Stephen Winter

GEMLCA: Running Legacy Code Applications as Grid Services

There are many legacy code applications that cannot be run in a Grid environment without significant modification. To avoid re-engineering of legacy code, we developed the Grid Execution Management for Legacy Code Architecture (GEMLCA) that enables deployment of legacy code applications as Grid services. GEMLCA implements a general architecture for deploying legacy applications as Grid services without the need for code re-engineering, or even access to the source files. With GEMLCA, only a user-level understanding is required to run a legacy application from a standard Grid service client. The legacy code runs in its native environment using the GEMLCA resource layer to communicate with the Grid client, thus hiding the legacy nature of the application and presenting it as a Grid service. GEMLCA as a Grid service layer supports submitting jobs, getting their results and status back. The paper introduces the GEMLCA concept, its life cycle, design and implementation. It also presents as an example a legacy simulation code that has been successfully transformed into a Grid service using GEMLCA.

Buttressing volatile desktop grids with cloud resources within a reconfigurable environment service for workflow orchestration

Cloud technology has the potential for widening access to high-performance computational resources for e-science research, but barriers to engagement with the technology remain high for many scientists. Workflows help overcome barriers by hiding details of underlying computational infrastructure and are portable between various platforms including cloud; they are also increasingly accepted within e-science research communities. Issues arising from the range of workflow systems available and the complexity of workflow development have been addressed by focusing on workflow interoperability, and providing customised support for different science communities. However, the deployments of such environments can be challenging, even where user requirements are comparatively modest. RESWO (Reconfigurable Environment Service for Workflow Orchestration) is a virtual platform-as-a-service cloud model that allows leaner customised environments to be assembled and deployed within a cloud. Suitable distributed computation resources are not always easily affordable and can present a further barrier to engagement by scientists. Desktop grids that use the spare CPU cycles available within an organisation are an attractively inexpensive type of infrastructure for many, and have been effectively virtualised as a cloud-based resource. However, hosts in this environment are volatile: leading to the tail problem, where some tasks become randomly delayed, affecting overall performance. To solve this problem, new algorithms have been developed to implement a cloudbursting scheduler in which durable cloud-based CPU resources may execute replicas of jobs that have become delayed. This paper describes experiences in the development of a RESWO instance in which a desktop grid is buttressed with CPU resources in the cloud to support the aspirations of bioscience researchers. A core component of the architecture, the cloudbursting scheduler, implements an algorithm to perform late job detection, cloud resource management and job monitoring. The experimental results obtained demonstrate significant performance improvements and benefits illustrated by use cases in bioscience research.

GEMLCA: grid execution management for legacy code architecture design

The grid execution management for legacy code architecture (GEMLCA) describes a solution for exposing and executing legacy applications through an OGSI grid service. This architecture has been introduced in a previous paper by the same authors where the general concept was demonstrated by creating an OGSI/GT3 version of the MadCity traffic simulator. The class structure of the architecture is described presenting each component and describing the relationships between them. Also, the current architecture implementation is evaluated through test results gained by running the MadCity traffic simulator as a C/PVM legacy application.

A graphical toolset for simulation modelling of parallel systems

In this paper, a simulation model for incorporation within a performance-oriented parallel software development environment is presented. This development environment is composed of a graphical design tool, a simulation facility, and a visualisation tool. Simulation allows parallel program performance to be predicted and design alternatives to be compared. The target parallel system models a virtual machine composed of a cluster of workstations interconnected by a local area network. The simulation model architecture is modular and extensible which allows re-configuration of the platform. The model description and the validation experiments which have been conducted to assess the correctness and the accuracy of the model are also presented.

