Private Cloud Deployment on Shared Computer Labs

A computer laboratory in a school or college is often shared for multiple class and lab sessions. However, often the computers in the lab are just left idling for an extended period of time. Those are potential resources to be harvested for cloud services. This manuscript details the deployment of a private cloud on the shared computer labs. Fundamental services like operation manager, configuration manager, cloud manager, and schedule manager were put up to power on/off computers remotely, specify each computer’s OS configuration, manage cloud services (i.e., provision and retire virtual machines), and schedule OS switching tasks, respectively. OpenStack was employed to manage computer resources for cloud services. The deployment of private cloud can improve the computers’ utilization on the shared computer labs

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

Coordinating Resource Use in Open Distributed Systems

In an open distributed system, computational resources are peer-owned, and distributed over time and space. The system is open to interactions with its environment, and the resources can dynamically join or leave the system, or can be discovered at runtime. This dynamicity leads to opportunities to carry out computations without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination, and control, which present challenges in a dynamic, open environment. In this thesis, I present an approach to address these challenges by separating the functionality concerns of concurrent computations from those of coordinating their resource use, with the purpose of reducing programming complexity, and aiding development of correct, efficient, and resource-aware concurrent programs. As a first step towards effectively coordinating distributed resources, I developed DREAM, a Distributed Resource Estimation and Allocation Model, which enables computations to reason about future availability of resources. I then developed a fine-grained resource coordination scheme for distributed computations. The coordination scheme integrates DREAM-based resource reasoning into a distributed scheduler, for deciding and enforcing fine-grained resource-use schedules for distributed computations. To control the overhead caused by the coordination, a tuner is implemented which explicitly balances the overhead of the control mechanisms against the extent of control exercised. The effectiveness and performance of the resource coordination approach have been evaluated using a number of case studies. Experimental results show that the approach can effectively schedule computations for supporting various types of coordination objectives, such as ensuring Quality-of-Service, power-efficient execution, and dynamic load balancing. The overhead caused by the coordination mechanism is relatively modest, and adjustable through the tuner. In addition, the coordination mechanism does not add extra programming complexity to computations

Architectures for ubiquitous 3D on heterogeneous computing platforms

Today, a wide scope for 3D graphics applications exists, including domains such as scientific visualization, 3D-enabled web pages, and entertainment. At the same time, the devices and platforms that run and display the applications are more heterogeneous than ever. Display environments range from mobile devices to desktop systems and ultimately to distributed displays that facilitate collaborative interaction. While the capability of the client devices may vary considerably, the visualization experiences running on them should be consistent. The field of application should dictate how and on what devices users access the application, not the technical requirements to realize the 3D output. The goal of this thesis is to examine the diverse challenges involved in providing consistent and scalable visualization experiences to heterogeneous computing platforms and display setups. While we could not address the myriad of possible use cases, we developed a comprehensive set of rendering architectures in the major domains of scientific and medical visualization, web-based 3D applications, and movie virtual production. To provide the required service quality, performance, and scalability for different client devices and displays, our architectures focus on the efficient utilization and combination of the available client, server, and network resources. We present innovative solutions that incorporate methods for hybrid and distributed rendering as well as means to manage data sets and stream rendering results. We establish the browser as a promising platform for accessible and portable visualization services. We collaborated with experts from the medical field and the movie industry to evaluate the usability of our technology in real-world scenarios. The presented architectures achieve a wide coverage of display and rendering setups and at the same time share major components and concepts. Thus, they build a strong foundation for a unified system that supports a variety of use cases.Heutzutage existiert ein großer Anwendungsbereich für 3D-Grafikapplikationen wie wissenschaftliche Visualisierungen, 3D-Inhalte in Webseiten, und Unterhaltungssoftware. Gleichzeitig sind die Geräte und Plattformen, welche die Anwendungen ausführen und anzeigen, heterogener als je zuvor. Anzeigegeräte reichen von mobilen Geräten zu Desktop-Systemen bis hin zu verteilten Bildschirmumgebungen, die eine kollaborative Anwendung begünstigen. Während die Leistungsfähigkeit der Geräte stark schwanken kann, sollten die dort laufenden Visualisierungen konsistent sein. Das Anwendungsfeld sollte bestimmen, wie und auf welchem Gerät Benutzer auf die Anwendung zugreifen, nicht die technischen Voraussetzungen zur Erzeugung der 3D-Grafik. Das Ziel dieser Thesis ist es, die diversen Herausforderungen zu untersuchen, die bei der Bereitstellung von konsistenten und skalierbaren Visualisierungsanwendungen auf heterogenen Plattformen eine Rolle spielen. Während wir nicht die Vielzahl an möglichen Anwendungsfällen abdecken konnten, haben wir eine repräsentative Auswahl an Rendering-Architekturen in den Kernbereichen wissenschaftliche Visualisierung, web-basierte 3D-Anwendungen, und virtuelle Filmproduktion entwickelt. Um die geforderte Qualität, Leistung, und Skalierbarkeit für verschiedene Client-Geräte und -Anzeigen zu gewährleisten, fokussieren sich unsere Architekturen auf die effiziente Nutzung und Kombination der verfügbaren Client-, Server-, und Netzwerkressourcen. Wir präsentieren innovative Lösungen, die hybrides und verteiltes Rendering als auch das Verwalten der Datensätze und Streaming der 3D-Ausgabe umfassen. Wir etablieren den Web-Browser als vielversprechende Plattform für zugängliche und portierbare Visualisierungsdienste. Um die Verwendbarkeit unserer Technologie in realitätsnahen Szenarien zu testen, haben wir mit Experten aus der Medizin und Filmindustrie zusammengearbeitet. Unsere Architekturen erreichen eine umfassende Abdeckung von Anzeige- und Rendering-Szenarien und teilen sich gleichzeitig wesentliche Komponenten und Konzepte. Sie bilden daher eine starke Grundlage für ein einheitliches System, das eine Vielzahl an Anwendungsfällen unterstützt

