Pegasus: Particle Physicists Engagement with Grids: A Socio-technical Usability Study

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A review of the IT artifact in Grid, Cloud and Utility Computing.

In a much quoted paper, Orlikowski and Iacconno (2001) charged the IS research community with ignoring technology or failing to make it the central focus of research, and thereby cheating the information systems field of its raison d’etre. Perhaps their critique is well taken, certainly it seems to have incited many to boldly claim that they have seen the light (or never lost sight of it) and will now account for the IT artefact be it RFID, mobile devices, CRM etc, and not by proxy.  But in all this the most ancient, enduring and distinctive manifestation of ‘the IT artefact’, as a computer, has been overlooked – its raw power as an information storage and processing device. It almost seems that, since the coming of the VLSI chip, and the year on year empirical re-validation of Moore’s Law (Moore 1965), that computing power and the computer itself can be just taken for granted[1]. We do not seem very often to worry about the computer and assume that if we need more computing power then it will be just around the corner (Cornford 2003). Yet we observe recently a change in the nature of computing which reorients our assumptions about this “computer power” from individual to collective. We observe the changing nature of computer processing within the information systems field, reviewing in particular the shift from individualised PCs running shrink-wrapped software packages supported by servers and databases, to a new form of distributed computing model represented by Utility, Grid and Cloud computing. These new concepts remain poorly defined within information systems and are dominated by practitioner marketing materials which confuse. The focus of this review is thus on the new Computing Artifact, and the technological components it is constructed from.

Utility computing is the conceptual core of our analysis but much of the current debate on this idea is discourse on the concept of “Cloud Computing” – a more marketable vision perhaps than utility computing. Cloud Computing is a new and confused term. Gartner define cloud computing succinctly as “a style of computing where massively scalable IT-related capabilities are provided ‘as a service’ using Internet technologies to multiple external customers”[2]. Yet our interest is not in the particulars of cloud computing itself but the opportunities presented for researchers and practitioners by this new technology.  We argue that fundamental to both cloud computing and utility computing is a decoupling of the physicality of IT infrastructure from the architecture of such infrastructures use. While in the past we thought about the bare-metal system (a humming grey box in an air-conditioned machine room with physical attributes and a host of peripherals) today such ideas are conceptual and virtualized – hidden from view.  It is this decoupling which will form the basis of our discussion of the technology of the Grid. 

There certainly is a strong element of hype in much of the Utility, Grid and Cloud computing discourse and perhaps such hype is necessary. As Swanson and Ramillar (1997) remind us, the organising visions of information and communications technology are formed as much in extravagant claims and blustering sales talk as they are in careful analysis, determination of requirements or proven functionality.  We can at times observe a distinct tension between the technologists’ aspiration to develop and define an advanced form of computer infrastructure, and a social construction of such technology through discourses of marketing, public relations.  We find a plethora of terms associated with Utility computing within commercial settings include Autonomic Computing; Grid Computing; On-Demand Computing; Real-time Enterprise; Service-Oriented Computing; Adaptive computing (or Adaptive Enterprise) (Goyal and Lawande 2006; Plaszczak and Wellner 2007) and peer-to-peer computing (Foster and Iamnitchi 2003). We have adopted the term “utility computing” as our categorization of this mixed and confused definitional landscape.

Many authors who write about Utility Computing start with an attempt to provide a definition, often accompanied by a comment as to the general “confusion” surrounding the term (e.g. (Gentzsch 2002)). It is unrealistic to expect an accepted definition of a technology which is still emerging, but by tracing the evolution of definitions in currency we can see how the understanding of new technology is influenced by various technical, commercial and socio-political forces. Put another way, the computer is not a static thing, but rather a collection of meanings that are contested by different groups (Bijker 1995), and as any other technology, embodies to degrees its  developers’ and users’ social, political, psychological, and professional commitments, skills, prejudices, possibilities and constraints.

Computing Utility: The Shifting nature of Computing.