Creating scalable traffic simulation on clusters

We describe the implementation of a transport simulation in a parallel environment. The implementation is based on a graphical parallel programming environment called P-GRADE. The transport simulator, called MadCity, simulates a specific road network of a city and shows cars moving on the roads. To achieve scalability of the traffic simulation, the use of templates is necessary. This helps to control the number of participating processes required for the simulation without making modifications to the simulators source code. Performance results are collected from four, eight and sixteen nodes of the Parsifal cluster and compared with the sequential execution results of the simulator. The implementation of the transport simulator is extended further to support the simulation of multiple cities within the same cluster and on the Grid.

Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

Scientific workflows are common in biomedical research, particularly for molecular docking simulations such as those used in drug discovery. Such workflows typically involve data distribution between computationally demanding stages which are usually mapped onto large scale compute resources. Volunteer or Desktop Grid (DG) computing can provide such infrastructure but has limitations resulting from the heterogeneous nature of the compute nodes. These constraints mean that reducing the make span of a given workflow stage submitted to a DG becomes problematic. Late jobs can significantly affect the make span, often completing long after the bulk of the computation has finished. In this paper we present a system capable of significantly reducing the make span of a scientific workflow. Our system comprises a DG which is dynamically augmented with an infrastructure as a service (IaaS) Cloud. Using this solution, the Cloud resources are used to process replicated late jobs. Our system comprises a core component termed the scheduler, which implements an algorithm to perform late job detection, Cloud resource management (instantiation and reuse), and job monitoring. We offer a formal definition of this algorithm, whilst we also provide an evaluation of our prototype using a production scientific workflow.

Traffic Simulation in P-Grade as a Grid Service

Grid Execution Management for Legacy Code Architecture (GEMLCA) is a general architecture to deploy existing legacy applications as Grid services without re-engineering the original code. Using GEMLCA from the P-Grade portal, legacy code programs can be accessed as Grid services and even participate in complex Grid workflows. The parallel version of MadCity, a discrete time-based traffic simulator, was created using P-Grade. This paper describes how MadCity is offered as a Grid service using GEMLCA and how this solution is embedded into the P-Grade portal.

Large-scale virtual screening experiments on Windows Azure-based cloud resources

Molecular docking simulations have high potential to contribute to a wide area of molecular and biomedical research in various disciplines including molecular biology, drug design, environmental studies and psychology. Conducting large‐scale molecular docking experiments requires a vast amount of computing resources. Several types of distributed computing infrastructures have been investigated and utilized recently to conduct such simulations, including service and desktop grid systems or local clusters. This paper investigates and analyses how Windows Azure‐based cloud resources can be applied for this purpose. A virtual screening experiment framework has been implemented on a Windows Azure‐based cloud using the generic worker concept. Virtual machines can be instantiated in the cloud on demand scaling up the simulations based on the volume of molecules to be docked and the available financial resources. Bioscientists are able to execute the simulations and visualise the results from a high‐level user interface. The paper describes the experiences when implementing the molecular docking application on this novel platform and provides the first benchmarking experiments to evaluate the suitability of the infrastructure for computation intensive simulations. Copyright

Publishing and Executing Parallel Legacy Code Using an OGSI Grid Service

This paper describes an architecture for publishing and executing parallel legacy code using an OGSI Grid service. A framework is presented that aids existing legacy applications to be deployed as OGSI Grid services and the concept is demonstrated by creating an OGSI/GT3 version of the Westminster MadCity traffic simulator application. This paper presents the Grid Execution Management for Legacy Code Architecture (GEMLCA), and describes the progress and achievements of its implementation.

CTDNet-a mechanism for the concurrent execution of lambda graphs

The authors describe CTDNet, a data-driven reduction machine for the concurrent execution of applicative functional programs in the form of lambda calculus expressions. Such programs are stored as binary-tree-structured process graphs in which all processes maintain pointers to their immediate neighbors (i.e. ancestor and two children). Processes are of two basic types: master processes, which represent the original process graph, and slave processes, which carry out the actual executional work and are dynamically created and destroyed. CTDNet uses a distributed eager evaluation scheme with a modification to evaluate conditional expressions lazily, together with a form of distributed string reduction with some graphlike modifications. >