Multicore Scheduling of Real-Time Irregular Parallel Algorithms in Linux

Face à estagnação da tecnologia uniprocessador registada na passada década, aos principais fabricantes de microprocessadores encontraram na tecnologia multi-core a resposta `as crescentes necessidades de processamento do mercado. Durante anos, os desenvolvedores de software viram as suas aplicações acompanhar os ganhos de performance conferidos por cada nova geração de processadores sequenciais, mas `a medida que a capacidade de processamento escala em função do número de processadores, a computação sequencial tem de ser decomposta em várias partes concorrentes que possam executar em paralelo, para que possam utilizar as unidades de processamento adicionais e completar mais rapidamente. A programação paralela implica um paradigma completamente distinto da programação sequencial. Ao contrário dos computadores sequenciais tipificados no modelo de Von Neumann, a heterogeneidade de arquiteturas paralelas requer modelos de programação paralela que abstraiam os programadores dos detalhes da arquitectura e simplifiquem o desenvolvimento de aplicações concorrentes. Os modelos de programação paralela mais populares incitam os programadores a identificar instruções concorrentes na sua lógica de programação, e a especificá-las sob a forma de tarefas que possam ser atribuídas a processadores distintos para executarem em simultâneo. Estas tarefas são tipicamente lançadas durante a execução, e atribuídas aos processadores pelo motor de execução subjacente. Como os requisitos de processamento costumam ser variáveis, e não são conhecidos a priori, o mapeamento de tarefas para processadores tem de ser determinado dinamicamente, em resposta a alterações imprevisíveis dos requisitos de execução. `A medida que o volume da computação cresce, torna-se cada vez menos viável garantir as suas restrições temporais em plataformas uniprocessador. Enquanto os sistemas de tempo real se começam a adaptar ao paradigma de computação paralela, há uma crescente aposta em integrar execuções de tempo real com aplicações interativas no mesmo hardware, num mundo em que a tecnologia se torna cada vez mais pequena, leve, ubíqua, e portável. Esta integração requer soluções de escalonamento que simultaneamente garantam os requisitos temporais das tarefas de tempo real e mantenham um nível aceitável de QoS para as restantes execuções. Para tal, torna-se imperativo que as aplicações de tempo real paralelizem, de forma a minimizar os seus tempos de resposta e maximizar a utilização dos recursos de processamento. Isto introduz uma nova dimensão ao problema do escalonamento, que tem de responder de forma correcta a novos requisitos de execução imprevisíveis e rapidamente conjeturar o mapeamento de tarefas que melhor beneficie os critérios de performance do sistema. A técnica de escalonamento baseado em servidores permite reservar uma fração da capacidade de processamento para a execução de tarefas de tempo real, e assegurar que os efeitos de latência na sua execução não afectam as reservas estipuladas para outras execuções. No caso de tarefas escalonadas pelo tempo de execução máximo, ou tarefas com tempos de execução variáveis, torna-se provável que a largura de banda estipulada não seja consumida por completo. Para melhorar a utilização do sistema, os algoritmos de partilha de largura de banda (capacity-sharing) doam a capacidade não utilizada para a execução de outras tarefas, mantendo as garantias de isolamento entre servidores. Com eficiência comprovada em termos de espaço, tempo, e comunicação, o mecanismo de work-stealing tem vindo a ganhar popularidade como metodologia para o escalonamento de tarefas com paralelismo dinâmico e irregular. O algoritmo p-CSWS combina escalonamento baseado em servidores com capacity-sharing e work-stealing para cobrir as necessidades de escalonamento dos sistemas abertos de tempo real. Enquanto o escalonamento em servidores permite partilhar os recursos de processamento sem interferências a nível dos atrasos, uma nova política de work-stealing que opera sobre o mecanismo de capacity-sharing aplica uma exploração de paralelismo que melhora os tempos de resposta das aplicações e melhora a utilização do sistema. Esta tese propõe uma