Since Von Neumann defined our modern computing architecture we have seen computers as consisting of a processing unit (capable of undertaking calculation) and a memory (capable of storing instructions and data for the processing unit to use).  Running on this machine is operating system software which manages (and abstracts) the way applications software makes use of this physical machine. The development of computing networks, client-server computing and ultimately the internet essentially introduced a form of communication into this system – allowing storage and computing to be shared with other locations or sites - but ultimately the concept of a "personal computer" or "server computer" remains.  

This basic computer architecture no longer represents computing effectively. Firstly the physical computer is becoming virtualized – represented as software rather than as a physical machine. Secondly it is being distributed through Grid computing infrastructure such that it is owned by virtual rather than physical organizations. Finally these two technologies are brought together in a commoditization of computing infrastructure as cloud computing – where all physicality of the network and computer is hidden from view. It is for this reason that in 2001 Shirky –at a  P2P Webservices conference stated that “Thomas Watson’s famous quote that’ I think there is a world market for maybe five computers’ was wrong - he overstated the number by four”. For Shirky the computer was now a single device collectively shared.  All PCs, mobile phones and connected devices share this Cloud of services on demand – and where processing occurs is not relevant. We now review the key technologies involved in Utility Computing (see table).

  1: Internet – Bandwidth and Internet Standards

At the core of the Utility Computing model is the network. The internet and its associated standards have enabled interoperability among systems and provides the foundation for Grid Standards.

2: Virtualisation


Central to the Cloud Computing idea is the concept of Virtualising the machine. While we desire services, these are provided by personal-machines (albeit simulated in software).

3:Grid Computing Middleware and Standards

Just as the Internet infrastructure (standards, hardware and software) provides the foundation of the Web, so Grid Standards and Software extend this infrastructure to provide utility computing utilising large clusters of distributed  computers.

Internet – Bandwidth and Standards

The internet emerged because of attempts to connect mainframe computers together to undertake analysis beyond the capability of one machine - for example within the SAGE air-defence system or ARPANET for scientific analysis (Berman and Hey 2004). Similarly the Web emerged from a desire to share information globally between various different computers (Berners-Lee 1989). Achieving such distribution of resources is however founded upon a communications infrastructure (of wires and radio-waves) capable of transferring information at the requisite speed (bandwidth) and without delays (latency). Until the early 2000s however the bandwidth required for large applications and processing services to interact was missing. During the dot-com boom however a huge amount of fibre-optic cable and network routing equipment was installed across the globe by organisations, such as the failed WorldCom, which reduced costs dramatically and increased availability.

Having an effective network infrastructure in place is not enough. A set of standards (protocols) are also required which define mechanisms for resource sharing (Baker, Apon et al. 2005). Internet standards (HTTP/HTML/TCP-IP) made the Web possible by defining how information is shared globally through the internet. These standards ensure that a packet of information is reliably directed between machines. It is this standardised high-speed high-bandwidth Internet infrastructure upon which Utility Computing is built.


Virtualization for cloud computing is a basic idea of providing a software simulation of an underlying hardware machine.  These simulated machines (so called Virtual Machines) present themselves to the software running upon them as identical to a real machine of the same specification. As such the virtual machine must be installed with an operating system (e.g. Windows or Linux) and can then run applications within it. This is not a new technology and was first demonstrated in 1967 by IBM’s CP/CMS systems as a means of sharing a mainframe with many users who are each presented with their own “virtual machine” (Ceruzzi 2002).  However its relevance to modern computing rests in its ability to abstract the computer away from the physical box and onto the internet. “Today the challenge is to virtualize computing resources over the Internet. This is the essence of Grid computing, and it is being accomplished by applying a layer of open Grid protocols to every “local” operating system, for example Linux, Windows, AIX, Solaris, z\OS” (Wladawsky-Berget 2004). Once such Grid enabled virtualization is achieved it is possible to decouple the hardware from the now virtualized machine, for example running multiple virtual machines on one server or moving a virtual machine between servers using the internet. Crucially for the user it appears they are interacting with a machine with similar attributes to a desktop machine or server - albeit somewhere within the internet-cloud.

