Using priced timed automaton to analyse the energy consumption in cloud computing environment

According to the fact that cloud servers have different energy consumption on different running states, as well as the energy waste problem caused by the mismatching between cloud servers and cloud tasks, we carry out researches on the energy optimal method achieved by a priced timed automaton for the cloud computing center in this paper. The priced timed automaton is used to model the running behaviors of the cloud computing system. After introducing the matching matrix of cloud tasks and cloud resources as well as the power matrix of the running states of cloud servers, we design a generation algorithm for the cloud system automaton based on the generation rules and reduction rules given ahead. Then, we propose another algorithm to settle the minimum path energy consumption problem in the cloud system automaton, therefore obtaining an energy optimal solution and an energy optimal value for the cloud system. A case study and repeated experimental analyses manifest that our method is effective and feasible.     

AutoMigrate: a framework for developing intelligent,self-managing cloud services with maximum availability

Cloud services are on-demand services provided to end-users over the Internet and hosted by cloud service providers. A cloud service consists of a set of interacting applications/processes running on one or more interconnected VMs. Organizations are increasingly using cloud services as a cost-effective means for outsourcing their IT departments. However, cloud service availability is not guaranteed by cloud service providers, especially in the event of anomalous circumstances that spontaneously disrupt availability including natural disasters, power failure, and cybersecurity attacks. In this paper, we propose a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. The framework connects an autonomic computing agent to the cloud to automatically migrate cloud services based on anticipated cloud disruption. The autonomic agent employs a modular design to facilitate the incorporation of different techniques for deciding when to migrate cloud services, what cloud services to migrate, and where to migrate the selected cloud services. We incorporated a virtual machine selection algorithm for deciding what cloud services to migrate that maximizes the availability of high priority services during migration under time and network bandwidth constraints. We implemented the framework and conducted experiments to evaluate the performance of the underlying techniques. Based on the experiments, the use of this framework results in less down-time due to migration, thereby leading to reduced cloud service disruption.     

A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).     

Estimating Cloud Cover

The purpose of this activity was to help students understand the percentage of cloud cover and make more accurate cloud cover observations. Students estimated the percentage of cloud cover represented by simulated clouds and assigned a cloud cover classification to those simulations.     

Variation of the enhanced biologically damaging solar UV due to clouds.

The variation of the biologically damaging solar UV (UVBE) enhanced by clouds above that of clear sky UVBE has been investigated. This was undertaken for summer through to winter for SZA of 5 to 60 degrees employing an integrated automatic cloud and spectral UV measurement system that recorded the solar UV spectra and the sky images at five minute intervals. The UVBE calculated with action spectra with higher relative effectiveness in the UVA produced the lower percentage of cloud enhanced cases. The DNA UVBE provided the highest percentage of cloud enhanced cases compared to the total number of UV scans with 2.2% cloud enhanced cases. As a comparison, the plant and fish melanoma UVBE provided the lowest percentage of cloud enhanced cases with 0.6 to 0.8% cloud enhanced cases. For the cases of cloud enhanced UVBE, the average ratio of the measured UVBE to calculated cloud free UVBE for the photokeratitis, cataracts, plant, generalized plant damage and fish melanoma action spectra was 1.21 to 1.25. In comparison, the highest value of 1.4 was for the DNA action spectrum.     

RHAS: robust hybrid auto-scaling for web applications in cloud computing

Cluster Computing - The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives...     

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.     

基于云计算的区域医疗信息服务体系构建

分析了云计算对网络信息服务体系的影响,研究了云计算应用的特点,并以上海新华（崇明）区域医疗体系的构建为例,从网络服务结构和模块两个方面讨论分析了基于云计算的新华（崇明）区域医疗信息服务体系构建的内容, 最后完成了新华（崇明）区域医疗信息化云计算服务平台的设计。     

Genetic Diversity in RNA Virus Quasispecies Is Controlled by Host-Virus Interactions

Many RNA viruses have genetically diverse populations known as quasispecies. Important biological characteristics may be related to the levels of diversity in the quasispecies (quasispecies cloud size), including adaptability and host range. Previous work using Tobacco mosaic virus and Cucumber mosaic virus indicated that evolutionarily related viruses have very different levels of diversity in a common host. The quasispecies cloud size for these viruses remained constant throughout serial passages. Inoculation of these viruses on a number of hosts demonstrated that quasispecies cloud size is not constant for these viruses but appears to be dependent on the host. The quasispecies cloud size remained constant as long as the viruses were maintained on a given host. Shifting the virus between hosts resulted in a change in cloud size to levels associated with the new host. Quasispecies cloud size for these viruses is related to host-virus interactions, and understanding these interactions may facilitate the prediction and prevention of emerging viral diseases.     

