An Intelligent Robust Mouldable Scheduler for HPC & Elastic Environments

Traditional scheduling techniques are of a by-gone era and do not cater for the dynamism of new and emerging computing paradigms. Budget constraints now push researchers to migrate their workloads to public clouds or to buy into shared computing services as funding for large capital expenditures are few and far between. The sites still hosting large or shared computing infrastructure have to ensure that the system utilisation and efficiency is as high as ossible. However, the efficiency can not come at the cost of quality of service as the availability of public clouds now means that users can move away. This thesis presents a novel scheduling system to improve job turn-around-time. The Robust Mouldable Scheduler outlined in these pages utilises real application benchmarks to profile system performance and predict job execution times at different allocations, something no other scheduler does at present. The system is able to make an allocation decisions ensuring the jobs can fit into spaces available on the system using fewer resources without delaying the job completion time. The results demonstrate significant improvement in workload turn-around-times using real High Performance Computing (HPC) trace logs. Utilising three years of the University of Huddersfield trace logs the mouldable scheduler consistently simulated faster workload completion. Further, the results establish that by not relying on the user to suggest resource allocations for jobs the system is able to mitigate bad-put into the system leading to improved efficiency. A thorough investigation of Research Computing Systems (RCS), workload management systems, scheduling algorithms and strategies, benchmarking and profiling toolkits, and simulators is presented to establish the state of the art. Within this thesis a method to profile applications and workloads that leverages common open-source tools on HPC systems is presented. The resultant toolkit is used to profile the University of Huddersfield workload. This workload forms the basis to evaluate the mouldable scheduler. The research includes advance computing paradigms such as utilising Artificial Intelligence methods to improve the efficiency of the scheduler, or Surge Computing, where workloads are scaled beyond institutional firewalls through elastic compute systems

Sexual violence as a sexual script in mainstream online pornography

This article examines the ways in which mainstream pornography positions sexual violence as a normative sexual script by analysing the video titles found on the landing pages of the three most popular pornography websites in the United Kingdom. The study draws on the largest research sample of online pornographic content to date and is unique in its focus on the content immediately advertised to a new user. We found that one in eight titles shown to first-time users on the first page of mainstream porn sites describe sexual activity that constitutes sexual violence. Our findings raise serious questions about the extent of criminal material easily and freely available on mainstream pornography websites and the efficacy of current regulatory mechanisms

When consumers switch product form: A cross-country study between UK, Brazil and China

This study is a cross-cultural comparison of product form switching in FMCG; to understand the adoption of new products within emerging markets. Brazil and China represent the emergent markets, whilst the UK provides comparison as a developed market. Laundry detergent is the chosen category of study as it is an FMCG but with complex habits, emotions, considerations and touchpoints impacting the final decision making process. Data were collated through 139 focus groups across the UK, Brazil and China and analysed using journey mapping techniques. The results of the consumer journeys demonstrate that FMCG product switching is a complex multifaceted process

Establishing a University Grid for HPC Applications

This thesis documents a project undertaken at the University of Huddersfield between October 2009 and August 2010 to setup a High Performance Computing (HPC) resource, which would serve the University’s research community by providing a robust computing solution. This thesis will look at all the various kinds of requirements different fields have, with regard to a computing solution, and the tools available to meet these specific needs. This report serves as a manual for any small to medium sized institution that considers setting up a local HPC resource. It looks at all considerations regarding hardware, software, licensing, infrastructure, HR etc for setting up a centralised computing resource with sustainability and robustness being the central aim of the proposed resource. The possibilities of cross-continent and cross-institution collaboration using Clusters and Grid technologies are explored and the method for connecting to the UK eScience community through the NGS is explained

Hybrid Computer Cluster with High Flexibility

In this paper we present a cluster middleware, designed to implement a Linux-Windows Hybrid HPC Cluster, which not only holds the characteristics of both operating system, but also accepts and schedules jobs in both environments. Beowulf Clusters have become an economical and practical choice for small-and-medium-sized institutions to provide High Performance Computing (HPC)resources. The HPC resources are required for running simulations, image rendering and other calculations, and to support the software requiring a specific operating system. To support the software, smallscale computer clusters would have to be divided in two or more clusters if they are to run on a single operating system. The x86 virtualisation technology would help running multiple operating systems on one computer, but only with the latest hardware which many legacy Beowulf clusters do not have. To aid the institutions, who rely on legacy nonvirtualisation- supported facilities rather than high-end HPC resources, we have developed and deployed a bi-stable hybrid system built around Linux CentOS 5.5 with the improved OSCAR middleware; and Windows Server 2008 and Windows HPC 2008 R2. This hybrid cluster is utilised as part of the University of Huddersfield campus grid. Keywords-cluster middleware, computer cluster, job scheduler, resource manage

Robust Mouldable Scheduling Using Application Benchmarking For Elastic Enviornments

In this paper we present a framework for developing an intelligent job management and scheduling system that utilizes application specific benchmarks to mould jobs onto available resources. In an attempt to achieve the seemingly irreconcilable goals of maximum usage and minimum turnaround time this research aims to adapt an open-framework benchmarking scheme to supply information to a mouldable job scheduler. In a green IT obsessed world, hardware efficiency and usage of computer systems becomes essential. With an average computer rack consuming between 7 and 25 kW it is essential that resources be utilized in the most optimum way possible. Currently the batch schedulers employed to manage these multi-user multi-application environments are nothing more than match making and service level agreement (SLA) enforcing tools. These management systems rely on user prescribed parameters that can lead to over or under booking of compute resources. System administrators strive to get maximum “usage efficiency” from the systems by manual fine-tuning and restricting queues. Existing mouldable scheduling strategies utilize scalability characteristics, which are inherently 2dimensional and cannot provide predictable scheduling information. In this paper we have considered existing benchmarking schemes and tools, schedulers and scheduling strategies, and elastic computational environments. We are proposing a novel job management system that will extract performance characteristics of an application, with an associated dataset and workload, to devise optimal resource allocations and scheduling decisions. As we move towards an era where on-demand computing becomes the fifth utility, the end product from this research will cope with elastic computational environments

Hybrid HPC – Establishing a Bi-Stable Dual Boot Cluster for Linux with OSCAR middleware and Windows HPC 2008 R2

The advent of open source software leading to Beowulf clusters has enabled small to medium sized Higher and Further education institutions to remove the “computational power” factor from research ventures. In an effort to catch up with leading Universities in the realm of research, many Universities are investing in small departmental HPC clusters to help with simulations, renders and calculations. These small HE/FE institutions have in the past benefited from cheaper software and operating system licenses. This raises the question as to which platform Linux of Windows should be implemented on the cluster. As the smaller/medium Universities move into research, many Linux based applications and code better suit their research needs, but the teaching base still keeps the department tied to Windows based applications. In such institutions, where it is usually recycled machines that are linked to form the clusters, it is not often feasible to setup more than one cluster. This paper will propose a method to implement a Linux-Windows Hybrid HPC Cluster that seamlessly and automatically accepts and schedules jobs in both domains. Using Linux CentOS 5.4 with OSCAR 5.2 beta 2 middleware with Windows Server 2008 and Windows HPC 2008 R2 (beta) a bi-stable hybrid system has been deployed at the University of Huddersfield. This hybrid cluster is known as the Queensgate Cluster. We will also examine innovative solutions and practices that are currently being followed in the academic world as well as those that have been recommended by Microsoft® Corp