Towards Energy-Proportional Computing for Enterprise-Class Server Workloads

Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Adding to the problem is the inability of the servers to exhibit energy proportionality, i.e., provide energy-ecient execution under all levels of utilization, which diminishes the overall energy eciency of the data center. It is imperative that we realize eective strategies to control the power consumption of the server and improve the energy eciency of data centers. With the advent of Intel Sandy Bridge processors, we have the ability to specify a limit on power consumption during runtime, which creates opportunities to design new power-management techniques for enterprise workloads and make the systems that they run on more energy-proportional. In this paper, we investigate whether it is possible to achieve energy proportionality for an enterprise-class server workload, namely SPECpower ssj2008 benchmark, by using Intel's Running Average Power Limit (RAPL) interfaces. First, we analyze the power consumption and characterize the instantaneous power prole of the SPECpower benchmark at a subsystem-level using the on-chip energy meters exposed via the RAPL interfaces. We then analyze the impact of RAPL power limiting on the performance, per-transaction response time, power consumption, and energy eciency of the benchmark under dierent load levels. Our observations and results shed light on the ecacy of the RAPL interfaces and provide guidance for designing power-management techniques for enterprise-class workloads

Understanding Word Embedding Stability Across Languages and Applications

Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this thesis, we consider several aspects of embedding spaces, including their stability. First, we propose a definition of stability, and show that common English word embeddings are surprisingly unstable. We explore how properties of data, words, and algorithms relate to instability. We extend this work to approximately 100 world languages, considering how linguistic typology relates to stability. Additionally, we consider contextualized output embedding spaces. Using paraphrases, we explore properties and assumptions of BERT, a popular embedding algorithm. Second, we consider how stability and other word embedding properties affect tasks where embeddings are commonly used. We consider both word embeddings used as features in downstream applications and corpus-centered applications, where embeddings are used to study characteristics of language and individual writers. In addition to stability, we also consider other word embedding properties, specifically batching and curriculum learning, and how methodological choices made for these properties affect downstream tasks. Finally, we consider how knowledge of stability affects how we use word embeddings. Throughout this thesis, we discuss strategies to mitigate instability and provide analyses highlighting the strengths and weaknesses of word embeddings in different scenarios and languages. We show areas where more work is needed to improve embeddings, and we show where embeddings are already a strong tool.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162917/1/lburdick_1.pd

Adaptive Performance and Power Management in Distributed Computing Systems

The complexity of distributed computing systems has raised two unprecedented challenges for system management. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, system power consumption must be controlled in order to avoid system failures caused by power capacity overload or system overheating due to increasingly high server density. However, most existing work, unfortunately, either relies on open-loop estimations based on off-line profiled system models, or evolves in a more ad hoc fashion, which requires exhaustive iterations of tuning and testing, or oversimplifies the problem by ignoring the coupling between different system characteristics (\ie, response time and throughput, power consumption of different servers). As a result, the majority of previous work lacks rigorous guarantees on the performance and power consumption for computing systems, and may result in degraded overall system performance. In this thesis, we extensively study adaptive performance/power management and power-efficient performance management for distributed computing systems such as information dissemination systems, power grid management systems, and data centers, by proposing Multiple-Input-Multiple-Output (MIMO) control and hierarchical designs based on feedback control theory. For adaptive performance management, we design an integrated solution that controls both the average response time and CPU utilization in information dissemination systems to achieve bounded response time for high-priority information and maximized system throughput in an example information dissemination system. In addition, we design a hierarchical control solution to guarantee the deadlines of real-time tasks in power grid computing by grouping them based on their characteristics, respectively. For adaptive power management, we design MIMO optimal control solutions for power control at the cluster and server level and a hierarchical solution for large-scale data centers. Our MIMO control design can capture the coupling among different system characteristics, while our hierarchical design can coordinate controllers at different levels. For power-efficient performance management, we discuss a two-layer coordinated management solution for virtualized data centers. Experimental results in both physical testbeds and simulations demonstrate that all the solutions outperform state-of-the-art management schemes by significantly improving overall system performance

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

In 1994, through classic control theory, John, Naim and Towill developed the ‘Automatic Pipeline, Inventory and Order-based Production Control System’ (APIOBPCS) which extended the original IOBPCS archetype developed by Towill in 1982 ─ well-recognised as a base framework for a production planning and control system. Due to the prevalence of the two original models in the last three decades in the academic and industrial communities, this paper aims to systematically review how the IOBPCS archetypes have been adopted, exploited and adapted to study the dynamics of individual production planning and control systems and whole supply chains. Using various databases such as Scopus, Web of Science, Google Scholar (111 papers), we found that the IOBPCS archetypes have been studied regarding the a) modification of four inherent policies related to forecasting, inventory, lead-time and pipeline to create a ‘family’ of models, b) adoption of the IOBPCS ‘family’ to reduce supply chain dynamics, and in particular bullwhip, c) extension of the IOBPCS family to represent different supply chain scenarios such as order-book based production control and closed-loop processes. Simulation is the most popular method adopted by researchers and the number of works based on discrete time based methods is greater than those utilising continuous time approaches. Most studies are conceptual with limited practical applications described. Future research needs to focus on cost, flexibility and sustainability in the context of supply chain dynamics and, although there are a few existing studies, more analytical approaches are required to gain robust insights into the influence of nonlinear elements on supply chain behaviour. Also, empirical exploitation of the existing models is recommended

