Using Fine-Grained Cycle Stealing to Improve Throughput, Efficiency and Response Time on a Dedicated Cluster while Maintaining Quality of Service

For various reasons, a dedicated cluster is not always fully utilized even when all of its processors are allocated to jobs. This occurs any time that a running job does not use 100% of each of the processors allocated to it. Keeping in mind the needs of both the cluster’s system administrators and its users, we would like to increase the throughput and efficiency of the cluster while maintaining or improving the average turnaround time of the jobs and the quality of service of the “primary” jobs originally scheduled on the cluster. To increase the throughput and efficiency of the cluster, we schedule background jobs to run concurrently with the primary jobs. However, to achieve our goal of maintaining or improving the average turnaround time of the jobs and the quality of service of the primary jobs, we investigate two methods of prioritizing the CPU usage of the primary and background jobs. The first method uses the existing “nice” mechanism in the 2.4 Linux kernel to give background processes a lower priority than primary processes. The second method involves modifying the 2.4 Linux kernel’s CPU scheduler to create a new guest process priority that prevents guest processes from running when primary processes are runnable. Our results come from empirical investigations using real production applications. Production runs using these applications are regularly performed in the dedicated cluster environment that we used for testing. Measurements of various statistics, such as wall time and CPU time, are taken directly from test runs that use these same production applications. This was helpful for comparison to results from models and synthetic applications. We found that using the existing nice mechanism significantly improves the throughput, efficiency and average turnaround time of the cluster but only at the expense of the quality of service of the primary jobs (primary job running times increased 5-25%). On the other hand, we can use the guest process priority to get similar improvements in throughput, efficiency and average turnaround time while not significantly impacting the quality of service of the primary jobs (primary job running times changed less than 1%)

Green Buildings and Ambient Intelligence: case study for N.A.S.A. Sustainability Base and future Smart Infrastructures

Con la diffusione delle smart infrastructures, espressione con cui ci si riferisce collettivamente ai concetti di smart cities e smart grid, i sistemi di building automation vedono il proprio ruolo espandersi oltre i tradizionali limiti degli ambienti isolati che sono progettati per gestire, supervisionare ed ottimizzare. Da sistemi isolati all’interno di edifici residenziali o commerciali, stanno iniziando ad ottenere un ruolo importante su scala più ampia nell’ambito di scenari più complessi a livello urbano o a livello di infrastruttura. Esempi di questa tendenza possono essere le attuali sperimentazioni in varie città del mondo per automatizzare l’illuminazione pubblica, complessi residenziali diffusi (spesso denominati smart connected comunities) e microgrid locali generate dalla federazione di varie unità residenziali a formare cosidette virtual power plants. A causa di questo processo, ci sono aspettative crescenti circa il potenziale delle reti di automazione di introdurre funzionalità sofisticate da un parte ed efficienza energetica dall’altra, ed entrambi gli aspetti su vasta scala. Sfortunatamente questi due obiettivi sono per diversi motivi in conflitto ed è dunque inevitabile individuare un ragionevole compromesso di progettazione. Questa ricerca realizza una caratterizzazione delle attuali tecnologie di automazione per identificare i termini di tale compromesso, con un’attenzione maggiormente polarizzata sugli aspetti di efficienza energetica, analizzata seguendo un approccio olistico, affrontando diversi aspetti del problema. Indubbiamente, data la complessità del vasto scenario tecnologico delle future smart infrastructures, non c’è una finalità sistematica nel lavoro. Piuttosto si intende fornire un contributo alla conoscenza, dando priorità ad alcune sfide di ricerca che sono altresì spesso sottovalutate. Il Green networking, ovvero l’efficienza energetica nel funzionamento di rete, è una di tali sfide. L’attuale infrastruttura IT globale è costruita su attrezzature che collettivamente consumano 21.4 TWh/anno (Global e-Sustainability Initiative, 2010). Questo è dovuto alla scarsa consapevolezza del fatto che le specifiche dei protocolli di comunicazione hanno varie implicazioni sull’efficienza energetica e alla generale tendenza ad una progettazione ridondante e sovra-dimensionata per il caso peggiore. Questo problema potrebbe essere riscontrato anche nelle reti di automazione, specialmente data la tendenza di cui si discuteva sopra, e in tal caso, queste potrebbero introdurre un ulteriore carbon footprint, in aggiunta a quello della rete internet. In questa ricerca si intende dimensionare tale problema e proporre approcci alternativi agli attuali modelli di hardware e protocollo tipici delle tecnologie di automazione in commercio. Spostandosi dalla rete di controllo all’ambiente fisico, altro obiettivo di questo lavoro è la caratterizzazione di sistemi di gestione automatica dei plug loads, carichi elettrici altrimenti non gestiti da alcun impianto di building automation. Per tali sistemi verranno mostrati i limiti e le potenzialità, identificando potenziali problematiche di design e proponendo un approccio integrato di tali sistemi all’interno di sistemi più ampi di gestione dell’energia. Infine, il meccanismo introdotto nella parte di green networking è potenzialmente in grado di fornire informazioni in tempo reale circa il contesto controllato. Si tratta di un potenziale sfruttabile per sviluppare soluzioni di Demand Side Management, allo scopo di effettuare previsioni di picco e di carico. Questa analisi è attualmente in corso, attraverso una partnership con Enel Distribuzione. With the advent of smart infrastructures, collective expression used here to refer to novel concepts such as smart cities and smart grid, building automation and control networks are having their role expanded beyond the traditional boundaries of the isolated environments they are designed to manage, supervise and optimize. From being confined within residential or commercial buildings as islanded, self-contained systems, they are starting to gain an important role on a wider scale for more complex scenarios at urban or infrastructure level. Example of this ongoing process are current experimental setups in cities worldwide to automate urban street lighting, diffused residential facilities (also often addressed to as smart connected communities) and local micro-grids generated by the federation of several residential units into so-called virtual power plants. Given this underlying process, expectations are dramatically increasing about the potential of control networks to introduce sophisticated features on one side and energy efficiency on the other, and both on a wide scale. Unfortunately, these two objectives are, in several ways, conflicting, and impose to settle for reasonable trade-offs. This research work performs an assessment of current control and automation technologies to identify the terms of this trade-off with a stronger focus on energy efficiency which is analyzed following a holistic approach covering several aspects of the problem. Nevertheless, given the complexity of the wide technology scenario of future smart infrastructure, there isn’t a systematic intention in the work. Rather, this research will aim at providing valuable contribution to the knowledge in the field, prioritizing challenges within the whole picture that are often neglected. Green networking, that is energy efficiency of the very network operation, is one of these challenges. The current worldwide IT infrastructure is built upon networking equipment that collectively consume 21.4 TWh/year (Global e-Sustainability Initiative, 2010). This is the result of an overall unawareness of energy efficiency implications of communication protocols specifications and a tendency toward over-provisioning and redundancy in architecture design. As automation and control networks become global, they may be subject to the same issue and introduce an additional carbon footprint along with that of the internet. This research work performs an assessment of the dimension of this problem and proposes an alternative approach to current hardware and protocol design found in commercial building automation technologies. Shifting from the control network to the physical environment, another objective of this work is related to plug load management systems, which will be characterized as to their performance and limitations, highlighting potential design pitfalls and proposing an approach toward integrating these systems into more general energy management systems. Finally, the mechanism introduced above to increase networking energy efficiency also demonstrated a potential to provide real-time awareness about the context being managed. This potential is currently under investigation for its implications in performing basic load/peak forecasting to support demand side management architectures for the smart grid, through a partnership with the Italian electric utility

