Grid-enabled problem solving environments

This section describes some related problem solving environments (PSEs) that are used for engineering design optimisation or concurrent engineering. This is to give the research an opportunity to learn important grid features that are suitable for optimisation problems as well as to identify the challenges that PSEs faced to solve optimisation problems. Although there are many PSEs, only 5 will be described within the context of this research. This is because from literature and industry survey (see Figures 2.3, 2.4, 2.6, 4.5 and 4.6 in chapters 2 and 4 respectively), these PSEs are the most favoured by researchers in design optimisation. For example, Globus is the defector middleware for most grid researchers.

2.6.1 Geodise

Geodise is one of the numerous UK e-Science projects which aim to provide grid- based seamless access to intelligent knowledge repositories, state-of-the-art collection of design optimisation and search tools, analysis codes, distributed computing services and data resources for multi-objective design optimisation (http://www.geodise.org/). The project was funded by the UK EPSRC (Engineering and Physical Sciences Research Council) for an initial period of 3 years from 2001 to 2004 with a grant value of £2,872,450 ($5,084,237). The project is based at the University of Southampton in UK with collaborating research partners at Universities of Oxford and Manchester also in UK. Collaborating companies are BAE Systems, Rolls Royce and Microsoft UK Ltd. The basic components of the Geodise Toolkit are application services, collaboration toolkits, data mining and analysis services, data management services, domain ontology and metadata, workflow services and problem solving environments. Geodise is usually used in conjunction with Matlab for its scripting and visualisation features. Matlab also have optimisation algorithms such as GA (Genetic Algorithm) for optimisation and search applications. Geodise has proved to be suitable for engineering processes involving computational fluid dynamics (CFD). The use of Geodise tools by the engineers is facilitated by intelligent design advisors targeted initially at CFD (Pound et al., 2003). Geodise is open source

software and is freely downloadable at the Geodise website. Figure 2.8 presents a simple Geodise architecture.

Traceability

Parallel Grid Machines

Clusters

Grid Resource Providers Pay-Per-Use Intelligent Resource Provider Licenses and Code Intelligent Application Manager Knowledge Repository Ontology/and Language for Engineering and Design Search Session Database Optimisation Archive Engineer Globus/Condor

CAD Systems Analysis

CAD DS CFD IDEAS FEA PDE FM FM Design Archive PORTAL Reliability & Security OPTIMISATION COMPUTATION APPLICATION SERVICE PROVIDER Visualisation OPTIONS System

Figure 2.8: Simple Geodise Architecture (Cox et al., 2001)

Geodise project is an attempt to improve the efficiency of design engineers by providing them with optimisation tools as grid services (Cox et al., 2001). The portal allows authorised design engineers seamless access to the system and the databases allow input and output operations managed by a service. The designer is guided through an optimisation knowledge-based service. The system allows incorporation of commercial systems such as CAD (computer aided design) and analysis tools by application providers as services. This demonstrates how design optimisation can be made more efficient by making search and optimisation tools handy to designers (Eres et al., 2004).

2.6.2 FIPER

FIPER (Federated Intelligent Product Environment) is a 4-year project sponsored by the National Institute for Standards and Technology-Advanced Technology Program (NIST-ATP) in the U.S. Its aim is to produce an intelligent system that leverages the

emerging web technologies in which engineering tools such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), PDM (Product Data Management) and optimisation algorithms (Rohl et al., 2000) act as distributed service providers as well as service requestors communicating through intelligent context models for concurrent engineering design optimisation. FIPER has a grant worth of $21.5 million with Ohio and Stanford Universities as academic partners and GE (General Electric) teaming with Engineous Software, BFGoodrich, Parker Hannifin and Ohio Aerospace Institute as company partners. FIPER uses GE Aircraft Engine functionalities to demonstrate the capturing of designer’s knowledge in Knowledge Based Engineering (KBE) systems to create Intelligent Master Model (IMM). This IMM contains the ‘what’, the ‘why’ and the ‘how’ of a design (Rohl et al., 2000) using multi-objective optimisation algorithms to produce a range of global optimal solutions. FIPER provides a graphical environment that permits interactive click-and-drag Grid programming interface which users can reuse and execute concurrently (Sobolewski and Kolonay, 2006).

2.6.3 SORCER

SORCER (Service Oriented Computing Environment) is an extension of the work of FIPER project. SORCER lab is based at the Texas Tech University, U.S.A. The goal of SORCER is to form Grids of distributed services that provide engineering data, applications, tools and evolutionary computing (EC) optimisation algorithms (Soorianarayanan and Sobolewski, 2004) for concurrent engineering design disciplines.

2.6.4 DAME

DAME (Distributed Aircraft Maintenance Environment) is also a UK project which provides an intelligent grid demonstrator system for health monitoring and fault trouble-shooting in mobile aircrafts. The need for mobile devices to have secured and seamless access to Grid services while actively working on the field is important in both scientific and business disciplines (Ong et al., 2005). In this framework, optimisation algorithms are used to select optimal diagnostic solutions based on certain constraints and parameters using multiple objective functions for the optimisation. The DAME project develops grid-based fault diagnosis and prognosis for aircraft maintenance (Jackson et al., 2003). This is done by capturing data and

information on various parts of the aircraft and providing this information to maintenance engineers as grid services when there is a deviation from normal behaviour. This helps engineers to take prompt decision.