Grid Computing

The term “Grid” is increasingly used in discussions about the future of ICT infrastructure, or more generally in discussion of how computing will be done in the future. Unlike “Cloud computing” which emerges and belongs to an IT industry and marketing domain, the term “Grid Computing” emerged from the super-computing (High Performances Computing) community (Armbrust, Fox et al. 2009). Our discussion of Utility computing begins with this concept of Grids as a foundation. As with the other concepts however for Grids hyperbole around the concept abounds, with arguments proposed that they are “the next generation of the internet”, “the next big thing”; or that will “overturn strategic and operating assumptions, alter industrial economics, upset markets  (…) pose daunting challenges for every user and vendor” (Carr 2005) and even “provide the electronic foundation for a global society in business, government, research, science and entertainment” (Berman, Fox et al. 2003). Equally, Grids have been accused of faddishness and that “there is nothing new” in comparison to older ideas, or that the term is used simply to attract funding or to sell a product with little reference to computational Grids as they were originally conceived (Sottrup and Peterson 2005).

From a technologists perspective an overall description might be that Grid technology aims to provide utility computing as a transparent, seamless and dynamic delivery of computing and data resources when needed, in a similar way to the electricity power Grid (Chetty and Buyya 2002; Smarr 2004). Indeed the word grid is directly taken from the idea of an electricity grid, a utility delivering power as and when needed. To provide that power on demand a Grid is built (held together) by a set of standards (protocols) specifying the control of such distributed resources. These standards are embedded in the Grid middleware, the software which powers the Grid.  In a similar way to how Internet Protocols such as FTP and HTTP enable information to be past through the internet and displayed on users PCs, so Grid protocols enable the integration of  resources such as sensors, data-storage, computing processors etc (Wladawsky-Berget 2004).

The idea of the Grid is usually traced back to the mid 1990s and the I-Way project to link together a number of US supercomputers as a ‘metacomputer’ (Abbas, 2004).  This was led by Ian Foster of the University of Chicago and Argonne National Laboratory. Foster and Carl Kesslemenn then the Globus project to develop the tools and middle ware for this metacomputer[3]. This tool kit rapidly took off in the world of supercomputing and Foster remains a prominent proponent of the Grid. According to Foster and Kesselman’s (1998) “bible of the grid” a computational Grid is “a hardware and software infrastructure that provides dependable, consistent, pervasive and inexpensive access to high-end computational capabilities”. In this Foster highlights “high-end” in order to focus attention on Grids as supercomputing resource supporting large scale science; “Grid technologies seek to make this possible, by providing the protocols, services and software development kits needed to enable flexible, controlled resource sharing on a large scale” (Foster 2000)[4].

Three years after their first book however the same authors shift their focus, again speaking of Grids as "coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations" (Foster, Kesselman et al. 2001). The inclusion of “multi-institutional” within this 2001 definition highlights the scope of the concept as envisaged by these key Grid proponents, with Berman (2003) further adding that Grids enable resource sharing “on a global scale”. Such definitions, and the concrete research projects that underlie them, make the commercial usage of the Grid seem hollow and opportunistic. These authors seem critical of the contemporaneous re-badging by IT companies of existing computer-clusters and databases as “Grid enabled” [5](Goyal and Lawande 2006; Plaszczak and Wellner 2007). This critique seems to run through the development of Grids within supercomputing research and science where many lament the use of the term by IT companies marketing clusters of computers in one location.

