A unified model for holistic power usage in cloud datacenter servers

Cloud datacenters are compute facilities formed by hundreds and thousands of heterogeneous servers requiring significant power requirements to operate effectively. Servers are composed by multiple interacting sub-systems including applications, microelectronic processors, and cooling which reflect their respective power profiles via different parameters. What is presently unknown is how to accurately model the holistic power usage of the entire server when including all these sub-systems together. This becomes increasingly challenging when considering diverse utilization patterns, server hardware characteristics, air and liquid cooling techniques, and importantly quantifying the non-electrical energy cost imposed by cooling operation. Such a challenge arises due to the need for multi-disciplinary expertise required to study server operation holistically. This work provides a unified model for capturing holistic power usage within Cloud datacenter servers. Constructed through controlled laboratory experiments, the model captures the relationship of server power usage between software, hardware, and cooling agnostic of architecture and cooling type (air and liquid). An exciting prospect is the ability to quantify the amount of non-electrical power consumed through cooling, allowing for more realistic and accurate server power profiles. This work represents the first empirically supported analysis and modeling of holistic power usage for Cloud datacenter servers, and bridges a significant gap between computer science and mechanical engineering research. Model validation through experiments demonstrates an average standard error of 3% for server power usage within both air and liquid cooled environments

Quality attribute trade-offs in the embedded systems industry: An exploratory case study

The embedded systems domain has grown exponentially over the past years. The industry is forced by the market to rapidly improve and release new products to beat the competition. Frenetic development rhythms thus shape this domain and give rise to several new challenges for software design and development. One of them is dealing with trade-offs between run-time and design-time quality attributes. To study practices, processes and tools concerning the management of run-time and design-time quality attributes as well as the trade-offs among them from the perspective of embedded systems software engineers. An exploratory case study with two qualitative data collection steps, namely interviews and a focus group, involving six different companies from the embedded systems domain with a total of twenty participants. The interviewed subjects showed a preference for run-time over design-time qualities. Trade-offs between design-time and run-time qualities are very common, but they are often implicit, due to the lack of adequate monitoring tools and practices. Practitioners prefer to deal with trade-offs in the most lightweight way possible, by applying ad-hoc practices, thus avoiding any overhead incurred. Finally, practitioners have elaborated on how they envision the ideal tool support for dealing with trade-offs. Although it is notoriously difficult to deal with trade-offs, constantly monitoring the quality attributes of interest with automated tools is key in making explicit and prudent trade-offs and mitigating the risk of incurring technical debt

Developing a water-energy-GHG emissions modeling framework: Insights from an application to California's water system

[EN] Integrating processes of water and energy interdependence in water systems can improve the understanding of the tradeoffs between water and energy in management and policy. This study presents a development of an integrated water resources management model that includes water-related energy use and GHG emissions. We apply the model to a simplified representation of California's water system. Accounting for water demands from cities, agriculture, environment and the energy sector, and combining a surface water management model with a simple groundwater model, the model optimizes water use across sectors during shortages from an economic perspective, calculating the associated energy use and electricity generation for each water demand. The results of California's water system show that urban end-uses account for most GHG emissions of the entire water cycle, but large water conveyance produces significant peaks over the summer season. Different policy scenarios show the significant tradeoffs between water, energy, and GHG emissions.Escrivà Bou, À.; Lund, J.; Pulido-Velazquez, M.; Hui, R.; Medellín-Azuara, J. (2018). Developing a water-energy-GHG emissions modeling framework: Insights from an application to California's water system. Environmental Modelling & Software. 109:54-65. doi:10.1016/j.envsoft.2018.07.011S546510

The New Technologies: An Integrated view, July, 1986

This paper is an English translation, by the author herself, of a paper that until now has only been published in Spanish. The editors of this working paper series are of the opinion that the paper - although written 24 years ago - represents such an important element in the writings of Carlota Perez that it should be made available also to the English-speaking research community. The paper presents an early notion of a techno-economic paradigm and - although internet was years away from being available - it is indeed an outline of the paradigm we presently live in. Many of the issues raised here, like alternative sources of energy and biotechnology, are still with us today, and many of the predictions have proved to be based on accurate perceptions.

Getting Smart: Learning From Technology-Empowered Frontline Interactions

Smart technologies are rapidly transforming frontline employee-customer interactions. However, little academic research has tackled urgent, relevant questions regarding such technology-empowered frontline interactions. The current study conceptualizes (1) smart technology use in frontline employee-customer interactions, (2) smart technology–mediated learning mechanisms that elevate service effectiveness and efficiency performance to empower frontline interactions, and (3) stakeholder interaction goals as antecedents of smart technology–mediated learning. We propose that emerging smart technologies, which can substitute for or complement frontline employees’ (FLEs) efforts to deliver customized service over time, may help resolve the long-standing tension between service efficiency and effectiveness because they can learn or enable learning from and across customers, FLEs, and interactions. Drawing from pragmatic and deliberate learning theories, the authors conceptualize stakeholder learning mechanisms that mediate the effects of frontline interaction goals on FLEs’ and customers’ effectiveness and efficiency outcomes. This study concludes with implications for research and practice

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible