Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.

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Lagos, Nigeria is rapidly urbanising and is one of the fastest growing cities in the world, with a population increasing at almost 500,000 people per year. Yet the impacts on Lagos’s local climate via its urban heat island (UHI) have not been well explored. Considering the tropics already have year-round high temperatures and humidity, small changes are very likely to tip these regions over heat-health thresholds. Using a well-established model, but with an extended investigation of uncertainty, we explore the impact of Lagos’s recent urbanisation on its UHI. Following a multi-physics evaluation, our simulations, against the background of an unusually warm period in February 2016 (where temperatures regularly exceeded 36°C), show a 0.44°C ensemble-time-mean increase in night-time UHI intensity between 1984–2016. The true scale of the impact is seen spatially where the area in which ensemble-time-mean UHIs exceeding 1°C were found to increase steeply from 254 km2 in 1984 to 1572 km2 in 2016. The rate of warming within Lagos will undoubtedly have a high impact due to the size of the population (12+ million) already at risk from excess heat. Significant warming and modifications to atmospheric boundary-layer heights are also found in rural areas downwind, directly caused by the city. However, there is limited long-term climate monitoring in Lagos or many similarly expanding cities, particularly in the tropics. As such, our modelling can only be an indication of this impact of urbanisation, and we highlight the urgent need to deploy instrumentation.

The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.

Background —The proliferation of cloud services has opened a space for cloud brokerage services. Brokers intermediate between cloud customers and providers to assist the customer in selecting the most suitable service, helping to manage the dimensionality, heterogeneity, and uncertainty associated with cloud services. Objective —Unlike other surveys, this survey focuses on the customer perspective. The survey systematically analyses the literature to identify and classify approaches to realise cloud brokerage, presenting an understanding of the state-of-the-art and a novel taxonomy to characterise cloud brokers. Method —A systematic literature survey was conducted to compile studies related to cloud brokerage and explore how cloud brokers are engineered. These studies are then analysed from multiple perspectives, such as motivation, functionality, engineering approach, and evaluation methodology. Results —The survey resulted in a knowledge base of current proposals for realising cloud brokers. The survey identified differences between the studies’ implementations, with engineering efforts directed at combinations of market-based solutions, middlewares, toolkits, algorithms, semantic frameworks, and conceptual frameworks. Conclusion —Our comprehensive meta-analysis shows that cloud brokerage is still a formative field. Although significant progress has been achieved in this field, considerable challenges remain to be addressed, which are also identified in this survey.