emory.edu:7800/")uriKernel2 = URI.create("http://zeus10.cyf-kr.edu.pl:7800/")userBuilderID = builder.addNewBuilder(uriKernel1, "MyBuilderPlugletA")providerBuilderID = builder.addNewBuilder(uriKernel2, "MyBuilderPlugletB")properties = MoccaTypeMap()properties.putString("mocca.plugletclasspath",

"http://emily.mathcs.emory.edu/mocca/mocca-samples.jar")properties.putString("mocca.builderID", userBuilderID.getSerialization())userID = builder.createInstance("My StarterComponent", "mocca.samples.pingpong.impl.MoccaStarterComponent”, properties)properties.putString("mocca.plugletclasspath", "http://emily.mathcs.emory.edu/mocca/mocca-samples.jar")properties.putString("mocca.builderID", providerBuilderID.getSerialization())providerID = builder.createInstance("MyPingComponent", "mocca.samples.pingpong.impl.PingPongComponent", properties)connectionID = builder.connect(userID, "PingPongUsesPort", providerID, "PingPongProvidesPort")MoccaBuilderClient.invokeGo(userID)


Automatic Flow Composer Example

Lookup

FlowOptimizer

FlowComposer

LinkEvaluator

SiteEvaluator

ComponentRegistry

Evaluate

Compose

Evaluate

• Compose application graph from initial data (e.g. initial ports) or incomplete graph

• First implemented for XCAT framework

• Easy migration to MOCCA

• Modification of code required (xcat.Port)

• Similar performance for XCAT and MOCCA (exchange of text documents)


Communication Intensive Application Benchmark

• Simplified scenario:

– 2 components

– Provides port: receive and send-back array of double (ping-pong)

• Tested on local Gigabit Ethernet and on transatlantic Internet between Atlanta and Krakow

• 2.4 Ghz Linux machines

• Comparison with XCAT


Small Data Packets

Factors:

• SOAP header overhead in XCAT

• Connection pools in RMIX


Large Data Packets

• Encoding (binary vs. base64)

• CPU saturation on Gigabit LAN (serialization)

• Variance caused by Java garbage collection


Support for Babel Components

• Currently: MOCCA_Light – pure Java framework

• Approach:

– Use Java bindings to Babelized components

– Automatically generate wrapping code

• Issues:

– Babel remote bindings

– Remote references

– Package hierarchy: gov.cca.Port extends gov.llnl.sidl.BaseInterface, sidl.BaseInterface

Component Pluglet

CCAComponent(Native)

MOCCA BabelServices (Java)

1. getPort 2. create

3. IOR call

Component Pluglet

CCAComponent(Native)

Babel port proxy(Java)

4. RMIX call

5. call

User Side Provider Side

Babel port stub (Java)

6. IOR call


Future directions of MOCCA• Support for multilingual components

– Goal: to enable loading of Babel CCAFFEINE components into MOCCA

– Automatic generation of wrappers from SIDL

• Support for dynamic component reconfiguration and adaptiveness– CCA standard allows for dynamic creation of ports and

connecting/reconnecting at runtime– Framework needs to support such behavior– Especially interesting for interactive (portal/PSE) usage

• Investigation of using hierarchical components– By using CCA BuilderService components may act as sub-

frameworks creating hierarchies of subcomponents inside them


Beyond CCA ?

• Supporting multiple component standards– Goal: to enable loading of components written for different

standards (e.g. Corba CCM, other)– Examples of similar solutions: CCAFFEINE supports „classic”

and „Babel” components; SCIRun2 implementing meta-component model

• Using MOCCA as promising platform for feasibility studies in various aspects of Grid components– For experiments with advanced features

• Scheduling and load-balancing• Fault-tolerance• Semantic description and composition

– As a platform for higher-level grid services and tools


References• Maciej Malawski, Dawid Kurzyniec, and Vaidy Sunderam. MOCCA

– towards a distributed CCA framework for metacomputing, Accepted for: 10th International Workshop on High-Level Parallel Programming Models and Supportive Environments (HIPS2005), http://mathcs.emory.edu/dcl/h2o/papers/h2o_hips05.pdf

• H2O Project homepage: http://www.mathcs.emory.edu/dcl/h2o/

Documents

MOCCA – A Distributed CCA Framework based on H2O