Short-term response of chlorophyll a concentration due to intense wind and freshwater peak episodes in estuaries: The case of fangar Bay (Ebro Delta)

Estuaries and coastal bays are areas of large spatio-temporal variability in physical and biological variables due to environmental factors such as local wind, light availability, freshwater inputs or tides. This study focuses on the effect of strong wind events and freshwater peaks on short-term chlorophyll a (Chl a) concentration distribution in the small-scale and microtidal, Fangar Bay (Ebro Delta, northwestern Mediterranean). The hydrodynamics of this bay are primarily driven by local wind episodes modulated by stratification in the water column. Results based on field-campaign observations and Sentinel-2 images revealed that intense wind episodes from both NW (offshore) and NE-E (onshore) caused an increase in the concentration of surface Chl a. The mechanisms responsible were horizontal mixing and the bottom resuspension (also linked to the breakage of the stratification) that presumably resuspended Chl a containing biomass (i.e., micropyhtobentos) and/or incorporated nutrients into the water column. On the other hand, sea-breeze was not capable of breaking up the stratification, so the chlorophyll a concentration did not change significantly during these episodes. It was concluded that the mixing produced by the strong winds favoured an accumulation of Chl a concentration, while the stratification that causes a positive estuarine circulation reduced this accumulation. However, the spatial-temporal variability of the Chl a concentration in small-scale estuaries and coastal bays is quite complex due to the many factors involved and deserve further intensive field campaigns and additional numerical modelling efforts.Postprint (published version

Shifting the digital skills discourse for the 4th industrial revolution

Copyright ©the Authors All rights reserved. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage and that copies: 1) bear this notice in full; and 2) give the full citation on the first page. It is permissible to abstract these works so long as credit is given. To copy in all other cases or to republish or to post on a server or to redistribute to lists requires specific permission and payment of a fee. Contact [email protected] to request redistribution permission.School of Computin

Short-Term Response of Chlorophyll a Concentration Due to Intense Wind and Freshwater Peak Episodes in Estuaries: The Case of Fangar Bay (Ebro Delta)

Estuaries and coastal bays are areas of large spatio-temporal variability in physical and biological variables due to environmental factors such as local wind, light availability, freshwater inputs or tides. This study focuses on the effect of strong wind events and freshwater peaks on short-term chlorophyll a (Chl a) concentration distribution in the small-scale and microtidal, Fangar Bay (Ebro Delta, northwestern Mediterranean). The hydrodynamics of this bay are primarily driven by local wind episodes modulated by stratification in the water column. Results based on field-campaign observations and Sentinel-2 images revealed that intense wind episodes from both NW (offshore) and NE-E (onshore) caused an increase in the concentration of surface Chl a. The mechanisms responsible were horizontal mixing and the bottom resuspension (also linked to the breakage of the stratification) that presumably resuspended Chl a containing biomass (i.e., micropyhtobentos) and/or incorporated nutrients into the water column. On the other hand, sea-breeze was not capable of breaking up the stratification, so the chlorophyll a concentration did not change significantly during these episodes. It was concluded that the mixing produced by the strong winds favoured an accumulation of Chl a concentration, while the stratification that causes a positive estuarine circulation reduced this accumulation. However, the spatial-temporal variability of the Chl a concentration in small-scale estuaries and coastal bays is quite complex due to the many factors involved and deserve further intensive field campaigns and additional numerical modelling efforts.info:eu-repo/semantics/publishedVersio

Literacy for digital futures : Mind, body, text

The unprecedented rate of global, technological, and societal change calls for a radical, new understanding of literacy. This book offers a nuanced framework for making sense of literacy by addressing knowledge as contextualised, embodied, multimodal, and digitally mediated. In today’s world of technological breakthroughs, social shifts, and rapid changes to the educational landscape, literacy can no longer be understood through established curriculum and static text structures. To prepare teachers, scholars, and researchers for the digital future, the book is organised around three themes – Mind and Materiality; Body and Senses; and Texts and Digital Semiotics – to shape readers’ understanding of literacy. Opening up new interdisciplinary themes, Mills, Unsworth, and Scholes confront emerging issues for next-generation digital literacy practices. The volume helps new and established researchers rethink dynamic changes in the materiality of texts and their implications for the mind and body, and features recommendations for educational and professional practice

