Evaluation of safety, efficacy and pharmacokinetics of Eltrombopag in patients with chronic immune thrombocytopenia: Meta-analysis of randomized controlled trials

The present meta-analysis is to evaluate the safety and efficiency of Eltrombopag in the prevention and therapy of Immune thrombocytopeni

Coordinating Vertical Elasticity of both Containers and Virtual Machines

International audienceElasticity is a key feature in cloud computing as it enables the automatic and timely provisioning and depro- visioning of computing resources. To achieve elasticity, clouds rely on virtualization techniques including Virtual Machines (VMs) and containers. While many studies address the vertical elasticity of VMs and other few works handle vertical elasticity of containers, no work manages the coordination between these two ver- tical elasticities. In this paper, we present the first approach to coordinate vertical elasticity of both VMs and containers. We propose an auto-scaling technique that allows containerized applications to adjust their resources at both container and VM levels. This work has been evaluated and validated using the RUBiS benchmark application. The results show that our approach reacts quickly and improves application perfor- mance. Our coordinated elastic controller outperforms container vertical elasticity controller by 18.34% and VM vertical elasticity controller by 70%. It also outperforms container horizontal elasticity by 39.6%

Teaching pharmacovigilance to undergraduate students: Our experience in poor-resource setting

Using medicines associated with adverse drug reactions (ADRs) might cause serious health complications. The pharmacist plays a unique role in monitoring ADRs, either by themselves or with the assistance of other health-care professionals, to diminish the hazards of ADRs by distinguishing, reporting, and evaluating any proposed ADRs. To train future pharmacists who have adequate knowledge of ADRs and related aspects, it is highly recommended to introduce the WHO-ISoP pharmacovigilance (PV) in the core curriculum. In this article, we shared the suggested curriculum in Aden University. It is based on comprehensive outlines and reference books that offer a broad view of all aspects related to PV. A brief student course evaluation was carried out. Fifty students participated in the survey. Students expressed the importance of the course and indicated that they wanted to know more about the types of ADRs and common medication errors. Some of them lacked an understanding of the causal relationship between ADRs and risk assessment and not familiar with the reporting forms. They suggested for PV awareness programs for health-care staff and public. The curriculum should be tailored according to the country's needs because each country has its own medication safety issues and PV program. To reach the ultimate objective, this article reports the initiative to develop PV proficiencies in a university setting

Model-Driven Management of Docker Containers

International audienceWith the emergence of Docker, it becomes easier to encapsulate applications and their dependencies into lightweight Linux containers and make them available to the world by deploying them in the cloud. Compared to hypervisor-based virtualization approaches, the use of containers provides faster start-ups times and reduces the consumption of computer resources. However, Docker lacks of deployability verification tool for containers at design time. Currently, the only way to be sure that the designed containers will execute well is to test them in a running system. If errors occur, a correction is made but this operation can be repeated several times before the deployment becomes operational. Docker does not provide a solution to increase or decrease the size of container resources in demand. Besides the deployment of containers, Docker lacks of synchronization between the designed containers and those deployed. Moreover, container management with Docker is done at low level, and therefore requires users to focus on low level system issues. In this paper we focus on these issues related to the management of Docker containers. In particular, we propose an approach for modeling Docker containers. We provide tooling to ensure the deployability and the management of Docker containers. We illustrate our proposal using an event processing application and show how our solution provides a significantly better compromise between performance and development costs than the basic Docker container solution

Resource management for cloud functions with memory tracing, profiling and autotuning

Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base

Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments

Un cadre flexible pour l’élasticité dans les nuages

Recently, cloud computing has been gaining more popularity and has received a great deal of attention from both industrial and academic worlds. Industries and application providers have moved or plan to move to clouds in order to focus on their core business. This frees them from the burden and cost of managing their physical servers in local data center infrastructures. However, the main factor motivating the use of cloud computing is its ability to provide resources according to the customer’s needs or what is referred to as elastic provisioning and de-provisioning. Therefore, elasticity is one of the key features in cloud computing that dynamically adjusts the amount of allocated resources to meet changes in workload demands.The workload of cloud applications usually varies drastically over time and hence maintaining sufficient resources to meet peak requirements can be costly, and will increase the application provider’s functional cost. Conversely, if providers cut the costs by maintaining only minimum computing resources, there will not be sufficient resources to meet peak requirements and cause bad performance, violating Service Level Agreement (SLA). Therefore, adapting cloud applications during their execution according to demand variation is a challenging task. In addition, cloud elasticity is diverse and heterogeneous because it encompasses different approaches, policies, purposes, and applications. Furthermore, elasticity can be applied at the infrastructure level or application level. The infrastructure is powered by a certain virtualization technology such as VMware, Xen, containers or a provider-specific virtualization platform. We are interested in investigating: How to overcome the problem of over-provisioning and under-provisioning? How to guaranty resource availability? How to overcome the problems of heterogeneity and resource granularity? How to standardize, unify elasticity solutions and model its diversity at a high level of abstraction to manage its different aspects?In this thesis, we solved such challenges and we investigated all the aspects of elasticity to manage efficiently the resources provisioning and de-provisioning in cloud computing.It extended the state-of-the-art by making the following three contributions. Firstly, an up-to-date state-of-the-art of the cloud elasticity which reviews different works related to elasticity for both Virtual Machines (VMs) and containers. Secondly, ElasticDocker, an approach to manage container elasticity including vertical elasticity, live migration, and elasticity combination between different virtualization techniques. Thirdly, Model-Driven Elasticity Management with OCCI (MoDEMO), a new unified, standard-based, model-driven, highly extensible, highly reconfigurable elasticity management framework that supports multiple elasticity policies, both vertical and horizontal elasticities, different virtualization techniques and multiple cloud providers.Récemment, le cloud computing a gagné beaucoup de popularité et a reçu beaucoup d'attention des deux mondes, industriel et académique, puisque cela les libère de la charge et le coût de la gestion de centres de données locaux. Toutefois, le principal facteur motivant l'utilisation du cloud est sa capacité à fournir des ressources en fonction des besoins du client. Ce concept est appelé l’élasticité. Adapter les applications cloud lors de leur exécution en fonction des variations de la demande est un grand défi. En outre, l'élasticité de cloud est diverse et hétérogène car elle englobe différentes approches, stratégies, objectifs, etc. Nous sommes intéressés à étudier: Comment résoudre le problème de sur/sous-approvisionnement? Comment garantir la disponibilité des ressources? Comment surmonter les problèmes d'hétérogénéité et de granularité des ressources? Comment standardiser, unifier les solutions d'élasticité et de modéliser sa diversité à un haut niveau d'abstraction? Dans cette thèse, trois majeures contributions ont été proposées: Tout d’abord, un état de l’art à jour de l’élasticité du cloud; cet état de l’art passe en revue les différents travaux relatifs à l’élasticité des machines virtuelles et des conteneurs. Deuxièmement, ElasticDocker, une approche permettant de gérer l’élasticité des conteneurs, notamment l’élasticité verticale, la migration et l’élasticité combinée. Troisièmement, MoDEMO, un nouveau cadre de gestion d'élasticité unifié, basé sur un standard, dirigé par les modèles, hautement extensible et reconfigurable, supportant plusieurs stratégies, différents types d’élasticité, différentes techniques de virtualisation et plusieurs fournisseurs de cloud

