Cooperative resource management in the cloud

L’évolution des infrastructures informatiques encourage la gestion séparée de l’infrastructure matérielle et de celle des logiciels. Dans cette direction, les infrastructures de cloud virtualisées sont devenues trés populaires. Parmi les différents modèles de cloud, les Infrastructures as a Service (IaaS) ont de nombreux avantages pour le fournisseur comme pour le client. Dans ce modèle de cloud, le fournisseur fournit ses ressources virtualisées et il est responsable de la gestion de son infrastructure. De son coté, le client gère son application qui est déployée dans les machines virtuelles allouées. Ces deux acteurs s’appuient généralement sur des systèmes d’administration autonomes pour automatiser les tâches d’administration. Réduire la quantité de ressources utilisées (et la consommation d’énergie) est un des principaux objectifs de ce modèle de cloud. Cette réduction peut être obtenue à l’exécution au niveau de l’application par le client (en redimensionnant l’application) ou au niveau du système virtualisé par le fournisseur (en regroupant les machines virtuelles dans l’infrastructure matérielle en fonction de leur charge). Dans les infrastructures de cloud traditionnelles, les politiques de gestion de ressources ne sont pas coopératives : le fournisseur ne possède pas d’informations détaillées sur les applications. Ce manque de coordination engendre des surcoûts et des gaspillages de ressources qui peuvent être réduits avec une politique de gestion de ressources coopérative. Dans cette thèse, nous traitons du problème de la gestion de ressources séparée dans un environnement de cloud virtualisé. Nous proposons un modèle de machines virtuelles élastiques avec une politique de gestion coopérative des ressources. Cette politique associe la connaissance des deux acteurs du cloud afin de réduire les coûts et la consommation d’énergie. Nous évaluons les bénéfices de cette approche avec plusieurs expériences dans un IaaS privé. Cette évaluation montre que notre politique est meilleure que la gestion des ressources non coordonnée dans un IaaS traditionnel, car son impact sur les performances est faible et elle permet une meilleure utilisation des ressources matérielles et logicielles. ABSTRACT : Recent advances in computer infrastructures encourage the separation of hardware and software management tasks. Following this direction, virtualized cloud infrastructures are becoming very popular. Among various cloud models, Infrastructure as a Service (IaaS) provides many advantages to both provider and customer. In this service model, the provider offers his virtualized resource, and is responsible for managing his infrastructure, while the customer manages his application deployed in the allocated virtual machines. These two actors typically use autonomic resource management systems to automate these tasks at runtime. Minimizing the amount of resource (and power consumption) in use is one of the main services that such cloud model must ensure. This objective can be done at runtime either by the customer at the application level (by scaling the application) or by the provider at the virtualization level (by migrating virtual machines based on the infrastructure’s utilization rate). In traditional cloud infrastructures, these resource management policies work uncoordinated: knowledge about the application is not shared with the provider. This behavior faces application performance overheads and resource wasting, which can be reduced with a cooperative resource management policy. In this research work, we discuss the problem of separate resource management in the cloud. After having this analysis, we propose a direction to use elastic virtual machines with cooperative resource management. This policy combines the knowledge of the application and the infrastructure in order to reduce application performance overhead and power consumption. We evaluate the benefit of our cooperative resource management policy with a set of experiments in a private IaaS. The evaluation shows that our policy outperforms uncoordinated resource management in traditional IaaS with lower performance overhead, better virtualized and physical resource usage

Cooperative Resource Management in a IaaS

International audienceVirtualized IaaS generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible, for energy saving. However, two situations are not taken into account, and could enhance consolidation. First, since the managed VMs can be of various sizes (small, medium, large, etc.), VMs packing can be obstructed when sizes don't fit available spaces on servers. Therefore, we would need to "split" such VMs. Second, two VMs which host replicas of the same application server (for scalability) could be "fusion Ned" when they are located on the same physical server, in order to reduce virtualization overhead and VMs memory footprint. Split and fusion operations lead to the management of elastic VMs and requires cooperation between the application level and the provider level, as they impact management at both levels. In this paper, we propose a IaaS resource management system which implements elastic VMs based on split/fusion operations and cooperative management. We show its benefit with a set of experiments

