QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope

Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions. We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create pre-warmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set of analytics and interactive multi-stage serverless workloads, Aquatope significantly outperforms prior systems, reducing QoS violations by 5x, and cost by 34% on average and up to 52% compared to other QoS-meeting methods

On risk management of shipping system in ice-covered waters : Review, analysis and toolbox based on an eight-year polar project

Publisher Copyright: © 2022 The AuthorsWith the climate change, polar sea ice is diminishing. This, on one hand, enables the possibility for e.g., Arctic shipping and relevant resource exploitation activities, but on the other hand brings additional risks induced by these activities. Increasing research focuses have been observed on the relevant topics in the complex and harsh polar environment and its fragile ecosystem. However, from risk management perspective, there is still a lack of holistic analysis and understanding towards safe shipping in the ice-covered waters and its available models applicable for managing risks in the system. Therefore, this paper aims to establish a framework and analysis for better understanding of this gap. The paper targets a comprehensive and long-term project specifically focusing on holistic safe shipping in ice-covered waters as the analysis basis. It firstly creates a holistic framework for the shipping system in ice-covered waters and then implements review and analysis of project publications on their overall features. Quantitative prediction models are selected for a structured applicability analysis. Furthermore, an extensive review outside the project following the elements established for the holistic shipping system is conducted so that this paper provides an overview of models for the shipping system in ice-covered waters, addressing the status of the current toolbox. Moreover, it helps to identify the next scientific steps on risk management of shipping in ice-covered waters.Peer reviewe

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Innovations towards Climate-Induced Disaster Risk Assessment and Response

A changing climate may portend increasing disaster risk across many countries and business enterprises. While many aspects of the hazards, exposure and vulnerability that constitute disaster risk have been well studied, several challenges remain. A critical aspect that needs to be addressed is the rapid response and recovery from a climate-induced disaster. Often, governments need to allocate funds or design financial instruments that can be activated rapidly to mobilize response and recovery. The proposed research addresses this general problem, focusing on a few selected issues. First, there is the question of how to rapidly detect and index a climate hazard, such as a flood, given proxy remote sensing data on attributes that may be closely related to the hazard. The second is the need to robustly estimate the return periods of extreme climate hazards, and the temporal changes in their projected frequency of occurrence using multi-century climate proxies. The third is the need to assess the potential losses from the event, including the disruption of services, and cascading failure of interlinked infrastructure elements. The fourth is the impact on global and regional supply chains that are induced by the event, and the associated financial impact. For each of these cases, it is useful to ground an analysis and the development of an approach around real world examples, which can then collectively inform a strategy for emergency response. Here, this will be pursued through an analysis of flooding in the Philippines, livestock mortality induced by drought and freezing winter in Mongolia, Hurricane Sandy impacts in New York, supply chain impacts in Thailand, and an end to end analysis of the potential process using data from Thailand and Bangladesh. Collectively, these analyses are expected to inform climate hazard planning and securitization processes with broad applicability at a regional to national level

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Innovations towards Climate-Induced Disaster Risk Assessment and Response

A changing climate may portend increasing disaster risk across many countries and business enterprises. While many aspects of the hazards, exposure and vulnerability that constitute disaster risk have been well studied, several challenges remain. A critical aspect that needs to be addressed is the rapid response and recovery from a climate-induced disaster. Often, governments need to allocate funds or design financial instruments that can be activated rapidly to mobilize response and recovery. The proposed research addresses this general problem, focusing on a few selected issues. First, there is the question of how to rapidly detect and index a climate hazard, such as a flood, given proxy remote sensing data on attributes that may be closely related to the hazard. The second is the need to robustly estimate the return periods of extreme climate hazards, and the temporal changes in their projected frequency of occurrence using multi-century climate proxies. The third is the need to assess the potential losses from the event, including the disruption of services, and cascading failure of interlinked infrastructure elements. The fourth is the impact on global and regional supply chains that are induced by the event, and the associated financial impact. For each of these cases, it is useful to ground an analysis and the development of an approach around real world examples, which can then collectively inform a strategy for emergency response. Here, this will be pursued through an analysis of flooding in the Philippines, livestock mortality induced by drought and freezing winter in Mongolia, Hurricane Sandy impacts in New York, supply chain impacts in Thailand, and an end to end analysis of the potential process using data from Thailand and Bangladesh. Collectively, these analyses are expected to inform climate hazard planning and securitization processes with broad applicability at a regional to national level

Resilience, Reliability, and Recoverability (3Rs)

Recent natural and human-made disasters, mortgage derivatives crises, and the need for stable systems in different areas have renewed interest in the concept of resilience, especially as it relates to complex industrial systems with mechanical failures. This concept in the engineering systems (infrastructure) domain could be interpreted as the probability that system conditions exceed an irrevocable tipping point. But the probability in this subject covers the different areas that different approaches and indicators can evaluate. In this context, reliability engineering is used the reliability (uptime) and recoverability (downtime) indicators (or performance indicators) as the most useful probabilistic tools for performance measurement. Therefore, our research penalty area is the resilience concept in combination with reliability and recoverability. It must be said that the resilience evaluators must be considering a diversity of knowledge sources. In this thesis, the literature review points to several important implications for understanding and applying resilience in the engineering area and The Arctic condition. Indeed, we try to understand the application and interaction of different performance-based resilience concepts. In this way, a collection of the most popular performance-based resilience analysis methods with an engineering perspective is added as a state-of-the-art review. The performance indicators studies reveal that operational conditions significantly affect the components, industry activities, and infrastructures performance in various ways. These influential factors (or heterogeneity) can broadly be studied into two groups: observable and unobservable risk factors in probability analysis of system performance. The covariate-based models (regression), such as proportional hazard models (PHM), and their extent are the most popular methods for quantifying observable and unobservable risk factors. The report is organized as follows: After a brief introduction of resilience, chapters 2,3 priorly provide a comprehensive statistical overview of the reliability and recoverability domain research by using large scientific databases such as Scopus and Web of Science. As the first subsection, a detailed review of publications in the reliability and recoverability assessment of the engineering systems in recent years (since 2015) is provided. The second subsection of these chapters focuses on research done in the Arctic region. The last subsection presents covariate-based reliability and recoverability models. Finally, in chapter 4, the first part presents the concept and definitions of resilience. The literature reviews four main perspectives: resilience in engineering systems, resilience in the Arctic area, the integration of “Resilience, Reliability, and Recoverability (3Rs)”, and performance-based resilience models