In 2002 Foster provides a three point checklist to assess a Grid (Foster 2002). A Grid 1) coordinates resources that are NOT subject to centralized control; 2) uses standard, open, general purpose protocols and interfaces; 3) delivers non-trivial qualities of service. Fosters highlighting of ‘NOT’, and the inclusion of ‘open protocols’ appear as a further challenge to the commercialization of centralized, closed grids. While this checkpoint was readily accepted by the academic community and is widely cited, unsurprisingly, it was not well received by the commercial Grid community (Plaszczak and Wellner 2007). The demand for “decentralization” was seen as uncompromising and excluded “practically all known ‘grid’ systems in operation in industry” (Plaszczak and Wellner 2007, p57). It is perhaps in response to this definition that the notion of “Enterprise Grids” (Goyal and Lawande 2006) emerged as a form of Grid operating within an organisation, though possibly employing resources across multiple corporate locations employing differing technology. It might ultimately be part of the reason why "Cloud computing" has eclipsed Grid computing as a concept. The commercial usage of Grid terms such as “Enterprise Grid Computing” highlights the use of Grids away from the perceived risk of globally distributed Grids and is the foundation of modern Cloud Computing providers (e.g Amazon S3).  The focus is not to achieve increased computing power through connecting distributed clusters of machines, but as a solution to the “Silos of applications and IT systems infrastructure” within an organisation’s IT function (Goyal and Lawande 2006, p4) through a focus on utility computing and reduced complexity. Indeed in contrast to most academic Grids such “Enterprise Grids” demand homogeneity of resources and centralization within Grids as essential components. It is these Grids which form the backdrop for Cloud Computing and ultimately utility computing in which cloud provider essentially maintain a homogenous server-farm providing virtualized cloud service.  In such cases the Grid is far from distributed, rather existing as “a centralized pool of resources to provide dedicated support for virtualized architecture” (Plaszczak and Wellner 2007,p174) often within data-centers.

Before considering the nature of Grids we discuss their underlying architecture. Foster (Foster, Kesselman et al. 2001) provides an hour-glass Grid architecture (Figure 1). It begins with the fabric which provides the interfaces to the local resources of the machines on the Grid (be they physical or virtual machines). This layer provides the local, resource-specific facilities and could be computer processors, storage elements , tape-robots, sensor, databases or networks. Above this is a resource and connectivity layer which defines the communication and authentication protocols required for transactions to be undertaken on the Grid. The next layer provides a resource management function including directories, brokering systems, as well as monitoring and diagnostic resources. In the final layer reside the tools and applications which use the Grid. It is here that Virtualization software resides to provide services.


Figure 1: The Layered Grid Architecture from Foster 2004.

One of the key challenges of Grids is the management of the resources they manage for the users. Central to achieving this is the concept of a Virtual Organisation (VO). A Virtual Organisation is a set of individuals and/or institutions defined by the sharing rules for a set of resources (Foster and Kesselman 1998) or “a set of Grid entities, such as individuals, applications, services or resources, that are related to each other by some level of trust” (Plaszczak and Wellner 2007). By necessity these resources must be controlled “with resource providers and consumers defining clearly and carefully just what is shared, who is allowed to share, and the conditions under which sharing occurs” (Foster and Kesselman 1998) and for this purpose VOs are technically defined along with the rules of their resources sharing. A Grid VO implies the assumptions of “the absence of central location, central control, omniscience, and an existing trust relationship” (Abbas 2004). It is this ability to control access to resources which is also vital within Cloud Computing - allowing walled-gardens for security and accounting of resource usage for billing.

Various classes and categories of Grids exist. According to Abbas Grids can be categorised according to their increasing scale - desktop grids, cluster grids, enterprise grids, and global grids (Abbas 2004). Desktop Grids are based on existing dispersed desktop PC’s and can create a new computing resource by employing unused processing and storage capacity while the existing user can continue to use the machine. Cluster Grids describe a form of parallel of distributed computer system that consists of a collection of interconnected yet standardised computer nodes[6] working together to act, as far as the user is concerned, as a single unified computing resource[7].   Many existing supercomputers are clusters which “use Smart Software Systems (SSS) to virtualise independent operation-system instances to provide an HPC[8] service” (Abbas 2004). 

All the above are arguably grids, and potentially can just about live up to Fosters 3 tests. However, for the information systems field, for Pegasus, and for those who wish to explore Cloud Computing, it is the final category of global Grids that is the most significant. Global Grids employ the public internet infrastructure to communicate between Grid Nodes, and rely on heterogeneous computing and networking resources.  Some global grids have gained a large amount of publicity by providing social benefit which capture the public imagination. Perhaps the first large scale such project was SETI@home which searches radio-telescope data for signs of extra-terrestrial intelligence. undertaking  research for healthcare and Folding@home concerned with protein folding experiments are other examples. Folding@home indeed can claim to be the worlds most powerful distributed computing network according to the Guinness Book of Records, with 700,000 Sony PlayStation 3 machines and over 1,000 trillion calculations per second[9]. Each works by dividing a problem into steps and distributing software over the internet to the computers of those volunteering. Since within the home and workplace a large number of desktop computers remain idle most of the time such donations have little impact on the user. Indeed the average computer is idle for over 90% of the time, and even when used only a very small amount of the CPU’s capabilities are employed (Smith 2005).

