Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Hierarchical Parallelisation of Functional Renormalisation Group Calculations -- hp-fRG

The functional renormalisation group (fRG) has evolved into a versatile tool in condensed matter theory for studying important aspects of correlated electron systems. Practical applications of the method often involve a high numerical effort, motivating the question in how far High Performance Computing (HPC) can leverage the approach. In this work we report on a multi-level parallelisation of the underlying computational machinery and show that this can speed up the code by several orders of magnitude. This in turn can extend the applicability of the method to otherwise inaccessible cases. We exploit three levels of parallelisation: Distributed computing by means of Message Passing (MPI), shared-memory computing using OpenMP, and vectorisation by means of SIMD units (single-instruction-multiple-data). Results are provided for two distinct High Performance Computing (HPC) platforms, namely the IBM-based BlueGene/Q system JUQUEEN and an Intel Sandy-Bridge-based development cluster. We discuss how certain issues and obstacles were overcome in the course of adapting the code. Most importantly, we conclude that this vast improvement can actually be accomplished by introducing only moderate changes to the code, such that this strategy may serve as a guideline for other researcher to likewise improve the efficiency of their codes

Probabilistic grid scheduling based on job statistics and monitoring information

This transfer thesis presents a novel, probabilistic approach to scheduling applications on computational Grids based on their historical behaviour, current state of the Grid and predictions of the future execution times and resource utilisation of such applications. The work lays a foundation for enabling a more intuitive, user-friendly and effective scheduling technique termed deadline scheduling. Initial work has established motivation and requirements for a more efficient Grid scheduler, able to adaptively handle dynamic nature of the Grid resources and submitted workload. Preliminary scheduler research identified the need for a detailed monitoring of Grid resources on the process level, and for a tool to simulate non-deterministic behaviour and statistical properties of Grid applications. A simulation tool, GridLoader, has been developed to enable modelling of application loads similar to a number of typical Grid applications. GridLoader is able to simulate CPU utilisation, memory allocation and network transfers according to limits set through command line parameters or a configuration file. Its specific strength is in achieving set resource utilisation targets in a probabilistic manner, thus creating a dynamic environment, suitable for testing the scheduler’s adaptability and its prediction algorithm. To enable highly granular monitoring of Grid applications, a monitoring framework based on the Ganglia Toolkit was developed and tested. The suite is able to collect resource usage information of individual Grid applications, integrate it into standard XML based information flow, provide visualisation through a Web portal, and export data into a format suitable for off-line analysis. The thesis also presents initial investigation of the utilisation of University College London Central Computing Cluster facility running Sun Grid Engine middleware. Feasibility of basic prediction concepts based on the historical information and process meta-data have been successfully established and possible scheduling improvements using such predictions identified. The thesis is structured as follows: Section 1 introduces Grid computing and its major concepts; Section 2 presents open research issues and specific focus of the author’s research; Section 3 gives a survey of the related literature, schedulers, monitoring tools and simulation packages; Section 4 presents the platform for author’s work – the Self-Organising Grid Resource management project; Sections 5 and 6 give detailed accounts of the monitoring framework and simulation tool developed; Section 7 presents the initial data analysis while Section 8.4 concludes the thesis with appendices and references