Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times

Automated builds are integral to the Continuous Integration (CI) software development practice. In CI, developers are encouraged to integrate early and often. However, long build times can be an issue when integrations are frequent. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of a job. Such models can help developers to better manage their time and tasks. Also, project managers can explore different factors to determine the best setup for a build job that will keep the build wait time to an acceptable level. Software organizations transitioning to CI practices can use the predictive models to anticipate build times before CI is implemented. The research community can modify our predictive models to further understand the factors and relationships affecting build times.Comment: 4 paged version published in the Proceedings of the IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) Pages 487-490. MSR 201

Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues

In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces

LUTI modelling in the Netherlands

This paper presents the use of the Dutch LUTI model TIGRIS in the planning process. TIGRIS is a long-term, incremental, time-based interaction and allocation model for land use, mobility, congestion and accessibility. In TIGRIS accessibility is described as a location factor for land-use that generates mobility. This increased mobility leads to congestion and changes in accessibility, and afterwards to new changes in land use. The model is used for regional and national forecasting, and follows a learning-by-doing approach. In this paper four applications of TIGRIS are described with a focus on assessing impacts and the role in the planning process. Because TIGRIS was originally developed as a sketch planning model with limited detail and the calibration on the basis of empirical data was rather limited, a new version, TIGRIS XL, is currently being developed

Policy innovation in the Italian labour market: the influence of institutions

Gegenstand der Analyse dieses Papiers ist der Umfang von Policy-Innovationen innerhalb des italienischen Arbeitsmarkts angesichts der wachsenden Arbeitslosigkeit. Dabei geht es um die Art, wie öffentliche und private Akteure ihr Routinehandeln zu verändern und neue Programme zu entwickeln versuchen, umgegen die alarmierenden Veränderungen auf dem Arbeitsmarkt anzugehen.Im Vordergrund stehen dabei die in der jüngsten Zeit geschlossenen Solidaritäts-Abmachungen (bei denen es um Verträge mit flexibler und verkürzter Arbeitszeitgeht), die sehr bewußt von Entscheidungsträgern eingeführt wurden, um Entlassungen zu vermeiden und neue Einstellungen zu ermöglichen. Ein detaillierter Vergleich zwischen den mehrheitlich praktizierten Standardansätzen in derArbeitsmarktpolitik und den neuen Arbeitsprogrammen der 90er Jahre zeigt deutlichein anhaltendes Setzen auf alte Policy-Muster und ein niedriges Innovationsniveauin neue Policy-Ansätze. Die Arbeitgeber halten an den alten Programmen fest, da sieden Verlust von deren Anreizstrukturen befürchten. Ebenso sind öffentlicheMaßnahmen pfadabhängig vor dem Hintergrund des bestehenden Institutionengefüges von Anreizen und Einschränkungen, eine Abhängigkeit, dieinnovative Erfolge verhindert. --

EOR as sequestration: Geoscience perspective

CO2 Enhanced Oil Recovery (EOR) has a development and operational history several decades longer than geologic sequestration of CO2 designed to benefit the atmosphere and provides much of the experience on which confidence in the newer technology is based. With modest increases in surveillance and accounting, future CO2 EOR using anthropogenic CO2 (CO2-A) captured to decrease atmospheric emissions can be used as part of a sequestration program.Bureau of Economic Geolog

Experiments in climate governance – lessons from a systematic review of case studies in transition research

Experimentation has been proposed as one of the ways in which public policy can drive sustainability transitions, notably by creating or delimiting space for experimenting with innovative solutions to sustainability challenges. In this paper we report on a systematic review of articles published between 2009 and 2015 that have addressed experiments aiming either at understanding decarbonisation transitions or enhancing climate resilience. Using the case survey method, we find few empirical descriptions of real-world experiments in climate and energy contexts in the scholarly literature, being observed in only 25 articles containing 29 experiments. We discuss the objectives, outputs and outcomes of these experiments noting that explicit experimenting with climate policies could be identified only in 12 cases. Based on the results we suggest a definition of climate policy experiments and a typology of experiments for sustainability transitions that can be used to better understand the role of and learn more effectively from experiments in sustainability transitions

Framework for resource efficient profiling of spatial model performance, A

2022 Summer.Includes bibliographical references.We design models to understand phenomena, make predictions, and/or inform decision-making. This study targets models that encapsulate spatially evolving phenomena. Given a model M, our objective is to identify how well the model predicts across all geospatial extents. A modeler may expect these validations to occur at varying spatial resolutions (e.g., states, counties, towns, census tracts). Assessing a model with all available ground-truth data is infeasible due to the data volumes involved. We propose a framework to assess the performance of models at scale over diverse spatial data collections. Our methodology ensures orchestration of validation workloads while reducing memory strain, alleviating contention, enabling concurrency, and ensuring high throughput. We introduce the notion of a validation budget that represents an upper-bound on the total number of observations that are used to assess the performance of models across spatial extents. The validation budget attempts to capture the distribution characteristics of observations and is informed by multiple sampling strategies. Our design allows us to decouple the validation from the underlying model-fitting libraries to interoperate with models designed using different libraries and analytical engines; our advanced research prototype currently supports Scikit-learn, PyTorch, and TensorFlow. We have conducted extensive benchmarks that demonstrate the suitability of our methodology