Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Using Spammers\u27 Computing Resources for Volunteer Computing

Spammers are continually looking to circumvent counter-measures seeking to slow them down. An immense amount of time and money is currently devoted to hiding spam, but not enough is devoted to effectively preventing it. One approach for preventing spam is to force the spammer\u27s machine to solve a computational problem of varying difficulty before granting access. The idea is that suspicious or problematic requests are given difficult problems to solve while legitimate requests are allowed through with minimal computation. Unfortunately, most systems that employ this model waste the computing resources being used, as they are directed towards solving cryptographic problems that provide no societal benefit. While systems such as reCAPTCHA and FoldIt have allowed users to contribute solutions to useful problems interactively, an analogous solution for non-interactive proof-of-work does not exist. Towards this end, this paper describes MetaCAPTCHA and reBOINC, an infrastructure for supporting useful proof-of-work that is integrated into a web spam throttling service. The infrastructure dynamically issues CAPTCHAs and proof-of-work puzzles while ensuring that malicious users solve challenging puzzles. Additionally, it provides a framework that enables the computational resources of spammers to be redirected towards meaningful research. To validate the efficacy of our approach, prototype implementations based on OpenCV and BOINC are described that demonstrate the ability to harvest spammer\u27s resources for beneficial purposes

Data Center Load Forecast Using Dependent Mixture Model

The dependency on cloud computing is increasing day by day. With the boom of data centers, the cost is also increasing, which forces industries to come up with techniques and methodologies to reduce the data center energy use. Load forecasting plays a vital role in both efficient scheduling and operating a data center as a virtual power plant. In this thesis work a stochastic method, based on dependent mixtures is developed to model the data center load and is used for day-ahead forecast. The method is validated using three data sets from National Renewable Energy Laboratory (NREL) and one other data centers. The proposed method proved better than the classical autoregressive, moving-average, as well as the neural network-based forecasting method, and resulted in a reduction of 7.91% mean absolute percentage error (MAPE) for the forecast. A more accurate forecast can improve power scheduling and resource management reducing the variable cost of power generation as well as the overall data center operating cost, which was quantified as a yearly savings of $13,705 for a typical 100 MW coal fired tier-IV data center

Enhancing the programmability and energy efficiency of storage in hpc and virtualized environments

Mención Internacional en el título de doctorA decade ago computing systems hit a clock and power ceiling that places the energetic challenge among the most relevant issues in High Performance Computing (HPC). Motivated by the fact that computation is increasingly becoming cheaper than data movement in terms of power, our work studies and optimizes data movement across different levels of the software stack. We propose novel methodologies for analyzing, modeling, and optimizing the energy efficiency of data movement. More precisely, we propose methodologies to enhance the understanding of power consumption in the software I/O stack, and optimize the I/O energy efficiency in the operating system’s I/O stack, low-level CPU device drivers, and virtualized environments. Our experimental results show that through the understanding of the different operating system layers and their interaction, it is possible to develop novel coordination techniques that optimize the energy consumption and increase performance of I/O workloads. First, we develop a methodology for data collection, power and performance characterization, and modeling power usage in the I/O stack. Our work presents a detailed study of power and energy usage across all system components during various I/O-intensive workloads. We propose a data gathering methodology that combines software and hardware-based instrumentation in order to study I/O data movement, and develop novel power prediction models employing data analysis techniques. Second, this thesis presents novel CPU-level optimizations that improve the energy efficiency of I/O workloads. We address two issues present in modern processors: thermal imbalance causing performance variation and an inefficient use of CPU resources during I/O workloads. We develop novel techniques for power optimization and thermal efficiency through cross-layer coordination of CPU and I/O management. Third, we also focus on optimizing data sharing among virtual domains. In our work we refer to this as virtualized data sharing, which mainly differs from existing solutions by coordinating data flows through the software I/O stack. We develop a virtualized data sharing solution in order to reduce data movement among virtual environments, introducing new abstractions and mechanisms to more efficiently coordinate storage I/O.Hace una década, los computadores alcanzaron el límite físico de la frecuencia y potencia disipada, estableciendo el consumo energético como uno de los principales obstáculos en el campo de la computación de alto rendimiento. Motivados por el hecho de que la computación resulta cada vez menos costosa que el movimiento de datos en términos de energía, nuestro trabajo estudia y optimiza el movimiento de datos en varios niveles de la arquitectura software. En este trabajo proponemos nuevas metodologías para analizar, modelar y optimizar la eficiencia energética del movimiento de datos. Concretamente, proponemos metodologías para mejorar el análisis del consumo de potencia en la arquitectura software de E/S, así como optimizar la eficiencia energética de: la pila de E/S del sistema operativo, controladores de la CPU y entornos virtuales de E/S. Los resultados experimentales muestran que, mediante la comprensión de la interacción de las capas del sistema operativo, es posible desarrollar nuevas técnicas de coordinación que optimicen el consumo energético e incrementen el rendimiento de las cargas de trabajo de E/S. En primer lugar desarrollamos una metodología para la recolección de datos y la caracterización del rendimiento y consumo de potencia en la pila de E/S. Nuestro trabajo presenta un estudio detallado del consumo energético y de potencia de cada uno de los componentes del sistema durante la ejecución de cargas de trabajo de E/S. Concretamente proponemos una metodología de captura de datos que combina instrumentación hardware y software para estudiar el movimiento de datos, con el fin de desarrollar nuevos modelos de predicción de consumo empleando técnicas de análisis de datos. En segundo lugar, esta Tesis Doctoral presentamos nuevas optimizaciones a nivel de CPU que mejoran la eficiencia energética de las cargas de trabajo de E/S. Para ello consideramos dos problemas fundamentales en los procesadores modernos: el desequilibrio térmico que causa variablidad de rendimiento y el uso ineficiente de los recursos de la CPU durante cargas de trabajo de E/S. Además desarrollamos nuevas técnicas que optimizan la eficiencia energética a través de la coordinación entre las distintas capas del sistema operativo que gestionan CPU y la E/S. En tercer lugar, también centramos este trabajo en la optimización del intercambio de datos entre dominios virtuales. En nuestro trabajo nos referimos a esto como el intercambio de datos virtualizado, que se diferencia principalmente de las soluciones existentes mediante la coordinación de los flujos de datos mediante la cooperación entre distintos dominios virtuales. Para ello desarrollamos una solución de intercambio de datos que minimiza la copia de datos con el fin de reducir el movimiento de datos, e introducimos nuevas abstracciones y mecanismos para coordinar de manera más eficiente el almacenamiento de E/S en entornos virtuales.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Laurent Lefevre.- Vocal: Arturo González Escriban

