Jonathan Giddy

Bio: Jonathan Giddy is an academic researcher from Cardiff University. The author has contributed to research in topics: Grid computing & Grid. The author has an hindex of 13, co-authored 22 publications receiving 1368 citations. Previous affiliations of Jonathan Giddy include Monash University.

A computational economy for grid computing and its implementation in the Nimrod-G resource broker

Abstract: Computational grids that couple geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service (QoS). Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user-defined QoS requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength grids. We present the results of experiments using the Nimrod-G resource broker for scheduling parametric computations on the World Wide Grid (WWG) resources that span five continents.

An Economy Driven Resource Management Architecture for Global Computational Power Grids

Abstract: The growing computational power requirements of grand challenge applications has promoted the need for linking highperformance computational resources distributed across multiple organisations. This is fueled by the availability of the Internet as a ubiquitous commodity communication media, low cost high-performance machines such as clusters across multiple organisations, and the rise of scientific problems of multi-organisational interest. The availability of expensive, special class of scientific instruments or devices and data sources in few organisations has increased the interest in offering a remote access to these resources. The recent popularity of coupling (local and remote) computational resources, special class of scientific instruments, and data sources across the Internet for solving problems has led to the emergence of a new platform called “Computational Grid”. This paper identifies the issues in resource management and scheduling driven by computational economy in the emerging grid computing context. They also apply to clusters of clusters environment (known as federated clusters or hyperclusters) formed by coupling multiple (geographically distributed) clusters located in the same or different organisations. We discuss our current work on the Nimrod/G resource broker, whose scheduling mechanism is driven by a user supplied application deadline and a resource access budget. However, current Grid access frameworks do not provide the dynamic resource trading services that are required to facilitate flexible application scheduling. In order to overcome this limitation, we have proposed an infrastructure called GRid Architecture for Computational Economy (GRACE). In this paper we present the motivations for grid computing, resource management architecture, Nimrod/G resource broker, computational economy, and GRACE infrastructure and its APIs along with future work.

An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications

Abstract: Computational Grids are becoming attractive and promising platforms for solving large-scale (problem solving) applications of multi-institutional interest. However, the management of resources and scheduling computations in the Grid environment is a complex undertaking as they are (geographically) distributed, heterogeneous in nature, owned by different individuals or organisations with their own policies, different access and cost models, and have dynamically varying loads and availability. This introduces a number of challenging issues such as site autonomy, heterogeneous substrate, policy extensibility, resource allocation or co-allocation, online control, scalability, transparency, and “economy of computations”. Some of these issues are being addressed by system-level Grid middleware toolkits such as Globus.

Economic models for management of resources in peer-to-peer and grid computing

Abstract: The accelerated development in Peer-to-Peer (P2P) and Grid computing has positioned them as promising next generation computing platforms. They enable the creation of Virtual Enterprises (VE) for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. The framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price for goods based on supply-and-demand and their value to the user. They include commodity market, posted price, tenders and auctions. In this paper, we discuss the use of these models for interaction between Grid components in deciding resource value and the necessary infrastructure to realize them. In addition to normal services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking, and enforcement services. Furthermore, we demonstrate the usage of some of these economic models in resource brokering through Nimrod/G deadline and cost-based scheduling for two different optimization strategies on the World Wide Grid (WWG) testbed that contains peer-to-peer resources located on five continents: Asia, Australia, Europe, North America, and South America.

Grid Resource Management, Scheduling and Computational Economy

Abstract: This paper identifies the issues in resource management and scheduling in the emerging grid computing context and briefly discusses techniques for scheduling using computational economy concept. In the context of Nimrod/G system, scheduling based on the deadline and cost for parametric computing over the grid is discussed. We also highlight the future work on grid a for computational economy (GRACE) infrastructure that the grid tools and application developers can use.

Above the Clouds: A Berkeley View of Cloud Computing

Abstract: Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.

GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing

Abstract: SUMMARY Clusters, Grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving largescale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers’ or owners’ requirements, and continuously adapt to changes in resource availability. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java-based discrete-event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time- and space-shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod-G

A taxonomy and survey of grid resource management systems for distributed computing

Abstract: The resource management system is the central component of distributed network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and services. In this paper, an abstract model and a comprehensive taxonomy for describing resource management architectures is developed. The taxonomy is used to identify approaches followed in the implementation of existing resource management systems for very large-scale network computing systems known as Grids. The taxonomy and the survey results are used to identify architectural approaches and issues that have not been fully explored in the research. Copyright © 2001 John Wiley & Sons, Ltd.