implementação do algoritmo p-CSWS para o Linux. Em concordância com a estrutura modular do escalonador do Linux, ´e definida uma nova classe de escalonamento que visa avaliar a aplicabilidade da heurística p-CSWS em circunstâncias reais. Ultrapassados os obstáculos intrínsecos `a programação da kernel do Linux, os extensos testes experimentais provam que o p-CSWS ´e mais do que um conceito teórico atrativo, e que a exploração heurística de paralelismo proposta pelo algoritmo beneficia os tempos de resposta das aplicações de tempo real, bem como a performance e eficiência da plataforma multiprocessador.With sequential machines approaching their physical bounds, parallel computers are rapidly becoming pervasive in most areas of modern technology. To realize the full potential of parallel platforms, applications must split onto concurrent parts that can be assigned to different processors and execute in parallel. Parallel programming models abstract the myriad of parallel computer specifications to simplify the development of concurrent applications, allowing programmers to decompose their code onto concurrent tasks, and leaving it to the runtime system to schedule these tasks for parallel execution. The resulting parallelism is often input-dependent and irregular, requiring that the mapping of tasks to processors be performed at runtime in response to dynamic changes of the workload. Motivated by the promises of performance scalability and cost effectiveness, real-time researchers are now beginning to exploit the benefits of parallel processing, with ground-breaking scheduling heuristics to improve the efficiency of time-sensitive concurrent applications. Realtime developments are switching to open scenarios, where real-time tasks of variable and unpredictable size share the available processing resources with other applications, making it essential to utilize as much of the available processing capacity as possible. The p-CSWS algorithm employs bandwidth isolation, capacity-sharing and work-stealing to exploit the intra-task parallelism of hard and soft real-time executions on parallel platforms. This thesis proposes an implementation of the p-CSWS scheduler for the Linux kernel, to evaluate its applicability to real scenarios and bring Linux one step closer to becoming a viable open real-time platform. To the best of our knowledge we are the first to employ scheduling heuristics to exploit dynamic parallelism of real-time tasks on the Linux kernel. Through extensive tests, we show that...

Computational Sprinting: Exceeding Sustainable Power in Thermally Constrained Systems

Although process technology trends predict that transistor sizes will continue to shrink for a few more generations, voltage scaling has stalled and thus future chips are projected to be increasingly more power hungry than previous generations. Particularly in mobile devices which are severely cooling constrained, it is estimated that the peak operation of a future chip could generate heat ten times faster than than the device can sustainably vent. However, many mobile applications do not demand sustained performance; rather they comprise short bursts of computation in response to sporadic user activity. To improve responsiveness for such applications, this dissertation proposes computational sprinting, in which a system greatly exceeds sustainable power margins (by up to 10Ã?) to provide up to a few seconds of high-performance computation when a user interacts with the device. Computational sprinting exploits the material property of thermal capacitance to temporarily store the excess heat generated when sprinting. After sprinting, the chip returns to sustainable power levels and dissipates the stored heat when the system is idle. This dissertation: (i) broadly analyzes thermal, electrical, hardware, and software considerations to analyze the feasibility of engineering a system which can provide the responsiveness of a plat- form with 10Ã? higher sustainable power within today\u27s cooling constraints, (ii) leverages existing sources of thermal capacitance to demonstrate sprinting on a real system today, and (iii) identifies the energy-performance characteristics of sprinting operation to determine runtime sprint pacing policies