Dynamic resource provisioning for data center workloads with data constraints

Dynamic resource provisioning, as an important data center software building block, helps to achieve high resource usage efficiency, leading to enormous monetary benefits. Most existing work for data center dynamic provisioning target on stateless servers, where any request can be routed to any server. However, the assumption of stateless behaviors no longer holds for subsystems that subject to data constraints, as a request may depend on a certain dataset stored on a small subset of servers. Routing a request to a server without the required dataset violates data locality or data availability properties, which may negatively impact on the response times. To solve this problem, this thesis provides an unified framework consisting of two main steps: 1) determining the proper amount of resources to serve the workload by analyzing the schedulability utilization bound; 2) avoiding transition penalties during cluster resizing operations by deliberately design data distribution policies. We apply this framework to both storage and computing subsystems, where the former includes distributed file systems, databases, memory caches, and the latter refers to systems such as Hadoop, Spark, and Storm. Proposed solutions are implemented into MemCached, HBase/HDFS, and Spark, and evaluated using various datasets, including Wikipedia, NYC taxi trace, Twitter traces, etc

Software Approaches to Manage Resource Tradeoffs of Power and Energy Constrained Applications

Power and energy efficiency have become an increasingly important design metric for a wide spectrum of computing devices. Battery efficiency, which requires a mixture of energy and power efficiency, is exceedingly important especially since there have been no groundbreaking advances in battery capacity recently. The need for energy and power efficiency stretches from small embedded devices to portable computers to large scale data centers. The projected future of computing demand, referred to as exascale computing, demands that researchers find ways to perform exaFLOPs of computation at a power bound much lower than would be required by simply scaling today's standards. There is a large body of work on power and energy efficiency for a wide range of applications and at different levels of abstraction. However, there is a lack of work studying the nuances of different tradeoffs that arise when operating under a power/energy budget. Moreover, there is no work on constructing a generalized model of applications running under power/energy constraints, which allows the designer to optimize their resource consumption, be it power, energy, time, bandwidth, or space. There is need for an efficient model that can provide bounds on the optimality of an application's resource consumption, becoming a basis against which online resource management heuristics can be measured. In this thesis, we tackle the problem of managing resource tradeoffs of power/energy constrained applications. We begin by studying the nuances of power/energy tradeoffs with the response time and throughput of stream processing applications. We then study the power performance tradeoff of batch processing applications to identify a power configuration that maximizes performance under a power bound. Next, we study the tradeoff of power/energy with network bandwidth and precision. Finally, we study how to combine tradeoffs into a generalized model of applications running under resource constraints. The work in this thesis presents detailed studies of the power/energy tradeoff with response time, throughput, performance, network bandwidth, and precision of stream and batch processing applications. To that end, we present an adaptive algorithm that manages stream processing tradeoffs of response time and throughput at the CPU level. At the task-level, we present an online heuristic that adaptively distributes bounded power in a cluster to improve performance, as well as an offline approach to optimally bound performance. We demonstrate how power can be used to reduce bandwidth bottlenecks and extend our offline approach to model bandwidth tradeoffs. Moreover, we present a tool that identifies parts of a program that can be downgraded in precision with minimal impact on accuracy, and maximal impact on energy consumption. Finally, we combine all the above tradeoffs into a flexible model that is efficient to solve and allows for bounding and/or optimizing the consumption of different resources