Cloud forest dynamics in the Mexican neotropics during the last 1300 years

Key questions for understanding the resilience and variability of Mexican Neotropical cloud forest assemblages in current and future climate change include: How have human disturbances and climate change affected the dynamics of the cloud forest assemblage? What are the predominant processes responsible for its present day composition and distribution? Are the current conservation strategies for the cloud forest in accordance with preserving its natural variability through time? In this study, the temporal dynamics of the cloud forest in west‐central Mexico over the last ～1300 years were reconstructed using palaeoecological techniques. These included analyses of fossil pollen, microfossil charcoal, and sediment geochemistry. Results indicated that a cloud forest assemblage has been the predominant vegetation type in this region over the last ～1300 years. During this time, however, there have been changes in the vegetation with an apparent expansion of cloud forest from ～832 to 620 cal years bp and a decline from 1200 to 832 cal years bp . Climate change (intervals of aridity) and human disturbances through anthropogenic burning appear to have been the main factors influencing the dynamics of this cloud forest. The spatial heterogeneity reported for high‐altitude forests in this region, in concert with high beta diversity, appears to be a manifestation of the high temporal variability in species composition for these forests. Greater turnover in cloud forest taxa occurred during intervals of increased humidity and is probably representative of a higher temporal competition for resources among the cloud forest taxa. The present results support the current protection scheme for cloud forests in west‐central Mexico where areas are kept in exclusion zones to avoid timber extraction, grazing, and agriculture; this will maintain diversity within these forests, even if there are only a few individuals per species, and enable the forests to retain some resilience to current and future climate change.     

Delivery of germinal granules and localized RNAs via the messenger transport organizer pathway to the vegetal cortex of Xenopus oocytes occurs through directional expansion of the mitochondrial cloud

During Xenopus oogenesis, the message transport organizer (METRO) pathway delivers germinal granules and localized RNAs to the vegetal cortex of the oocyte via the mitochondrial cloud (Balbiani body). According to the traditional model, the mitochondrial cloud is thought to break up at the onset of vitellogenesis and the germinal granules and METRO-localized RNAs are transported within the mitochondrial cloud fragments to the vegetal cortex of the oocyte. We used light and electron microscopy in situ hybridization and three-dimensional reconstruction to show that germinal granules and METRO-localized RNAs are delivered to the oocyte cortex before the onset of mitochondrial cloud fragmentation and that the delivery involves accumulation of localized RNAs and aggregation of germinal granules at the vegetal tip of the mitochondrial cloud and subsequent internal expansion of the mitochondrial cloud between its animal (nuclear) and vegetal tips, which drives the germinal granules and METRO-localized RNAs toward the vegetal cortex. Thus the fragmentation of the cloud that occurs later in oogenesis is irrelevant to the movement of METRO-localized RNAs and germinal granules. On the basis of these findings, we propose here a revised model of germinal granule and localized RNAs delivery to the oocyte vegetal cortex via the METRO pathway.     

Whole cell microbial transformation in cloud point system

Cloud point system, consisting of nonionic surfactant in an aqueous solution, has been developed as a novel medium for whole cell microbial transformation. The basic properties of cloud point system including phase separation and solubilization are introduced. The application of cloud point system for extractive microbial transformation is different from that of water-organic solvent two-phase partitioning system or aqueous two-phase system are discussed, which mainly focus on the biocompatibility of microorganism in a cloud point system and a downstream process of microbial transformation in cloud point system with oil-water-surfactant microemulsion liquid-liquid extraction for surfactant recovery and product separation. Finally, examples of whole cell microbial transformation in cloud point systems, especially in situ extraction of moderate polar substrate/product, are also presented.     

Studies on the cloud point and micellar phases of triton X-100 using 220 MHz proton magnetic resonance techniques

High-resolution proton magnetic resonance techniques at 220 MHz were employed to follow the transformation of Triton X-100 between its micellar and cloud point phases as a function of temperature. The results obtained suggest that while a phase separation occurs rather sharply above the cloud point, the increase in temperature below the cloud point is accompanied by the gradual formation of very large structures suspended in the aqueous phase. The proton magnetic resonance studies show that the separation of phases, which occurs above the cloud point, appears to be accompanied by a fractionation of the polydisperse detergent. In addition, a lowering of the cloud point of Triton X-100 by dipalmitoyl phosphatidylcholine was observed by visual means and the results are reported here.     

A two-stage virtual machine abnormal behavior-based anomaly detection mechanism

Virtual machine abnormal behavior detection is an effective way to help cloud platform administrators monitor the running status of cloud platform to improve the reliability of cloud platform, which has become one of the research hotspots in the field of cloud computing. Aiming at the problems of high computational complexity and high false alarm rate in the existing virtual machine anomaly monitoring mechanism of cloud platform, this paper proposed a two-stage virtual machine abnormal behavior-based detection mechanism. Firstly, a workload-based incremental clustering algorithm is used to monitor and analyze both the virtual machine workload information and performance index information. Then, an online anomaly detection mechanism based on the incremental local outlier factor algorithm is designed to enhance detection efficiency. By applying this two-phase detection mechanism, it can significantly reduce the computational complexity and meet the needs of real-time performance. The experimental results are verified on the mainstream Openstack cloud platform.