Two levels autonomic resource management in virtualized IaaS

International audienceVirtualized cloud infrastructures are very popular as they allow resource mutualization and therefore cost reduction. For cloud providers, minimizing the number of used resources is one of the main services that such environments must ensure. Cloud customers are also concerned with the minimization of used resources in the cloud since they want to reduce their invoice. Thus, resource management in the cloud should be considered by the cloud provider at the virtualization level and by the cloud customers at the application level. Many research works investigate resource management strategies in these two levels. Most of them study virtual machine consolidation (according to the virtualized infrastructure utilization rate) at the virtualized level and dynamic application sizing (according to its workload) at the application level. However, these strategies are studied separately. In this article, we show that virtual machine consolidation and dynamic application sizing are complementary. We show the efficiency of the combination of these two strategies, in reducing resource usage and keeping an application’s Quality of Service. Our demonstration is done by comparing the evaluation of three resource management strategies (implemented at the virtualization level only, at the application level only, or complementary at both levels) in a private cloud infrastructure, hosting typical JEE web applications (evaluated with the RUBiS benchmark)

Two-Phase Defect Detection Using Clustering and Classification Methods

Autonomous fault management of network and distributed systems is a challenging research problem and attracts many research activities. Solving this problem heavily depends on expertise knowledge and supporting tools for monitoring and detecting defects automatically. Recent research activities have focused on machine learning techniques that scrutinize system output data for mining abnormal events and detecting defects. This paper proposes a two-phase defect detection for network and distributed systems using log messages clustering and classification. The approach takes advantage of K-means clustering method to obtain abnormal messages and random forest method to detect the relationship of the abnormal messages and the existing defects. Several experiments have evaluated the performance of this approach using the log message data of Hadoop Distributed File System (HDFS) and the bug report data of Bug Tracking System (BTS). Evaluation results have disclosed some remarks with lessons learned

Vocational Orientation and the Need for Establishing Career Counselling Office in Vietnamese Schools

School psychology is a field that has been receiving a lot of attention recently in Vietnam as it prepares to restructure the general education curriculum. In particular, vocational orientation and career counseling are most concerned due to the emergence of new subjects in the educational curriculum - Experimental and Vocational Orientation Activities. The objective of this study is to investigate the current situation of vocational orientation problems and the need for career counseling of Vietnamese high school students to provide evidence to develop the school counseling activities and the School Psychology in Vietnam. This is a quantitative study used a questionnaire to examine the current situation of vocational orientation problems and the need for career counseling of 1200 high school students in Vietnam to illustrate the importance of vocational orientation work. The results showed that Vietnamese high school students faced many vocational orientation problems and wanted support from school counselors. But the reality did not meet the needs of students: Vietnamese schools lacked a team of school counselors both in quality and quantity, also lack of school counseling offices in almost high schools. This result is expected to contribute to the development of the School Psychology in Vietnam, but firstly, to promote the establishment of counseling office at least one office per school

Cooperative Resource Management in a IaaS

Virtualized IaaS generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible, for energy saving. However, two situations are not taken into account, and could enhance consolidation. First, since the managed VMs can be of various sizes (small, medium, large, etc.), VMs packing can be obstructed when sizes don't fit available spaces on servers. Therefore, we would need to "split" such VMs. Second, two VMs which host replicas of the same application server (for scalability) could be "fusion Ned" when they are located on the same physical server, in order to reduce virtualization overhead and VMs memory footprint. Split and fusion operations lead to the management of elastic VMs and requires cooperation between the application level and the provider level, as they impact management at both levels. In this paper, we propose a IaaS resource management system which implements elastic VMs based on split/fusion operations and cooperative management. We show its benefit with a set of experiments

F2SD: A dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.Comment: Accepted at ICMV 202

Real-Time Smile Detection using Deep Learning

Real-time smile detection from facial images is useful in many real world applications such as automatic photo capturing in mobile phone cameras or interactive distance learning. In this paper, we study different architectures of object detection deep networks for solving real-time smile detection problem. We then propose a combination of a lightweight convolutional neural network architecture (BKNet) with an efficient object detection framework (RetinaNet). The evaluation on the two datasets (GENKI-4K, UCF Selfie) with a mid-range hardware device (GTX TITAN Black) show that our proposed method helps in improving both accuracy and inference time of the original RetinaNet to reach real-time performance. In comparison with the state-of-the-art object detection framework (YOLO), our method has higher inference time, but still reaches real-time performance and obtains higher accuracy of smile detection on both experimented datasets