A framework for SLA-centric service-based Utility Computing

Nicht angegebenService oriented Utility Computing paves the way towards realization of service markets, which promise metered services through negotiable Service Level Agreements (SLA). A market does not necessarily imply a simple buyer-seller relationship, rather it is the culmination point of a complex chain of stake-holders with a hierarchical integration of value along each link in the chain. In service value chains, services corresponding to different partners are aggregated in a producer-consumer manner resulting in hierarchical structures of added value. SLAs are contracts between service providers and service consumers, which ensure the expected Quality of Service (QoS) to different stakeholders at various levels in this hierarchy. \emph{This thesis addresses the challenge of realizing SLA-centric infrastructure to enable service markets for Utility Computing.} Service Level Agreements play a pivotal role throughout the life cycle of service aggregation. The activities of service selection and service negotiation followed by the hierarchical aggregation and validation of services in service value chain, require SLA as an enabling technology. \emph{This research aims at a SLA-centric framework where the requirement-driven selection of services, flexible SLA negotiation, hierarchical SLA aggregation and validation, and related issues such as privacy, trust and security have been formalized and the prototypes of the service selection model and the validation model have been implemented. } The formal model for User-driven service selection utilizes Branch and Bound and Heuristic algorithms for its implementation. The formal model is then extended for SLA negotiation of configurable services of varying granularity in order to tweak the interests of the service consumers and service providers. %and then formalizing the requirements of an enabling infrastructure for aggregation and validation of SLAs existing at multiple levels and spanning % along the corresponding service value chains. The possibility of service aggregation opens new business opportunities in the evolving landscape of IT-based Service Economy. A SLA as a unit of business relationships helps establish innovative topologies for business networks. One example is the composition of computational services to construct services of bigger granularity thus giving room to business models based on service aggregation, Composite Service Provision and Reselling. This research introduces and formalizes the notions of SLA Choreography and hierarchical SLA aggregation in connection with the underlying service choreography to realize SLA-centric service value chains and business networks. The SLA Choreography and aggregation poses new challenges regarding its description, management, maintenance, validation, trust, privacy and security. The aggregation and validation models for SLA Choreography introduce concepts such as: SLA Views to protect the privacy of stakeholders; a hybrid trust model to foster business among unknown partners; and a PKI security mechanism coupled with rule based validation system to enable distributed queries across heterogeneous boundaries. A distributed rule based hierarchical SLA validation system is designed to demonstrate the practical significance of these notions

Towards An Efficient Cloud Computing System: Data Management, Resource Allocation and Job Scheduling

