Locally Minimum Storage Regenerating Codes in Distributed Cloud Storage Systems

In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.

MCloudSim:modeling and simulating distributed clouds

Many distributed clouds which try to integrate the advantages of centralized clouds and distributed systems have been studied in recent years.Traditional cloud simulators focus on large scale data centers with virtualized servers,and cannot meet the needs of distributed cloud simulations.This paper introduces a generalized and extensible simulation framework,named MCloudSim,which is used for modeling and simulating distributed clouds.MCloudSim has the following characteristics;(1)the overlay protocol in MCloudSim can be freely replaced without affecting the function of other modules;(2) a Petri net workflow theory based task model is proposed to simulate distributed tasks;(3) a distributed cloud can be easily built by assembling and expanding the basic resource entities provided by MCloudSim.Finally,simulation results of scenarios with a 3-tier central-controlled distributed cloud and a P2 P based cloud prove that MCloudSim has high efficiency and satisfies performance in supporting large scale experiments and different distributed clouds.

Video-based Person Re-identification Based on Distributed Cloud Computing

Person re-identification has been a hot research issues in the field of computer vision.In recent years,with the maturity of the theory,a large number of excellent methods have been proposed.However,large-scale data sets and huge networks make training a time-consuming process.At the same time,the parameters and their values generated during the training process also take up a lot of computer resources.Therefore,we apply distributed cloud computing method to perform person re-identification task.Using distributed data storage method,pedestrian data sets and parameters are stored in cloud nodes.To speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution process.Moreover,we assign different layers of the re-identification network to different nodes to complete the training in the way of model parallelism.By comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent years.Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption.Many factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial variations.The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations.This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs.Based on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.Realistic databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Service optimization in programmable cloud network

With the rapid development in cloud data centers and cloud service customers,the demand for high quality cloud service has been grown rapidly.To face this reality,this paper focuses on service optimization issues in cloud computing environment.First,a service-oriented architecture is proposed and programmable network facilities are utilized in it to optimize specific cloud services.Then various cloud services are categorized into two subcategories;static services and dynamic services.Furthermore,the concepts of cloud service quality and cloud resource idle rate are defined,and the aforementioned concepts have also been taken into consideration as parameters in the service optimization algorithm to improve the cloud service quality and optimize system workload simultaneously.Numerical simulations are conducted to verify the effectiveness of the proposed algorithm in balancing the workload of all servers.

A public Cloud-based China's Landslide Inventory Database(Cs LID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database(Cs LID) by utilizing Google's public cloud computing platform. Firstly, Cs LID(Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the Cs LID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the Cs LID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.

Relationships between Cloud Droplet Spectral Relative Dispersion and Entrainment Rate and Their Impacting Factors

Cloud microphysical properties are significantly affected by entrainment and mixing processes.However,it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution.Previously,the relationship between relative dispersion and entrainment rate was found to be positive or negative.To reconcile the contrasting relationships,the Explicit Mixing Parcel Model is used to determine the underlying mechanisms.When evaporation is dominated by small droplets,and the entrained environmental air is further saturated during mixing,the relationship is negative.However,when the evaporation of big droplets is dominant,the relationship is positive.Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets.However,if cloud condensation nuclei are not entrained,the relationship is positive.If cloud condensation nuclei are entrained,the relationship is dependent on many other factors.High values of vertical velocity,relative humidity of environmental air,and liquid water content,and low values of droplet number concentration,are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets.Further,the signs of the relationship are not strongly affected by the turbulence dissipation rate,but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate.A conceptual model is proposed to reconcile the contrasting relationships.This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.

Cognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution

In this paper, we explore the process of emotional state transition. And the process is impacted by emotional state of interaction objects. First of all, the cognitive reasoning process and the micro-expressions recognition is the basis of affective computing adjustment process. Secondly, the threshold function and attenuation function are proposed to quantify the emotional changes. In the actual environment, the emotional state of the robot and external stimulus are also quantified as the transferring probability. Finally, the Gaussian cloud distribution is introduced to the Gross model to calculate the emotional transitional probabilities. The experimental results show that the model in human-computer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot. This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.

Spatial Distributions of Atmospheric Radiative Fluxes and Heating Rates over China during Summer

The latitude-altitude distributions of radiative fluxes and heating rates are investigated by utilizing CloudSat satellite data over China during summer. The Tibetan Plateau causes the downward shortwave fluxes of the lower atmosphere over central China to be smaller than the fluxes over southern and northern China by generating more clouds. The existence of a larger quantity of clouds over central China reflects a greater amount of solar radiation back into space. The vertical gradients of upward shortwave radiative fluxes in the atmosphere below 8 km are greater than those above 8 km. The latitudinal-altitude distributions of downward longwave radiative fluxes show a slantwise decreasing trend from low latitudes to high latitudes that gradually weaken in the downward direction. The upward longwave radiative fluxes also weaken in the upward direction but with larger gradients. The maximum heating rates by solar radiation and cooling rates by longwave infrared radiation are located over 28 40°N at 7 8 km mean sea level (MSL), and they are larger than the rates in the northern and southern regions. The heating and cooling rates match well both vertically and geographically.

