Distributed Environment Research Articles

Overview of Applied Data Analytic Mechanisms and Approaches Using Permissioned Blockchains

Blockchain technology deployment has surged in diverse domains to secure and maintain valuable data. Wherever valuable data exists, the motivation of applying analytics emerges. However, this case is slightly different since it deals with a distributed system environment with security constraints such as privacy and confidentiality. This study aims to provide an overview of approaches that applied analytics over permissioned blockchains. Moreover, extract key features from these studies to report and discuss common features and best practices. This contributes to determining the requirements to apply analytics and outlines the remaining challenges. The research method was conducted in four phases. The initial phase states the goals and objectives. Subsequently, the analysis phase examines a group of research papers to extract key features from various studies. These features were divided into three categories: general aspects, data management, and an analytics perspective. Afterward, the outcomes are classified according to the findings and observations to point out common aspects and best practices. Finally, the evaluation of the research determines the requirements to apply data analytics over permissioned blockchains. Based on the findings and observations of these research papers. Most of the studies focused on off-chain analytics with the assistance of a third party. Also, most of the analytics types were descriptive and diagnostic, whereas fewer studies proposed predictive analytics. This explains the lack of existing approaches that use artificial intelligence and real-time analysis. The most used blockchain platform for analytics was Hyperledger fabric for multiple reasons mentioned in detail in this research.

Network intrusion detection based on ensemble classification and feature selection method for cloud computing

AbstractCloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.

IReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network

A new distributed environment at less financial expenditure on communication over the Internet is presented by cloud computing. In recent times, the increased number of users has made network traffic monitoring a difficult task. Although traffic monitoring and security problems are rising in parallel, there is a need to develop a new system for providing security and reducing network traffic. A new method, iReTADS, is proposed to reduce the network traffic using a data summarization technique and also provide network security through an effective real-time neural network. Although data summarization plays a significant role in data mining, still no real methods are present to assist the summary evaluation. Thus, it is a serious endeavor to present four metrics for data summarization with temporal features such as conciseness, information loss, interestingness, and intelligibility. In addition, a new metric time is also introduced for effective data summarization. Finally, a new neural network known as Modified Synergetic Neural Network (MSNN) on summarized datasets for detecting the real-time anomaly-behaved nodes in network and cloud is introduced. Experimental results reveal that the iReTADS can effectively monitor traffic and detect anomalies. It may further drive studies on controlling the outbreaks and controlling pandemics while studying medical datasets, which results in smart healthy cities.

A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^a</xref> with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>^a</label> <uri>https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/</uri> </fn>

Scalable Access Control Scheme of Internet of Things Based on Blockchain

With the development of Internet of things (IoT) technology, a large-scale, heterogeneous and dynamic distributed IoT environment has gradually formed between different IoTs. In order to solve the scalability problem of restricted device access management in the Internet of things, a distributed access control system model of the Internet of things based on blockchain technology is proposed. The system model adopts a single smart contract, which simplifies the whole process in the blockchain network and reduces the communication overhead between nodes. According to the simulation results and evaluation, it is proved that the solution has good scalability.

Efficient Distributed Approaches to Core Maintenance on Large Dynamic Graphs

As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage and efficiency. In this article, we investigate distributed approaches to core maintenance on a pregel-like system, which is a famous graph computing system. We first design a core decomposition algorithm to obtain core numbers of vertices in a given graph. Based on it, a distributed batch-stream combined algorithm (DBCA) is devised to efficiently maintain the core numbers when vertex/edge updates happen. In particular, we introduce a new task assignment strategy to DBCA based on diversity of the edge-cores of updated edges. To ensure that DBCA can accurately process core maintenance, we develop a message interaction protocol to resolve the problem of crosstalk among different tasks. Comprehensive experiments have been conducted on real/synthetic graphs, more specifically, in two typical distributed environments built on Supercomputing Center and Alibaba Cloud. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

A novel approach towards tracing the parents of orphaned children and dead bodies in cloud and IoT-based distributed environment by integrating DNA databank with Aadhar and FIR databases

A novel approach towards tracing the parents of orphaned children and dead bodies in cloud and IoT-based distributed environment by integrating DNA databank with Aadhar and FIR databases

A novel approach towards tracing the parents of orphaned children and dead bodies in cloud and IoT-based distributed environment by integrating DNA databank with Aadhar and FIR databases