Cloud computing is an emerging technology in distributed computing, and it has proved to be an effective infrastructure to provide services to users. Cloud is developing day by day and faces many challenges. One of challenges is to build cost-effective data management system that can ensure high data availability while maintaining consistency. Another challenge in cloud is efficient resource allocation which ensures high resource utilization and high SLO availability. Scheduling, referring to a set of policies to control the order of the work to be performed by a computer system, for high throughput is another challenge. In this dissertation, we study how to manage data and improve data availability while reducing cost (i.e., consistency maintenance cost and storage cost); how to efficiently manage the resource for processing jobs and increase the resource utilization with high SLO availability; how to design an efficient scheduling algorithm which provides high throughput, low overhead while satisfying the demands on completion time of jobs. Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of replicas for each data object, which neglects diverse data popularities and cannot utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability hence minimize SLA (Service Level Agreement) violations while minimize cost caused by replication in order to maximize the revenue. In this dissertation, we build a nonlinear programming model to maximize data availability in both types of failures and minimize the cost caused by replication. Based on the model\u27s solution for the replication degree of each data object, we propose a low-cost multi-failure resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. In current cloud, providers still need to reserve resources to allow users to scale on demand. The capacity offered by cloud offerings is in the form of pre-defined virtual machine (VM) configurations. This incurs resource wastage and results in low resource utilization when the users actually consume much less resource than the VM capacity. Existing works either reallocate the unused resources with no Service Level Objectives (SLOs) for availability\footnote{Availability refers to the probability of an allocated resource being remain operational and accessible during the validity of the contract~\cite{CarvalhoCirne14}.} or consider SLOs to reallocate the unused resources for long-running service jobs. This approach increases the allocated resource whenever it detects that SLO is violated in order to achieve SLO in the long term, neglecting the frequent fluctuations of jobs\u27 resource requirements in real-time application especially for short-term jobs that require fast responses and decision making for resource allocation. Thus, this approach cannot fully utilize the resources to process data because they cannot quickly adjust the resource allocation strategy dealing with the fluctuations of jobs\u27 resource requirements. What\u27s more, the previous opportunistic based resource allocation approach aims at providing long-term availability SLOs with good QoS for long-running jobs, which ensures that the jobs can be finished within weeks or months by providing slighted degraded resources with moderate availability guarantees, but it ignores deadline constraints in defining Quality of Service (QoS) for short-lived jobs requiring online responses in real-time application, thus it cannot truly guarantee the QoS and long-term availability SLOs. To overcome the drawbacks of previous works, we adequately consider the fluctuations of unused resource caused by bursts of jobs\u27 resource demands, and present a cooperative opportunistic resource provisioning (CORP) scheme to dynamically allocate the resource to jobs. CORP leverages complementarity of jobs\u27 requirements on different resource types and utilizes the job packing to reduce the resource wastage and increase the resource utilization. An increasing number of large-scale data analytics frameworks move towards larger degrees of parallelism aiming at high throughput. Scheduling that assigns tasks to workers and preemption that suspends low-priority tasks and runs high-priority tasks are two important functions in such frameworks. There are many existing works on scheduling and preemption in literature to provide high throughput. However, previous works do not substantially consider dependency in increasing throughput in scheduling or preemption. Considering dependency is crucial to increase the overall throughput. Besides, extensive task evictions for preemption increase context switches, which may decrease the throughput. To address the above problems, we propose an efficient scheduling system Dependency-aware Scheduling and Preemption (DSP) to achieve high throughput in scheduling and preemption. First, we build a mathematical model to minimize the makespan with the consideration of task dependency, and derive the target workers for tasks which can minimize the makespan; second, we utilize task dependency information to determine tasks\u27 priorities for preemption; finally, we present a probabilistic based preemption to reduce the numerous preemptions, while satisfying the demands on completion time of jobs. We conduct trace driven simulations on a real-cluster and real-world experiments on Amazon S3/EC2 to demonstrate the efficiency and effectiveness of our proposed system in comparison with other systems. The experimental results show the superior performance of our proposed system. In the future, we will further consider data update frequency to reduce consistency maintenance cost, and we will consider the effects of node joining and node leaving. Also we will consider energy consumption of machines and design an optimal replication scheme to improve data availability while saving power. For resource allocation, we will consider using the greedy approach for deep learning to reduce the computation overhead caused by the deep neural network. Also, we will additionally consider the heterogeneity of jobs (i.e., short jobs and long jobs), and use a hybrid resource allocation strategy to provide SLO availability customization for different job types while increasing the resource utilization. For scheduling, we will aim to handle scheduling tasks with partial dependency, worker failures in scheduling and make our DSP fully distributed to increase its scalability. Finally, we plan to use different workloads and real-world experiment to fully test the performance of our methods and make our preliminary system design more mature