The Grid-frame Era

Richard Hall | Chief Technology Officer, UK

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ConceptsBusiness opportunityCase studyAvanade Grid ArchitectureExcel Services and GridWho is Avanade?

Entering the Grid-Frame era means the most demanding computational tasks can be delivered at minimal cost and zero incremental hardware

Grid computing concepts

Grid computing is closely associated with ‘on demand’ and ‘utility’ concepts, and can be categorised in two main forms:

High Performance Computing (HPC) where dedicated farms of servers crunch intensive calculations once the preserve of supercomputers, in fields such as aerospace, finance, geophysics, mechanical engineering, pharmaceutical research and weather simulation

Cycle scavenging where highly distributed workstations and servers lend their spare capacity to compute elements of a large workload, effectively forming a virtual supercomputer (a ‘grid-frame’). This pool of distributed resource, whose membership waxes and wanes subject to availability, can then be tasked by a range of different applications

Enterprise wide spare capacity

Enterprise infrastructure

HPC

Internal UtilityGrid power

JobJob

Job

Job

Utility computing Definition

Utility / adaptive / dynamic and ‘on demand’ are all much used terms

For simplicity we will define utility computing to mean:

An infrastructure and application capacity used flexibly by a range of end users (or applications)

Often mediated by web services

Typically built with a Service Oriented Architecture (SOA) approach

Utility computing Internal and external

An internal utility acts as a shared service within an organisation, delivering functionality on tap to different departments and users as required – and possible metering/charging on a per use basis – alleviating silo spending on IT

An external utility acts as a service supplier for the IT fabric and potentially the application functionality/licensing on an ‘on demand’ basis, maintaining the bulk of the infrastructure independently from the client, and potentially shared between multiple organisations. Normally for general purpose IT at a layer below business process outsourcing

Business opportunity

Escaping the high performance niche

Grid computing Business opportunity

Mentioning grid computing often invokes concept of HPC dedicated farms with elaborately coded applications

Either an organisation has developed this – at great expense – or does not see this as affordable route

In both cases grid is seen as only ever having a limited footprint in the organisation

Cycle scavenging turns this thinking on its head, asking instead:

“What applications would you run if no additional hardware was required, and both development and operations were simple?”

The Grid-Frame Era

Computationally intensive tasks can now be addressed for zero incremental hardware costs and minimal development

A new range of business problems are now within reach which previously would not have justified dedicated hardware farms

Typical workloads destined for a mainframe can now be serviced by the grid-frame

As Microsoft tools deliver ubiquitous business intelligence to the desktop users, grid computing will add far deeper insight

Grid solutions will escape the HPC niche and serve the whole organisation

Grid computing Banking scenario example

Risk Management System

Core banking

Front office trading

application Dedicatedserver cluster

Ad hoc Windows grid farm (AGA.NET)

Customer analytics

Scale out

Distributed resource

Workloadgovernance

Monte Paschi de Siena

Evolution of grid within an investment bank

Case study Monte Paschi de Siena

Monte Paschi de Siena (MPS) is one of the Italy’s oldest financial institutions

Consorzio Operativo Gruppo MPS is a shared service provider within the MPS group

Set up as a consortium it manages information technology systems and administrative services for companies within the group

Forward looking IT strategy, striving to improve performance for MPS

Adopted SOA in 1999, and Grid development in 2004

Case study Environment

Every day, the systems of Consorzio Operativo Gruppo MPS execute millions of online transactions and many batch procedures on mainframes of member banks and of the other companies within the consortium

The bank’s mainframe runs a range of batch processes. However, as it has become an increasingly multi-channel architecture, it has evolved to support real time applications as well

Infrastructure estate composed of over 3,000 department servers, 20,000 desktops and 1,000 nodes in the Central Server Farm

Case study Business requirements

Reduce business risk by reducing the workload on the central mainframe (thereby removing capacity and economic constraints)

Improve IT performance and reduce costs related to data processing

Optimise distributive processing capacity across estate

Ensure effective utilisation of the Central Server Farm

Case study Solution architecture

Avanade joined forces with management at Development Division (Architecture Office) and at the Group’s Division staff (Competence Centre) to develop a solution based on Grid Computing called the Avanade Grid Architecture (AGA)

AGA runs on the Microsoft .NET platform designed to enable Service Oriented Architecture solutions

All system functionality exposed as web services

The ‘chat’ between each of the nodes and the central system is SOAP over HTTP

Case study Solution security

AGA leverages the security model defined for WS-Security

Particular attention has been devoted to application security issues. For example, by implementing Code Access Security, it is possible to allow the infrastructure to execute only digitally signed tasks

Alternatively AGA can ensure that only the infrastructure can initiate execution of tasks and prevent additional tasks being initiated or authorised.