The management of academic workloads: full report on findings

The pressures on UK higher education (from explicit competition and growth in student numbers, to severe regulatory demands) are greater than ever, and have resulted in a steady increase in measures taken by universities to actively manage their finances and overall quality. These pressures are also likely to have impacted on staff and, indeed, recent large surveys in the sector have indicated that almost half of respondents find their workloads unmanageable. Against this background it would seem logical that the emphasis on institutional interventions to improve finance and quality, should be matched by similar attention given to the allocation of workloads to staff, and a focus on how best to utilise people’s time - the single biggest resource available within universities. Thus the aim of this piece of research was to focus on the processes and practices surrounding the allocation of staff workloads within higher education. Ten diverse organisations were selected for study: six universities in the UK, two overseas universities and two non higher education (but knowledge-intensive) organisations. In each, a crosssection of staff was selected, and in-depth interviews carried out. A total of 59 such interviews were carried out across the ten organisations. By identifying typical practices, as well as interesting alternatives, views on the various strengths and weaknesses of each of their workload allocation approaches was collated; and associated factors requiring attention identified. Through an extensive process of analysis, approaches which promoted more equitable loads for individuals, and which might provide synergies for institutions were also investigated

Prediction Models for Estimating the Efficiency of Distributed Multi-Core Systems

The efficiency of a multi-core architecture is directly related to the mechanisms that map the threads (processes in execution) to the cores. Determining the resource availability (CPU and main memory) of the multi-core architecture based on the characteristics of the threads that are in execution is the art of system performance prediction. In this dissertation we develop several prediction models for multi-core architectures and perform empirical evaluations to demonstrate the accuracy of these models. Prediction of resource availability is important in the context of making process assignment, load balancing, and scheduling decisions. In distributed infrastructure, resources are allocated on demand on a chosen set of compute nodes. The nodes chosen to perform the computations dictate the efficiency by which the jobs assigned to them will be executed. The prediction models allows us to estimate the resource availability without explicitly querying the individual nodes. With the model in hand and knowledge of the jobs (such as peak memory requirement and CPU execution profile), we can determine the appropriate compute nodes for each of the jobs in such a way that it will improve resource utilization and speed job execution. More specially, we have accomplished the following as part of this dissertation: (a) Develop mathematical models to estimate the upper- and lower-limits of CPU and memory availability for single- and multi-core architectures. (b) Perform empirical evaluation in a heterogeneous environment to validate the accuracy of the models. (c) Introduce two task assignment policies that are capable of dispatching tasks to distributed compute nodes intelligently by utilizing composite prediction and CPU usage models. (d) Propose a technique and introduce models to identify combinations of parameters for efficiency usage of GPU devices to obtain optimal performance

Artificial Intelligence and Climate Change

As artificial intelligence (AI) continues to embed itself in our daily lives, many focus on the threats it poses to privacy, security, due process, and democracy itself. But beyond these legitimate concerns, AI promises to optimize activities, increase efficiency, and enhance the accuracy and efficacy of the many aspects of society relying on predictions and likelihoods. In short, its most promising applications may come, not from uses affecting civil liberties and the social fabric of our society, but from those particularly complex technical problems lying beyond our ready human capacity. Climate change is one such complex problem, requiring fundamental changes to our transportation, agricultural, building, and energy sectors. This Article argues for the enhanced use of AI to address climate change, using the energy sector to exemplify its potential promise and pitfalls. The Article then analyzes critical policy tradeoffs that may be associated with an increased use of AI and argues for its disciplined use in a way that minimizes its limitations while harnessing its benefits to reduce greenhouse-gas emissions