Portable parallel stochastic optimization for the design of aeropropulsion components

This report presents the results of Phase 1 research to develop a methodology for performing large-scale Multi-disciplinary Stochastic Optimization (MSO) for the design of aerospace systems ranging from aeropropulsion components to complete aircraft configurations. The current research recognizes that such design optimization problems are computationally expensive, and require the use of either massively parallel or multiple-processor computers. The methodology also recognizes that many operational and performance parameters are uncertain, and that uncertainty must be considered explicitly to achieve optimum performance and cost. The objective of this Phase 1 research was to initialize the development of an MSO methodology that is portable to a wide variety of hardware platforms, while achieving efficient, large-scale parallelism when multiple processors are available. The first effort in the project was a literature review of available computer hardware, as well as review of portable, parallel programming environments. The first effort was to implement the MSO methodology for a problem using the portable parallel programming language, Parallel Virtual Machine (PVM). The third and final effort was to demonstrate the example on a variety of computers, including a distributed-memory multiprocessor, a distributed-memory network of workstations, and a single-processor workstation. Results indicate the MSO methodology can be well-applied towards large-scale aerospace design problems. Nearly perfect linear speedup was demonstrated for computation of optimization sensitivity coefficients on both a 128-node distributed-memory multiprocessor (the Intel iPSC/860) and a network of workstations (speedups of almost 19 times achieved for 20 workstations). Very high parallel efficiencies (75 percent for 31 processors and 60 percent for 50 processors) were also achieved for computation of aerodynamic influence coefficients on the Intel. Finally, the multi-level parallelization strategy that will be needed for large-scale MSO problems was demonstrated to be highly efficient. The same parallel code instructions were used on both platforms, demonstrating portability. There are many applications for which MSO can be applied, including NASA's High-Speed-Civil Transport, and advanced propulsion systems. The use of MSO will reduce design and development time and testing costs dramatically

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Power consumption is rapidly becoming the dominant limiting factor for further improvements in computer design. Curiously, this applies both at the "high end" of workstations and servers and the "low end" of handheld devices and embedded computers. At the high-end, the challenge lies in dealing with exponentially growing power densities. At the low-end, there is a demand to make mobile devices more powerful and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels, without fully exploring their synergy with other levels. Most software techniques target either operating systems or compilers but do not explore the interaction between the two layers. These techniques also have not fully explored the potential of virtual machines for power management. In contrast, we are developing a system that integrates information from multiple levels of software and hardware, connecting these levels through a communication channel. At the heart of this system are a virtual machine that compiles and dynamically profiles code, and an optimizer that reoptimizes all code, including that of applications and the virtual machine itself. We believe this introspective, holistic approach enables more informed power-management decisions

Parallel processing of streaming media on heterogeneous hosts using work stealing

Master'sMASTER OF SCIENC

Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation

Although computer networks are inherently parallel systems, the parallel execution of network simulations on interconnected processors frequently yields only limited benefits. In this thesis, methods are proposed to estimate and understand the parallelization potential of network simulations. Further, mechanisms and architectures for exploiting the massively parallel processing resources of modern graphics cards to accelerate network simulations are proposed and evaluated