Parallel Real-Time Scheduling for Latency-Critical Applications

In order to provide safety guarantees or quality of service guarantees, many of today\u27s systems consist of latency-critical applications, e.g. applications with timing constraints. The problem of scheduling multiple latency-critical jobs on a multiprocessor or multicore machine has been extensively studied for sequential (non-parallizable) jobs and different system models and different objectives have been considered. However, the computational requirement of a single job is still limited by the capacity of a single core. To provide increasingly complex functionalities of applications and to complete their higher computational demands within the same or even more stringent timing constraints, we must exploit the internal parallelism of jobs, where individual jobs are parallel programs and can potentially utilize more than one core in parallel. However, there is little work considering scheduling multiple parallel jobs that are latency-critical. This dissertation focuses on developing new scheduling strategies, analysis tools, and practical platform design techniques to enable efficient and scalable parallel real-time scheduling for latency-critical applications on multicore systems. In particular, the research is focused on two types of systems: (1) static real-time systems for tasks with deadlines where the temporal properties of the tasks that need to execute is known a priori and the goal is to guarantee the temporal correctness of the tasks prior to their executions; and (2) online systems for latency-critical jobs where multiple jobs arrive over time and the goal to optimize for a performance objective of jobs during the execution. For static real-time systems for parallel tasks, several scheduling strategies, including global earliest deadline first, global rate monotonic and a novel federated scheduling, are proposed, analyzed and implemented. These scheduling strategies have the best known theoretical performance for parallel real-time tasks under any global strategy, any fixed priority scheduling and any scheduling strategy, respectively. In addition, federated scheduling is generalized to systems with multiple criticality levels and systems with stochastic tasks. Both numerical and empirical experiments show that federated scheduling and its variations have good schedulability performance and are efficient in practice. For online systems with multiple latency-critical jobs, different online scheduling strategies are proposed and analyzed for different objectives, including maximizing the number of jobs meeting a target latency, maximizing the profit of jobs, minimizing the maximum latency and minimizing the average latency. For example, a simple First-In-First-Out scheduler is proven to be scalable for minimizing the maximum latency. Based on this theoretical intuition, a more practical work-stealing scheduler is developed, analyzed and implemented. Empirical evaluations indicate that, on both real world and synthetic workloads, this work-stealing implementation performs almost as well as an optimal scheduler

Deploying an Ad-Hoc Computing Cluster Overlaid on Top of Public Desktops

A computer laboratory is often a homogeneous environment, in which the computers have the same hardware and software settings. Conducting system tests in this laboratory environment is quite challenging, as the laboratory is supposed to be shared with regular classes. This manuscript details the use of desktop virtualization to deploy dynamically a virtual cluster for testing and ad-hoc purposes. The virtual cluster can support an environment completely different from the physical environment and provide application isolation essential for separating the testing environment from the regular class activities. Windows 7 OS was running in the host desktops, and VMware Workstation was employed as the desktop virtualization manager. The deployed virtual cluster comprised virtual desktops installed with Ubuntu Desktop Linux OS. Lightweight applications using VMware VIX library and shell scripts were developed and employed to manage job submission to the virtual cluster. Evaluations on the virtual cluster’s deployment show that we can leverage on desktop virtualization to quickly and dynamically deploy a testing environment while exploiting the underutilized compute resources

Fault-tolerant computer study

A set of building block circuits is described which can be used with commercially available microprocessors and memories to implement fault tolerant distributed computer systems. Each building block circuit is intended for VLSI implementation as a single chip. Several building blocks and associated processor and memory chips form a self checking computer module with self contained input output and interfaces to redundant communications buses. Fault tolerance is achieved by connecting self checking computer modules into a redundant network in which backup buses and computer modules are provided to circumvent failures. The requirements and design methodology which led to the definition of the building block circuits are discussed

Standard interface definition for avionics data bus systems

Data bus for avionics system of space shuttle, noting functions of interface unit, error detection and recovery, redundancy, and bus control philosoph