Investigation of salt-tolerant rhizosphere bacteria from seawater-intruding paddy rice field in Vietnam

Salt‐tolerant plant growth‐promoting rhizobacteria (ST‐PGPR) are known as potential tools to improve rice salinity tolerance. In this study, we aimed to investigate the plant growth‐promoting rhizobacteria community richness of the paddy rice fields in Soc Trang and Ben Tre Provinces where were seriously affected by sea level rise. The salinity in the sampling sites ranged from 0.14‰ to 2.17‰ in November 2018, the rainy season. The microbial abundance of samples was evaluated by spreading the samples in tryptic soy agar (TSA) medium supplemented with various concentrations of NaCl. With the increase of salt concentration up to 10% NaCl, a total number of bacteria decreased for all the samples, ranging from 106 to 104 CFU/g, and bacterial colonies were not observed at 30% NaCl. Among a total of 48 salt-resisting bacteria isolated from the rice paddy field mud surrounding the rice root, 22 isolates were able to produce indole-3-acetic acid (IAA: phytohormone for the plant growth). Seventeen out of 48 isolates were able to grow in the medium without nitrogen or phosphor sources. Six isolates having high IAA producing activity, nitrogen fixation and phosphate solubilization were belonged to Bacillus (DT6, LT16, and LHT8), Halobacillus (DT8), Aeromonas (LHT1), and Klebsiella (LHT7) genera. All the sequences of the strains DT6, DT8, LT16, LHT1, LHT7, and LHT8 were registered in the GeneBank with the accession numbers MK335670, MK335671, MK335672, MK335673, MK335674, and MK335675, respectively.

THÀNH PHẦN HÓA HỌC VÀ HOẠT TÍNH CHỐNG OXY HÓA CỦA CÁC DỊCH CHIẾT TỪ HOA XUYẾN CHI (Bidens pilosa)

Bidens pilosa is used in traditional medicine in Vietnam. The antioxidant potential of the ethanol extract and fractions from the flowers of Bidens pilosa was evaluated through DPPH and ABTS radical scavenging and the total antioxidant capacity method. The ethyl acetate fraction exhibits the highest activity with the lowest IC50 value (IC50 = 31.54 μg·mL–1 and IC50 = 35.33 μg·mL–1 for DPPH and ABTS radical scavenging capacity), and the total antioxidant capacity was 85.05 ± 0.28 mg GA·g–1. The composition of Bidens pilosa flowers: the total phenolic, total flavonoid, polysaccharides, and triterpenoid, was examined by using the colorimetric method, and their quantities are equivalent to 59.35 ± 0.83 mg GAE·g–1, 42.35 ± 1.50 mg QE·g–1, 4.44 ± 0.02%, and 32.88 ± 0.66 mg acid oleanolic·g–1, respectively. Specifically, the polysaccharide and total triterpenoid content of Bidens pilosa flowers was reported for the first time.Xuyến chi đã được sử dụng trong các bài thuốc cổ truyền Việt Nam. Khả năng chống oxy hóa của cao toàn phần và các cao phân đoạn từ hoa cây Xuyến chi được đánh giá thông qua ba mô hình: tổng khả năng chống oxy hoá, khả năng bắt gốc tự do DPPH và khả năng bắt gốc ABTS. Kết quả cho thấy cao ethyl acetate có khả năng chống oxy hóa tốt nhất với IC50 nhỏ nhất (IC50 = 31,54 μg·mL–1 và           IC50 = 35,33 μg·mL–1 tương ứng với khả năng bắt gốc DPPH và ABTS) và hàm lượng các chất chống oxy hóa cao nhất (85,05 ± 0,28 mg·g–1 acid gallic). Hàm lượng các hợp chất có hoạt tính sinh học (tổng các hợp chất phenol, tổng flavonoid, tổng triterpenoid và polysaccharide) trong dịch chiết hoa cây Xuyến chi được xác định bằng phương pháp trắc quang. Hàm lượng tổng các hợp chất phenol và flavonoid là 59,35 ± 0,83 mg GAE·g–1 và 42,35 ± 1,50 mg QE·g–1; hàm lượng polysacharide và triterpenoid là 4,44 ± 0,02% và 32,88 ± 0,66 mg acid oleanolic·g–1. Lần đầu tiên, tổng hàm lượng triterpenoid và polysacharide trong hoa Xuyến chi được công bố