Diagnosing Evolution of Cloud Cluster via Spatio-temporal Trace Analysis

To analyze inner-enterprise cloud cluster performance, the role of workload analysis is of paramount interest to system designers. However, the ever-evolving nature of inner-enterprise cloud platforms such as diversity and spatio-temporal nature of workloads makes evolution diagnosing a challenging task. In this paper, we propose MuCoTrAna-Inner, an evolution diagnosing approach for a large-scale cloud data center based on comparative spatio-temporal trace analysis. Moreover, we present a case study on two representative big traces: Alibaba 2017 trace, and Alibaba 2018 trace. Novel quantitative findings along with the performance bottleneck inferences and recommendations based on workload analysis are provided. Our multifaceted analyses of the traces and new findings not only reveal interesting insights that are of interest to system designers and administrators, but also establish a new view to diagnosing the evolution of inner-enterprise cloud cluster based on trace analysis.

A Parameter for Quantifying the Macroscale Asymmetry of Tropical Cyclone Cloud Clusters

A parameter to quantify macroscale (i.e., systemwide) asymmetry of tropical cyclones (TC) in infrared satellite images, galaxy asymmetry (GASYM), which is adopted from astronomy, is described. In addition, an alternative approach to identify TC cloud clusters that is based on a density-based spatial clustering algorithm, cluster identification (CI), is presented in this study. Although a commonly used approach in TC study, the predefined radius of calculation (ROC), can be used to identify the TC region in the calculation of GASYM, this approach is not optimal because the size of the TC cloud cluster is often unknown in the calculation. The area specified by the ROC often includes pixels that do not belong to the TC cloud cluster and excludes pixels that belong to the TC cloud cluster. The CI approach addresses this issue by identifying TC cloud clusters of any size with any shape, because it depends solely on the threshold brightness temperature that corresponds to the upper bound of the brightness temperature of the specific cloud types. This study shows that the CI approach can be integrated into the GASYM calculation as an objective measure of TC symmetry. Although GASYM-CI and intensity are correlated, the relationship between GASYM-CI and intensity depends on the size of the TC cloud cluster. Comparison between GASYM and an existing objective method to quantify symmetry of TCs, the deviation angle variance technique, is also presented.

Implementation of Big-Data Applications Using Map Reduce Framework

Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce Word Count utility. The main aim of this paper is to give implements of Hadoop map-reduce programming by giving a hands-on experience in developing Hadoop based Word-Count and Apriori application. Word count problem using Hadoop Map Reduce framework. The Apriori Algorithm has been used for finding frequent item set using Map Reduce framework.

Retrieval of the vertical evolution of the cloud effective radius from the Chinese FY-4 (Feng Yun 4) next-generation geostationary satellites

The vertical evolution of the cloud effective radius (Re) reflects the precipitation-forming process. Based on observations from the first Chinese next-generation geostationary meteorological satellites (FY-4A, Feng Yun 4), we established a new method for objectively obtaining the vertical temperature vs. Re profile. First of all, Re was calculated using a bispectral lookup table. Then, cloud clusters were objectively identified using the maximum temperature gradient method. Finally, the Re profile in a certain cloud was then obtained by combining these two sets of data. Compared with the conventional method used to obtain the Re profile from the subjective division of a region, objective cloud-cluster identification establishes a unified standard, increases the credibility of the Re profile, and facilitates the comparison of different Re profiles. To investigate its performance, we selected a heavy precipitation event from the Integrative Monsoon Frontal Rainfall Experiment in summer 2018. The results showed that the method successfully identified and tracked the cloud cluster. The Re profile showed completely different morphologies in different life stages of the cloud cluster, which is important in the characterization of the formation of precipitation and the temporal evolution of microphysical processes.

Processing streams in a monitoring cloud cluster

The creation of monitoring clusters based on cloud computing technologies is a promising direction for the development of systems for continuous monitoring of objects for various purposes in the web space. Hadoop web-programming environment is the technological basis for the development of algorithmic and software solutions for the synthesis of monitoring clusters, including information security and information counteraction systems. The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. The need to use service monitoring plays an important role in the cloud computing industry, especially for SLA/QoS assessment, as the application or service may experience problems even if the virtual machines on which the work is taking place appear to be operational. This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. The obtained results made it possible to develop mathematical models of parallel DStreams from sources processed in a cloud cluster via Hadoop technology using the micro-batch architecture of the Spark Streaming module. These models take into account the flow of requests for maintenance from sources of different services, on the one hand, and, on the other hand, the needs of services in bit rate, taking into account the multichannel traffic of sources of various services. At the same time, analytical relations are obtained to calculate the required performance of the Hadoop cluster at a given value of the probability of batch loss.