Abstract-Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to * Corresponding

Summary Large‐scale scientific problems are often modeled as workflows. The ever‐growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay‐per‐use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.

Aims:  To establish the modes of action of the antagonistic fungal strain Clonostachys rosea BAFC3874 isolated from suppressive soils against Sclerotinia sclerotiorum and to determine its potential as a biocontrol agent. Methods and Results:  The antagonistic activity of C. rosea BAFC3874 was determined in vitro by dual cultures. The strain effectively antagonized S. sclerotiorum in pot‐grown lettuce and soybean plants. Antifungal activity assays of C. rosea BAFC3874 grown in culture established that the strain produced antifungal compounds against S. sclerotiorum associated with secondary metabolism. High mycelial growth inhibition coincided with sclerotia production inhibition. The C. rosea strain produced a microheterogeneous mixture of peptides belonging to the peptaibiotic family. Moreover, mycoparasitism activity was observed in the dual culture. Conclusions:  Clonostachys rosea strain BAFC3874 was proved to be an effective antagonist against the aggressive soil‐borne pathogen S. sclerotiorum in greenhouse experiments. The main mechanisms involve peptaibiotic metabolite production and mycoparasitism activity. Significance and Impact of the Study:  Clonostachys rosea BAFC3874 may be a good fungal biological control agent against S. sclerotiorum. In addition, we were also able to isolate and identify peptaibols, an unusual family of compounds in this genus of fungi.

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.Container orchestration systems enable the deployment of containerized applications on a shared cluster. They enable their execution and monitoring by transparently managing tasks and data deployed on a set of distributed resources. A reference architecture is shown in Figure 1; the components shown are common to most container orchestration systems; however, not all of them have to be implemented to have a fully functional system. Four main entities or layers are identified in the presented architecture, namely, one or more Jobs, a Cluster Manager Master, a Compute Cluster,

A multi-physics and multi-scale computational approach is proposed in the present work to study the evolution of microcracking in polycrystalline Silicon (Si) solar cells composing photovoltaic (PV) modules. Coupling between the elastic and the electric fields is provided according to an equivalent circuit model for the PV module where the electrically inactive area is determined from the analysis of the microcrack pattern. The structural scale of the PV laminate (the macro-model) is coupled to the scale of the polycrystals (the micro-model) using a multiscale nonlinear finite element approach where the macro-scale displacements of the Si cell borders are used as boundary conditions for the micro-model. Intergranular cracking in the Si cell is simulated using a nonlinear fracture mechanics cohesive zone model (CZM). A case-study shows the potentiality of the method, in particular as regards the analysis of the microcrack orientation and distribution, as well as of the effect of cracking on the electric characteristics of the PV module.

Aims:  To study phosphate solubilization in Penicillium purpurogenum as function of medium pH, and carbon and nitrogen concentrations. Methods and Results:  Tricalcium phosphate (CP) solubilization efficiency of P. purpurogenum was evaluated at acid or alkaline pH using different C and N sources. Glucose‐ and (NH4)2SO4‐based media showed the highest P solubilization values followed by fructose. P. purpurogenum solubilizing ability was higher in cultures grown at pH 6·5 than cultures at pH 8·5. Organic acids were detected in both alkaline and neutral media, but the relative percentages of each organic acid differed. Highest P release coincided with the highest organic acids production peak, especially gluconic acid. When P. purpurogenum grew in alkaline media, the nature and concentration of organic acids changed at different N and C concentrations. A factorial categorical experimental design showed that the highest P‐solubilizing activity, coinciding with the highest organic acid production, corresponded to the highest C concentration and lowest N concentration. Conclusions:  The results described in the present study show that medium pH and carbon and nitrogen concentrations modulate the P solubilization efficiency of P. purpurogenum through the production of organic acids and particularly that of gluconic acid. In the P solubilization optimization studies, glucose and (NH4)2SO4 as C and N sources allowed a higher solubilization efficiency at high pH. Significance and Impact of the Study:  This organism is a potentially proficient soil inoculant, especially in P‐poor alkaline soils where other P solubilizers fail to release soluble P. Further work is necessary to elucidate whether these results can be extrapolated to natural soil ecosystems, where different pH values are present. Penicillium purpurogenum could be used to develop a bioprocess for the manufacture of phosphatic fertilizer with phosphate calcium minerals.

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.