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In the era of big data, practical applications in various domains continually generate large-scale time-series data . Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data . It is critical to efficiently identify the potential periodic patterns from massive time-series data and provide accurate predictions. In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment. To effectively handle the massive historical datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is presented, which can reduce the data scale as well as accurately extracting the characteristics. Based on this, we propose a multi-layer time series periodic pattern recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis (FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP) algorithm is proposed. Data in the subsequent period are predicted based on all previous period models, in which a time attenuation factor is introduced to control the impact of different periods on the prediction results. Moreover, to improve the performance of the proposed algorithms, we propose a parallel solution on the Apache Spark platform, using the Streaming real-time computing module. To efficiently process the large-scale time-series datasets in distributed computing environments , Distributed Streams (DStreams) and Resilient Distributed Datasets (RDDs) are used to store and calculate these datasets. Logical and data dependencies of RDDs in the P-TSDCA, P-MTSPPR, and P-PTSP processes are considered, and the corresponding parallel execution solutions are conducted. Extensive experimental results show that our PPTSP algorithm has significant advantages compared with other algorithms in terms of prediction accuracy and performance.

The development of Internet of Things technology brings new opportunities for the development of edge computing. As an emerging computing model, edge computing makes full use of the equipment resources at the edge of the network and creates a new network computing system at the edge of the network. At the same time, the emergence of edge computing solves the problem of high latency in WAN which cannot be solved for a long time in the field of cloud computing, and brings users with low latency, fast response and good service experience. This article will use the edges computing architecture to construct a multi-layer data collection system. In this system model, sensors upload data to the designated edge nodes for processing, rather than remote cloud computing centers. Data collection and sample training tasks of sensor nodes in different ranges are realized through the design of multi-layer edge nodes. This system reduces the energy consumption of data uploading and the delay in network communication. As a result, it provides a better network experience for the end users. And it tries to solve the problem that the edge node in the edge system cannot satisfy multiple training task requests at the same time.

The new generation of technologies, represented by cloud computing, artificial intelligence (AI), internet of things (IoT), and mobile internet, is driving the great renovation of human society as well as the rapid development of new economy and new industries. New requirements are then emerged for the software engineering education at this new era. Hunan University (HNU) relies on outstanding platforms, such as the National Demonstration Software College, the National Featured Software Engineering Major, and the National Pilot Zone for Software Talents Education, carrying out researches on teaching mode renovation, course scheme renovation, and practice scheme renovation within a series of teaching renovation projects. As a result, the Innovative Software Talents (IST) education scheme has been established with accountable achievements and it also has been widely introduced, demonstrated, and applied among Chinese universities.

Big data applications have become increasingly popular with the appearance of cloud computing and green computing. Therefore, internet service providers (ISPs) need to build data centers for data storage and data processing under the cloud service pattern. However, data centers often consume a significant amount of energy and lead to pollutant emissions. In recent years, the high energy consumption and environmental pollution of data centers have become a pressing issue. This paper reviews the progress of energy-saving technologies in high-performance computing, energy conservation technologies for computer rooms and renewable energy applications during the construction and operation of data centers. From multiple perspectives of energy consumption, cost reduction, and environment protection, a comprehensive set of strategies are proposed to maximize data centers’ efficiency and minimize the environmental impact. This paper also provides energy-saving trends for data centers in the future.

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

Cloud service, which has changed the business computing pattern, redefines the calculation scope of distributed systems by cloud computing approaches. The existing cloud service model provides different users an advanced on-demand computing model, and then achieves resource interaction between cloud service providers and users by cloud computing. However, this model, working as a centralized service strategy of resource allocation, also generates much scattered space debris resulting in a low efficiency of resource scheduling and utilization. To address this issue, we devise an improved model of crowd sourcing service and propose a technique of storage resource allocation and scheduling, where cloud service providers, as a resource agent, only provide leasing storage space for individual applicants instead of providing storage resources directly. This technique achieves allocation and scheduling of individual storage resources dynamically, according to user's expectations and individual storage providers constraints. The experimental results based on a large data set show that the improved model reduces the storage space of cloud service providers by 88.2% and improves storage space utilization rate of individuals by 65.2%, The improved model reduces the user fees by 64.8% and the cloud services development cost by 78.6% than that of existing models, In addition, the improved model and methods provide higher profits and efficiency for cloud service providers.

In a secure query scheme over the encrypted cloud data, an authorized cloud user can obtain data files of interest by submitting encrypted query keywords to the cloud server, which performs a certain secure search algorithm and returns back the corresponding data file set. In practice, the returned query results may be incorrect or incomplete due to possible data corruption, software bugs, or intermediate attackers who maliciously tamper with results; moreover, the cloud server may also intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide the query results verification mechanism that allows the data user to verify results. In this paper, we design three varigrained and secure query results verification constructions leveraging the Bloom filter and cryptographic hash functions, for a query result set R, by which the data user can verify: (1) the correctness of each data file in R, (2) how many qualified data files are not returned by the cloud, and (3) which qualified data files are not returned by the cloud, respectively. Furthermore, our proposed verification mechanism can be very easily integrated into all secure query schemes for cloud computing. Performance evaluation shows that the proposed schemes are practical and efficient.