Un cadre flexible pour l’élasticité dans les nuages

Recently, cloud computing has been gaining more popularity and has received a great deal of attention from both industrial and academic worlds. Industries and application providers have moved or plan to move to clouds in order to focus on their core business. This frees them from the burden and cost of managing their physical servers in local data center infrastructures. However, the main factor motivating the use of cloud computing is its ability to provide resources according to the customer’s needs or what is referred to as elastic provisioning and de-provisioning. Therefore, elasticity is one of the key features in cloud computing that dynamically adjusts the amount of allocated resources to meet changes in workload demands.The workload of cloud applications usually varies drastically over time and hence maintaining sufficient resources to meet peak requirements can be costly, and will increase the application provider’s functional cost. Conversely, if providers cut the costs by maintaining only minimum computing resources, there will not be sufficient resources to meet peak requirements and cause bad performance, violating Service Level Agreement (SLA). Therefore, adapting cloud applications during their execution according to demand variation is a challenging task. In addition, cloud elasticity is diverse and heterogeneous because it encompasses different approaches, policies, purposes, and applications. Furthermore, elasticity can be applied at the infrastructure level or application level. The infrastructure is powered by a certain virtualization technology such as VMware, Xen, containers or a provider-specific virtualization platform. We are interested in investigating: How to overcome the problem of over-provisioning and under-provisioning? How to guaranty resource availability? How to overcome the problems of heterogeneity and resource granularity? How to standardize, unify elasticity solutions and model its diversity at a high level of abstraction to manage its different aspects?In this thesis, we solved such challenges and we investigated all the aspects of elasticity to manage efficiently the resources provisioning and de-provisioning in cloud computing.It extended the state-of-the-art by making the following three contributions. Firstly, an up-to-date state-of-the-art of the cloud elasticity which reviews different works related to elasticity for both Virtual Machines (VMs) and containers. Secondly, ElasticDocker, an approach to manage container elasticity including vertical elasticity, live migration, and elasticity combination between different virtualization techniques. Thirdly, Model-Driven Elasticity Management with OCCI (MoDEMO), a new unified, standard-based, model-driven, highly extensible, highly reconfigurable elasticity management framework that supports multiple elasticity policies, both vertical and horizontal elasticities, different virtualization techniques and multiple cloud providers.Récemment, le cloud computing a gagné beaucoup de popularité et a reçu beaucoup d'attention des deux mondes, industriel et académique, puisque cela les libère de la charge et le coût de la gestion de centres de données locaux. Toutefois, le principal facteur motivant l'utilisation du cloud est sa capacité à fournir des ressources en fonction des besoins du client. Ce concept est appelé l’élasticité. Adapter les applications cloud lors de leur exécution en fonction des variations de la demande est un grand défi. En outre, l'élasticité de cloud est diverse et hétérogène car elle englobe différentes approches, stratégies, objectifs, etc. Nous sommes intéressés à étudier: Comment résoudre le problème de sur/sous-approvisionnement? Comment garantir la disponibilité des ressources? Comment surmonter les problèmes d'hétérogénéité et de granularité des ressources? Comment standardiser, unifier les solutions d'élasticité et de modéliser sa diversité à un haut niveau d'abstraction? Dans cette thèse, trois majeures contributions ont été proposées: Tout d’abord, un état de l’art à jour de l’élasticité du cloud; cet état de l’art passe en revue les différents travaux relatifs à l’élasticité des machines virtuelles et des conteneurs. Deuxièmement, ElasticDocker, une approche permettant de gérer l’élasticité des conteneurs, notamment l’élasticité verticale, la migration et l’élasticité combinée. Troisièmement, MoDEMO, un nouveau cadre de gestion d'élasticité unifié, basé sur un standard, dirigé par les modèles, hautement extensible et reconfigurable, supportant plusieurs stratégies, différents types d’élasticité, différentes techniques de virtualisation et plusieurs fournisseurs de cloud

Towards Model-Driven Multi-Cloud Resource Management

Multi-Cloud computing has established itself as a paradigm of choice for acquiring resources from different providers and get the best of each of them to run their applications. However, managing resources in Multi-Cloud remains a challenging task. Several multi-cloud libraries based approaches include Apache Libcloud, Apache jclouds, δ-cloud, Daseincloud, fog, and pkgcloud exist in the cloud market. But these are still low level as Multi-Cloud management tasks must always be programmed. Therefore, application platforms are need to help developers to succeed. In this paper we give a model-driven approach for managing resources of multiple clouds at high level. We illustrate our proposal by providing a prototype of Multi-Cloud Designer for managing resource from multiple clouds

Autonomic Vertical Elasticity of Docker Containers with ElasticDocker

International audienceElasticity is the key feature of cloud computing to scale computing resources according to application workloads timely. In the literature as well as in industrial products, much attention was given to the elasticity of virtual machines, but much less to the elasticity of containers. However, containers are the new trend for packaging and deploying microservices-based applications. Moreover, most of approaches focus on horizontal elasticity, fewer works address vertical elasticity. In this paper, we propose ElasticDocker, the first system powering vertical elasticity of Docker containers autonomously. Based on the well-known IBM's autonomic computing MAPE-K principles, ElasticDocker scales up and down both CPU and memory assigned to each container according to the application workload. As vertical elasticity is limited to the host machine capacity, ElasticDocker does container live migration when there is no enough resources on the hosting machine. Our experiments show that ElasticDocker helps to reduce expenses for container customers, make better resource utilization for container providers, and improve Quality of Experience for application end-users. In addition, based on the observed migration performance metrics, the experiments reveal a high efficient live migration technique. As compared to horizontal elasticity, ElasticDocker outperforms Kubernetes elasticity by 37.63%