Explorar performance com Apollo Federation

The growing tendency in cloud-hosted computing and availability supported a shift in soft ware architecture to better take advantage of such technological advancements. As Mono lithic Architecture started evolving and maturing, businesses grew their dependency on soft ware solutions which motivated the shift into Microservice Architecture. The same shift is comparable with the evolution of Monolithic GraphQL solutions which, through its growth and evolution, also required a way forward in solving some of its bot tleneck issues. One of the alternatives, already chosen and proven by some enterprises, is GraphQL Federation. Due to its nobility, there is still a lack of knowledge and testing on the performance of GraphQL Federation architecture and what techniques such as caching strategies, batching and execution strategies impact it. This thesis aims to answer this lack of knowledge by first contextualizing the different as pects of GraphQL and GraphQL Federation and investigating the available and documented enterprise scenarios to extract best practices and to better understand how to prepare such performance evaluation. Next, multiple alternatives underwent the Analytic Hierarchy Process to choose the best way to develop a scenario to enable the performance analysis in a standard and structured way. Following this, the alternative base solutions were analysed and compared to deter mine the best fit for the current thesis. Functional and non-functional requirements were collected along with the rest of the design exercise to enhance the solution to be tested for performance. Finally, after the required development and implementation work was documented, the so lution was tested following the Goal Question Metric methodology and utilizing tools such as JMeter, Prometheus and Grafana to collect and visualize the performance data. It was possible to conclude that indeed different caching, batching and execution strategies have an impact on the GraphQL Federation solution. These impacts do shift between positive (improvements in performance) and negative (performance hindered by strategy) for the different tested strategies.A tendência de crescimento da computação cloud-hosted apoiou uma mudança na arquite tura do software para tirar maior proveito desses avanços tecnológicos. Com a evolução e amadurecimento das arquiteturas monolíticas, as empresas aumentaram sua dependência nas soluções software que motivou a mudança e adoção de arquiteturas de micro serviços. O mesmo se verificou com a evolução das soluções monolíticas GraphQL que, com o seu crescimento e evolução, também requeriam soluções para resolver algumas das suas novas complexidades. Uma das alternativas de resolução, já aplicado e provado na indústria, é o GraphQL Federation. Devido ao seu recente lançamento, ainda não existe um conhecimento sólido na performance de uma arquitetura de GraphQL Federation e que técnicas como estratégias de caching, batching e execution tem impacto sobre a mesma. Esta tese tem como intuito responder a esta falha de conhecimento através de, primeira mente, contextualizar os diferentes aspetos de GraphQL e GraphQL Federations com a investigação de casos de aplicação na indústria, para a extração de boas práticas e compreender o necessário ao desenvolvimento de uma avaliação de performance. De seguida, múltiplas alternativas foram sujeitas ao Analytic Hierarchy Process para escolher a melhor forma de desenvolver um cenário/solução necessária a uma análise de performance normalizada e estruturada. Com isto em mente, as duas soluções base foram analisadas e comparadas para determinar a mais adequada a esta tese. Requisitos funcionais e não funcionais foram recolhidos, assim como todo o restante exercício de design necessário ao desenvolvimento da solução para testes de performance. Finalmente, após a fase de desenvolvimento ser concluída e devidamente documentada, a solução foi testada seguindo a metodologia Goal Question Metric, e aplicando ferramentas como JMeter, Prometheus e Grafana para recolher e visualizar os dados de performance. Foi possível concluir que, de facto, as diferentes estratégias de caching, batching e execution tem impacto numa solução GraphQL Federation. Tais impactos variam entre positivos (com melhorias em termos de performance) e negatives (performance afetada por estratégias) para as diferentes estratégias testadas

Proceedings of the First Karlsruhe Service Summit Workshop - Advances in Service Research, Karlsruhe, Germany, February 2015 (KIT Scientific Reports ; 7692)

Since April 2008 KSRI fosters interdisciplinary research in order to support and advance the progress in the service domain. KSRI brings together academia and industry while serving as a European research hub with respect to service science. For KSS2015 Research Workshop, we invited submissions of theoretical and empirical research dealing with the relevant topics in the context of services including energy, mobility, health care, social collaboration, and web technologies