Another way to categories Grids is by the types of solutions that they best address (Jacob 2003). A computational grid is focused on undertaking large numbers of computations rapidly, and hence the focus is on using high performance processors. A data grid’s focus is upon the effective storage and distribution of large amounts of data, usually across multiple organisations of locations. The focus of such systems is upon data integrity, security and ease of access. It should be stressed that there are no hard boundaries between these two types of grid, and one need often pre-supposes the other and real users face both issues.

As an example of a grid project with a more data orientation, consider the  Biomedical Informatics Research Network, a grid infrastructure project that  serves biomedical research needs They express their offerings in terms of 5 complementary elements; a cyber infrastructure, software tools (applications) for biomedical data gathering, resources of shared data, data integration support, an ontology and support for multi-site integration of research activity. As they say, “By intertwining concurrent revolutions occurring in biomedicine and information technology, BIRN is enabling researchers to participate in large scale, cross-institutional research studies where they are able to acquire, share, analyze, mine and interpret both imaging and clinical data acquired at multiple sites using advanced processing and visualization tools.”

Other examples of Grid Computing exist within science, particularly particle physics. The particle  physics community faces the challenge of analyzing the unprecedented amounts of data - some 15 Petabytes per year  - that will be produced by the LHC (Large Hadron Collider) experiments at CERN[10].  To process this data CERN required around 100,000 computer-equivalents[11]  forming its associated grids by 2007, spread across the globe and incorporating a number of grid infrastructures (Faulkner, Lowe et al. 2006). In using the Grid physicists submit their computing-jobs to the Grid which spreads across the globe. Similarly data from the LHC is initially processed at CERN but is quickly spread to 12 computer centres across the world (so called Tier-1 Grid sites). From here data is spread to local data-centres at universities within these countries (Tier-2 sites).


In summary then the concept of a Grid is bound up in the purpose to which it is put, and the foundational technology upon which it is based. For its user however this complexity is hidden – they remain presented with a  computing resource which, they may continue to believe, is a single processor able to analyze their data – just as Von Neumann had envisaged it.


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Armbrust, M., A. Fox, et al. (2009). Above the Clouds: A Berkeley View of Cloud Computing, UC Berkeley Reliable Adaptive Distributed Systems Laboratory.

Baker, M., A. Apon, et al. (2005). "Emerging Grid Standards." Computer.

Berman, F. and T. Hey (2004). The Scientific Imperative. The Grid 2. I. Foster and C. Kesselman. San Francisco, Morgan Kaufmann.

Berman, F., G. Fox, et al. (2003). The Grid: past, present, future. Grid Computing - Making the Global Infrastructure a Reality. F. Berman, G. Fox and T. Hey, John Wiley & Sons, Ltd.

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[1] As the Intel web site modestly puts it, “Moore's Law:Made real by Intel® innovation” see

[2] Gartner Group – “Cloud Computing Confusion Leads to Opportunity” June 2008.

[4] Foster, I (200o) Internet Computing and the Emerging Grid, Nature, 7 December 2000 see

[5] for example suggests grids “enable you to create a single IT infrastructure that can be shared by all your business processes. Similarly suggests Grids as a utility computing cluster available on a timesharing basis.

[6] A computer node consists of a processor, memory, and network connection which acts as part of a larger system and is usually coordinated and controlled from elsewhere. (***need to check this and provide a reference!).

[7] Pfister, G.F. (1998) In search of clusters Prentice Hall 2nd edition.

[8]High Performance Computing.


[11] A computing-equivalent is the amount of processing power equivalent to a top-quality processor at a particular date in time. This allows the amount of processing power required to be estimated despite the fact that the actual processors within the Grid may be heterogeneous.


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Pegasus is funded by the UK EPSRC (Grant no EP/D049954/1).


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