Numerical Simulation of the Influence of Drop Size Distribution Shape on Cloud and Precipitation

At present,parameterization methods to describe cloud and precipitation processes are widely used in cloud and mesoscale models,but with different drop size distributions.When precipitation formation mechanism,weather modification technique,and mechanism of hail suppression with seeding are studied by using these models,a question that needs to be addressed is：what is the influence of different drop size distributions and related parameters on cloud and precipitation？In this paper,by using a three-dimensional hail cloud numerical model developed by the Institutes of Atmospheric Physics,Chinese Academy of Sciences, we performed numerical experiments with varied drop size distribution parameters for two hail storms,and analyzed the influence of shape parameters（ar,ai,and ag）of raindrops,ice crystal,and graupel size distributions on rainfall,hail amount,and microphysical processes in clouds.The results show that the variation of ar has no effect on precipitation formation on the whole,but affects directly the production rates for the physical processes related to raindrop.The ag variation has a less obvious effect on rainfall amount,but has a significant effect on hail amount,hailfall rate,and rainfall intensity.It impacts noticeably on the generation rate of the number and mass of ice crystal,graupel,and hail,and also to various degrees on all the microphysical processes in clouds.The ag variation also influences the growing process of the hydrometeors.The effects of the ai variation on part of the generation and growing processes of all the hydrometeors are significant,and even dramatic,such as the collection process of cloud water to rain through melting ice crystal（T CLcir）.However,for clouds located in different geographic regions,the variation of ai has different effects on precipitation,which reflects the complexity of the impact of drop size distribution on cloud and precipitation.At last,some issues about the application of cloud models are also discussed.

Proposed framework for blockchain technology in a decentralised energy network

The integration of distributed renewable energy sources into the conventional power grid has become a hot research topic, all part of attempts to reduce greenhouse gas emission. There are many distributed renewable energy sources available and the network participants in energy delivery have also increased. This makes the management of the new power grid with integrated distributed renewable energy sources extremely complex. Applying the technical advantages of blockchain technology to this complex system to manage peer-to-peer energy sharing, transmission, data storage and build smart contracts between network participants can develop an optimal consensus mechanism within the new power grid. This paper proposes a new framework for the application of blockchain in a decentralised energy network. The microgrid is assumed to be private and managed by local prosumers. An overview description of the proposed model and a case study are presented in the paper.

A New Classifier for Facial Expression Recognition:Fuzzy Buried Markov Model

To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model （FBMM） is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model （HMM） by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model （BMM）, FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information （MMI） method to replace maximum likelihood （ML） method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.

TIFAflow: Enhancing Traffic Archiving System with Flow Granularity for Forensic Analysis in Network Security

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.

Analysis of the Microphysical Properties of a Stratiform Rain Event Using an L-Band Profiler Radar

This paper investigates spatial and temporal distributions of the microphysical properties of precipitating stratiform clouds based on Doppler spectra of rain particles observed by an L-band profiler radar.The retrieval of raindrop size distributions（RSDs） is accomplished through eliminating vertical air motion and isolating the terminal fall velocity of raindrops in the observed Doppler velocity spectrum.The microphysical properties of raindrops in a broad stratiform region with weak convective cells are studied using data collected from a 1320-MHz wind profiler radar in Huayin,Shaanxi Province on 14 May 2009.RSDs and gamma function parameters are retrieved at altitudes between 700 and 3000 m above the surface,below a melting layer.It is found that the altitude of the maximum number of raindrops was closely related to the surface rain rate.The maximum number of large drops was observed at lower altitudes earlier in the precipitation event but at higher altitudes in later periods,suggesting decreases in the numbers of large and medium size raindrops.These decreases may have been caused by the breakup of larger drops and evaporation of smaller drops as they fell.The number of medium size drops decreased with increasing altitude.The relationship between reflectivity and liquid water content during this precipitation event was Z = 1.69×10~4M^（1.5）,and the relationship between reflectivity and rain intensity was Z = 256I^（1.4）.

Roundtable： Research Opportunities and Challenges for Large-Scale Software Systems

For this special section on software systems, six research leaders in software systems, as guest editors tor this special section, discuss important issues that will shape this field＇s future research directions. The essays included in this roundtable article cover research opportunities and challenges for large-scale software systems such as querying organization- wide software behaviors （Xusheng Xiao）, logging and log analysis （Jian-Ouang Lou）, engineering reliable cloud distributed systems （Shan Lu）, usage data （David C. Shepherd）, clone detection and management （Xin Peng）, and code search and beyond （Qian-Xiang Wang）. - Tao Xie, Leading Editor of Software Systems.