2.6.5 Globus toolkit

Globus is an open source toolkit considered as the de facto middleware for large-scale computing services. The Globus toolkit is funded by various organisations such as the Defense Advanced Research Projects Agency (DARPA) USA, National Science Foundation (NSF) USA, Globus Alliance and the UK e-Science program. The I-WAY (Information Wide Area Year) project which succeeded in linking many supercomputing centres was the first initiative that led to the development of the Globus toolkit in 1995. Globus toolkit consists of many services and protocols such as Globus Security Infrastructure (GSI), Globus Information Services (GIS) and Globus Resource Allocation and Management (GRAM). Globus has evolved to the current version 4.0 with many additions to the protocols DAI (Data Access and Integration) developed by the UK e-Science Centre and the standard ‘plumbing’ features of the OGSA (Open Grid Services Architecture) which provides standard implementation interface for participants (service providers and service requestors) in the Grid so as to ease and facilitate interoperability among heterogeneous resources and services (Foster and Kesselman, 1999). Globus works on a layered architecture. The lowest layer is called the fabric layer. This layer manages the core hardware and operating systems as well as shared computational resources (Foster et al., 2003). This layer has the capability to implement local specific MODO resource sharing using shared files and data catalogues. The next layer is the connectivity layer which is responsible for the network and connectivity authentication protocols such as TCP/IP (Transmission Control Protocol/Internet Protocol) for secure communication. It also uses APIs (Application Programmer’s Interfaces) and SDKs (Software Development Kits) for programmers to write communication codes. The third layer is the resource layer. This layer consists of core grid shared resources and it does initiation, monitoring and control of sharing individual resources within the grid community. This layer is where optimisation resources contributed by individual users are managed separately. The fourth layer is the collective layer which coordinates multiple sharing of resources. This layer is crucial as the whole resources in the grid are managed here. The fifth layer is the application layer. This layer is the one that interfaces directly with the end

users and it consists of applications. This is the layer where the design parameter input service is run to present input interface to designers. To join in the Grid, local users must follow the standard protocols to reduce customisation efforts (Foster et al., 2001). This philosophy encourages autonomic grid resource management. This research is using Globus as its middleware for implementing services for multi- objective optimisation. Figure 2.9 shows the schematic diagram of Globus architecture. Application Layer Collective Layer Resource Layer Connectivity Layer Fabric Layer Languages / Frameworks

Collective Services Protocols (APIs and SDKs)

Resource Service Protocols (APIs and SDKs)

Connectivity Protocols (APIs, TCP/IP)

Figure 2.9: Globus Layered Architecture (Foster and Kesselman, 1999)

2.6.6 Challenges that PSEs face in solving MODO problems

Now that the research has studied some PSEs, it is essential to look at problems that PSEs face in solving MODO problems. One of the major issues in the implementation of PSEs for MODO is dynamic and coordinated resource sharing in the presence of heterogeneous resources under high level of dependability. Although such capabilities are part of the OGSA, it is difficult to attain an appreciable level of coordination among distributed service composition and selection (Cheung et al., 2004). For example, if a resource on node A happens to be unavailable when node B needs it, what happens? This is an active area of research to ensure QoS to grid users. However, researchers are looking at providing redundancy in the form of grid overlay architecture that could take care of grid service failures (Grace et al., 2004). Computational and data adequacy for MODO applications is also still not met, even with the cycle-stealing concept using Condor scheduler. For example particle physics and multi-objective optimisation of complex systems using grid systems still present

challenges due to constraints such as global and local policies, dynamic resource utilisation and inadequate response time (Ranganatan and Foster, 2002). In this research, the concept of single population parallelisation is adopted to ensure efficient and speedy optimisation process. Single population parallelisation is the process of sending sub-parts of the optimisation process to different nodes with all sub-tasks sharing the same population and common parameters. Though Globus and other middleware provide good authentication and authorisation capabilities, different companies and researchers prefer different models to be adopted when building a PSE. This is why there is hardly any PSE that serve a generic purpose; most PSEs are developed to solve particular or specific problems. PSEs that are built for commercial purpose are restricted to legacy middleware because of the security issues. The GRIA project is one of such examples. In this research, the Virtual Organisation Membership Service (VOMS) is configured in Globus to assign different authorisation and authentication rights to different users based on the needs of each user. This concept allows users to have control over their resources. One aspect that is often ignored is the support and training aspect during PSE implementation. In this research, the proposed service specification document describes the negotiating terms between providers and users. These terms which are under the service level agreement includes training, support, availability, licence renewal and service updates.

Having identified some problems facing PSEs, the next section intends to identify the process and procedure for defining a grid service within PSEs such that certain precautionary measures are taken to address some of the problems.