The security model is extensible via API

Case study Solution integration

Flexibility and developers can quickly implement new processing tasks thanks to a set of APIs which is provided

Extended to support computing nodes on the Linux platform, realise the value of existing investments and third-party solutions

Incorporate open standards for Grid deployment

Retain manageability

Case study Workload evolution

First deployed for analysis of web server traffic to mine customer insight

Extended to classic batch processing of accounting jobs

As confidence in platform grew, MPS experimented with risk management scenario analysis

Fully integrated into frond and mid-office analytics, delivering daily regulatory risk reporting (Value at Risk, capital adequacy, Basel I)

Integrated with key third-party trading and risk applications

Over 1,000 jobs running on a daily basis

Case study Application functionality

Currently testing a new Portfolio Analysis Workbench application for intra-day analysis

Case study Lessons

Grid meets peak demands for ‘overflow’ processing

Allows timeshifted jobs to optimise utilisation over whole estate

New front end client applications developed to harness the AGA core platform to deliver specific tasks

Provide building blocks for dedicated computational engines, managing persistence of data and logic but able to farm load easily and grab spare capacity

AGA.NET overview

Design principles

AGA.NET design began with the realisation that the majority of nodes available for ad hoc task processing will be running Windows

Must be easy to refactor existing code to run across the Grid

Furthermore, ease of deployment and manageability are vital for realworld adoption

Hence the platform is built to take full advantage of Windows Management Instrumentation (WMI) and Active Directory

Design principles Co-ordination

The Co-ordinating Node performs three key functions

Scheduling: accepting new Task requests

Matching: assigning Task components to appropriate Nodes

Monitoring: policing activity and ensuring Tasks complete or are reallocated

Allows static and dynamic rules to govern allocation of tasks

Design principles Manageability

Controlled allocation of Tasks and managed deployment of all software / configuration (a Package) to computing Nodes

For example, Nodes can be configured by:

o Machine class – group membership

o Maximum complexity – measure of load which can be assigned

o Other measures of dynamic performance

Design principles Grid Security

Two aspects of Grid Security Framework

Infrastructure security:

Governed by Active Directory membership (nodes and admin) Communication secured by IPSEC Polling service on specific TCP port

Code security:

Set at compile time, verified at runtime Digitally signed assemblies

AGA.NET Logical architecture

Persistent Data Storage

Download Manager Cache Manager

Task Factory

Isolated Run-time environment (x task instance)

Generic Task State Management

Instrumentation

Exception Handler

Poller

Computing Node

Coordinating Node

Scheduler

Monitor

Task Request

State Data

External Client

Scheduler Client Interface

Data Source

Tasks Queue

Data Storage Access Layer

Nodes Inventory

Tasks Metadata

State Information

Data SourceData SourceData Source

Launcher

Tasks Package Repository

Package Manager

Matcher

Node State Data Provider

Design principles Resilience

The central Governance Node monitors overall task execution and can easily re-queue incomplete tasks

All state for the Grid and intermediate results are stored in SQL Server database where normal resilience procedures apply e.g. Clustering

Governance Node can be clustered but loss of service will not impact Grid processing already underway

Restarting or failover of the Governance Node allows new scheduling and active monitoring to recommence

Administration Example New package

Design principles Application development

AGA.NET is designed to facilitate deployment of existing business logic across the grid

Unlike academic or language / hardware neutral frameworks, AGA.NET assumes Windows development environment

Must maintain minimum complexity to ensure broadest adoption

Ensure compliance with emerging Grid standards such as WSRF (Web Services Resource Framework) for stateful web services

AGA.NET Sample code

using System; using Avanade.AGA.BaseClasses;using Avanade.AGA.Client; namespace TaskSample{

public class SimpleTask : Task{

public SimpleTask(){}

 public override TaskExecutionStatus ExecuteImpl(){

// Reads parameters provided when the task was scheduledInt32 iNum1 = (Int32) Context.InputParameters["Num1"];Int32 iNum2 = (Int32) Context.InputParameters["Num2"];

 Int32 iTot = iNum1 + iNum2;

 // Saves the result of the processing to the task’s stateContext.State["Tot"] = iTot;

 // Commits the contextContext.CommitState();

// Traces a messageLogTaskMessage(MessageType.TRACE, "Done!");

// Signals task’s completionreturn TaskExecutionStatus.Completed;

}}

}

Excel Services and Grid

Office 2007 / Excel Services deployment

Web and device access

Front, middle and back office solutions

(Unix, Linux, Mainframe)

Traders

QuantsDedicated

server cluster

Ad hoc Windows grid farm (AGA.NET)

Excel Services

Scale out

Distribute

Integrate

‘Out of the box’ versus ‘development required’

Function Out of the box Development required

Share and calculate sheets Yes

Audit and secure access Yes

Workflow Yes

Access via web, Excel client Yes

Excel client use all CPU (e.g. 1, 2 or 4) in desktop

Yes

Excel client with >4 CPU No Requires integration with WCC or Grid

Excel Services 1 sheet per server CPU Yes

Run user developed models/functions Partial Likely to need code tuning and some re-development

Access via other application Partial Likely to need integration effort for complex apps

Scale out with Windows Compute Cluster (WCC)

Partial Likely to need conversion to C and tuning

Scale out with Grid No Requires integration to chosen Grid tools

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Contact details

Richard HallChief Technology Officer

Avanade UK

141 Wardour Street, London, W1F 0UT

Email: richardha@avanade.com

Web: www.avanade.com/uk

Blog: richardhall.spaces.live.com