     

Introduction to cloud computing: platforms and solutions

This special issue of the cluster computing journal will feature articles that discuss tools and applications for cloud computing. Specifically, it aims at delivering the state-of-the-art research on current cloud computing tools topics, and at promoting the cloud applications discipline by bringing to the attention of the community novel problems that must be investigated.     

Efficient cloud service ranking based on uncertain user requirements

In a cloud computing environment, there are many providers offering various services of different quality attributes. Selecting a cloud service that meets user requirements from such a large number of cloud services is a complex and time-consuming process. At the same time, user requirements are sometimes described as uncertain (sets or intervals), something which should be taken into account while selecting cloud services. This paper proposes an efficient method for ranking cloud services while accounting for uncertain user requirements. For this purpose, a requirement interval is defined to fulfill uncertain user requirements. Since there are a large number of cloud services, the services falling outside the requirement interval are filtered out. Finally, the analytic hierarchy process is employed for ranking. The results evaluate the proposed method in terms of optimality of ranking, scalability, and sensitivity analyses. According to the test results, the proposed method outperforms the previous methods.

     

Energy efficient job scheduling with workload prediction on cloud data center

Data centers are the backbone of cloud infrastructure platform to support large-scale data processing and storage. More and more business-to-consumer and enterprise applications are based on cloud data center. However, the amount of data center energy consumption is inevitably lead to high operation costs. The aim of this paper is to comprehensive reduce energy consumption of cloud data center servers, network, and cooling systems. We first build an energy efficient cloud data center system including its architecture, job and power consumption model. Then, we combine the linear regression and wavelet neural network techniques into a prediction method, which we call MLWNN, to forecast the cloud data center short-term workload. Third, we propose a heuristic energy efficient job scheduling with workload prediction solution, which is divided into resource management strategy and online energy efficient job scheduling algorithm. Our extensive simulation performance evaluation results clearly demonstrate that our proposed solution has good performance and is very suitable for low workload cloud data center.     

Functional diversity of epiphytes in two tropical lowland rainforests, French Guiana: using bryophyte life-forms to detect areas of high biodiversity

Recent studies have described a new tropical lowland forest type in the Guianas, the tropical lowland cloud forest. It is characterized by an enriched epiphytic species diversity particularly for bryophytes compared to common lowland rainforest, and is facilitated by frequent early morning fog events in valley locations. While the increase in epiphytic species diversity in lowland cloud forests has been documented, uncertainties remain as to (1) how this small scale variation in water supply is shaping the functional diversity of epiphytic components in lowland forests, and (2) whether information on functional group composition of epiphytes might aid in discerning these cloud forests from the common lowland rainforest. We compare the distribution of functional groups of epiphytes across height zones in lowland cloud forest and lowland rain forest of French Guiana in terms of biomass, cover as well as the composition of bryophyte life-forms. Both forests differed in functional composition of epiphytes in the canopy, in particular in the mid and outer canopy, with the cloud forest having a higher biomass and cover of bryophytes and vascular epiphytes as well as a richer bryophyte life-form composition. Bryophyte life-forms characteristic for cloud forests such as tail, weft and pendants were almost lacking in the canopies of common rain forest whereas they were frequent in lowland cloud forests. We suggest that ground-based evaluation of bryophyte life-form composition is a straightforward approach for identifying lowland cloud forest areas for conservation, which represent biodiversity hotspots in tropical lowland forests.     

Proxy based seamless connection management method in mobile cloud computing

Nowadays, complex smartphone applications are developed that support gaming, navigation, video editing, augmented reality, and speech recognition which require considerable computational power and battery lifetime. The cloud computing provides a brand new opportunity for the development of mobile applications. Mobile Hosts (MHs) are provided with data storage and processing services on a cloud computing platform rather than on the MHs. To provide seamless connection and reliable cloud service, we are focused on communication. When the connection to cloud server is increased explosively, each MH connection quality has to be declined. It causes several problems: network delay, retransmission, and so on. In this paper, we propose proxy based architecture to improve link performance for each MH in mobile cloud computing. By proposed proxy, the MH need not keep connection of the cloud server because it just connected one of proxy in the same subnet. And we propose the optimal access network discovery algorithm to optimize bandwidth usage. When the MH changes its point of attachment, proposed discovery algorithm helps to connect the optimal access network for cloud service. By experiment result and analysis, the proposed connection management method has better performance than the 802.11 access method.     

Energy aware resource allocation of cloud data center: review and open issues

The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.