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The EU Atlantic Action Plan (AAP) has recently been updated and revised to support ‘blue growth’ along Europe’s western coastal regions. The revisions reflect recent challenges facing the Atlantic Arc maritime economies including the Covid-19 crisis, Brexit and the new requirements of the European Green Deal. This new revision, termed AAP 2.0, also addresses some of the weaknesses highlighted in the original Atlantic Action Plan particularly regarding identifying indicators that may be used to measure progress in the achievement of the Plan’s objectives. Using a database with comparable marine socio-economic data across the Atlantic regions, a number of indicators are identified that may be used to monitor progress of the AAP 2.0. Recent trends and spatial distributions across the Atlantic Arc region are shown for these indicators. The challenges in measuring progress are also highlighted, including where some AAP objectives and associated indicators may conflict with other EU policy aims and where the current monitoring framework can be bolstered with the inclusion of new indicators.

The EU Atlantic Action Plan (AAP) has recently been updated and revised to support ‘blue growth’ along Europe’s western coastal regions. The revisions reflect recent challenges facing the Atlantic Arc maritime economies including the Covid-19 crisis, Brexit and the new requirements of the European Green Deal. This new revision, termed AAP 2.0, also addresses some of the weaknesses highlighted in the original Atlantic Action Plan particularly regarding identifying indicators that may be used to measure progress in the achievement of the Plan’s objectives. Using a database with comparable marine socio-economic data across the Atlantic regions, a number of indicators are identified that may be used to monitor progress of the AAP 2.0. Recent trends and spatial distributions across the Atlantic Arc region are shown for these indicators. The challenges in measuring progress are also highlighted, including where some AAP objectives and associated indicators may conflict with other EU policy aims and where the current monitoring framework can be bolstered with the inclusion of new indicators.

In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e.g., training with sub-sampled datasets) in order to efficiently identify promising configurations to be then tested via high-fidelity observations (e.g., using the full dataset). Among these, HyperBand is arguably one of the most popular solutions, due to its efficiency and theoretically provable robustness. In this work, we introduce HyperJump, a new approach that builds on HyperBand's robust search strategy and complements it with novel model-based risk analysis techniques that accelerate the search by skipping the evaluation of low risk configurations, i.e., configurations that are likely to be eventually discarded by HyperBand. We evaluate HyperJump on a suite of hyper-parameter optimization problems and show that it provides over one-order of magnitude speed-ups, both in sequential and parallel deployments, on a variety of deep-learning, kernel-based learning, and neural architectural search problems when compared to HyperBand and to several state-of-the-art optimizers.

This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process while keeping into account user-specified constraints. TrimTuner jointly optimizes the cloud and application-specific parameters and, unlike state of the art works for cloud optimization, eschews the need to train the model with the full training set every time a new configuration is sampled. Indeed, by leveraging sub-sampling techniques and data-sets that are up to 60x smaller than the original one, we show that TrimTuner can reduce the cost of the optimization process by up to 50x. Further, TrimTuner speeds-up the recommendation process by 65x with respect to state of the art techniques for hyper-parameter optimization that use sub-sampling techniques. The reasons for this improvement are twofold: i) a novel domain specific heuristic that reduces the number of configurations for which the acquisition function has to be evaluated; ii) the adoption of an ensemble of decision trees that enables boosting the speed of the recommendation process by one additional order of magnitude., Comment: Mascots 2020

Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost efficiency, it is crucial to identify a deployment configuration that satisfies user-defined QoS constraints (e.g., on execution time), while avoiding unnecessary over-provisioning. This paper introduces Lynceus, a new approach for the optimization of cloud based data analytic jobs that improves overstate-of-the-art approaches by enabling significant cost savings both in terms of the final recommended configuration and of the optimization process used to recommend configurations. Unlike existing solutions, Lynceus optimizes in a joint fashion both the cloud-related and the application-level parameters. This allows for a reduction of the cost of recommended configurations by up to 3.7x at the 90-th percentile with respect to existing approaches, which treat the optimization of cloud-related and application-level parameters as two independent problems. Further, Lynceus reduces the cost of the optimization process (i.e., the cloud cost incurred for testing configurations) by up to 11x. Such an improvement is achieved thanks to two mechanisms: i) a timeout approach which allows to abort the exploration of configurations that are deemed suboptimal, while still extracting useful information to guide future explorations and to improve its predictive model - differently from recent works, which either incur the full cost for testing suboptimal configurations or are unable to extract any knowledge from aborted runs; ii) a long-sighted and budget-aware technique that determines which configurations to test by predicting the long-term impact of each exploration - unlike state-of-the-art approaches for the optimization of cloud jobs, which adopt greedy optimization methods., Comment: This updated version features a novel extension of our approach: the time out mechanism. Additionally, we improved the write-up of the paper, fruit of the collaboration with professor David Garlan and Carnegie Mellon Univeristy