A novel approach towards tracing the parents of orphaned children and dead bodies in cloud and IoT-based distributed environment by integrating DNA databank with Aadhar and FIR databases

On totalisation of computable functions in a distributive environment

Totalisation of operations and functions is a subject of great importance in the precise sciences at large, and in particular, in computer science, where computed functions must be totalised to account for edge cases and prevent system failures. In this paper, we investigate the totalisation of computable functions in the general and abstract setting of the axiomatic theory of computing systems, such as automata, computer networks, and algorithms, allowing us to define characteristics and discover relations which resonate true through many computational models – from individual automata through complex distributed systems with concurrent functioning. We explore the possible types of totalisations, the complexes of computing systems that do (or do not) have totalisations, and the behaviour of totalisation in various transformations and compositions. In addition, we point to a few interesting directions for future research.

A hybridized water wave energy harvester with a swing magnetic structure toward intelligent fishing ground

Wave energy is one of the most abundant energy sources in the ocean with a vast range of distributions, large-scale reserves, and high energy density. A hybridized triboelectric-electromagnetic energy harvester is demonstrated as an efficient method for harvesting wave energy. Here, a hybridized water wave energy harvester (H-WWEH) is proposed based on a swing magnetic structure with a permanent magnet as the mass block. Benefiting from the swing magnetic structure including a permanent magnet with polyester fiber (PET) friction layer adhered on the surface, the H-WWEH can harvest the water wave energy in any direction. When the H-WWEH is excited by the motion of the water wave, the friction layer will slide on a bottom friction layer to generate electric energy. At the same time, a coil installed under the bottom friction layer is used to scavenge the magnetic energy during the movement of the permanent magnet. By optimizing the length of the swing string, setting the electrode of TENG as Tai Chi shape, and specifying the connection between triboelectric nanogenerator (TENG) and electromagnetic generator (EMG) parallel, the output voltage and current of TENG and EMG can reach 90 V and 0.61 μA, 5.3 V and 6.4 mA with the wave at a low frequency of 1.75 Hz in a simulated water environment. On the basis of the prominent performance, a self-powered wireless wave height alarm system for the intelligent fishing ground is built, and the wireless transmission is spontaneous through the inclination switch. It demonstrates that the H-WWEH has the potential to drive distributed and self-powered marine environment monitoring sensors.

PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things

Botnets have become a major threat in the Internet of Things (IoT) landscape, due to the damages that these sets of compromised IoT devices may cause. To increase their attacks’ success, modern botnets are designed in a distributed manner, following a P2P structure. Recently, several botnet detection solutions have been proposed. Among them, community behavior analysis solutions seem to be promising because of their high detection accuracy. However, such solutions are not optimized for real life scenarios since they only run in a static mode, that is, reading all network traffic at once. As such, they do not support real-time data analysis. In order to handle such issue, these solutions should run in a dynamic distributed environment where different actors participate in the detection process. However, this collaborative environment brings up the issue of trust among the actors.To address this issue, in this paper, we present PAutoBotCatcher, a dynamic botnet detection framework based on community behavior analysis among peers managed by different actors. PAutoBotCatcher leverages on blockchain to ensure immutability and transparency among all actors. To optimize continuous detection while keeping good accuracy, we design a set of optimization techniques, such as caching detection’s output and pre-processing the shared network traffic. In addition, we leverage on different privacy-preserving techniques to protect devices from re-identification during the botnet detection process. We have extensively tested our solution to show its effectiveness and to demonstrate that blockchain is a good solution for dynamic botnet detection.

An Efficient Algorithm for Fast Service Edge Selection in Cloud-Based Telco Networks

Telecommunication operators are increasingly integrating computational infrastructure into their networks at different location levels, including the network edge. This makes a highly distributed processing environment a reality, which is expected to enable next-generation services. This article proposes a novel and efficient algorithm to determine the best service execution locations through the “service edge,” a concept that groups services in categories according to their requirements and benefits from the flexibility of distributed cloud resources. The article is focused on describing the algorithm so that it can be integrated into the telco operators' management and orchestration systems. Simulation results underpin the practical feasibility of the proposed algorithm.

Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge

Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication algorithm to detect super points in a distributed environment, Rough Estimator based Asynchronous Distributed super points detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super points. Meanwhile, the famous Linear Estimator (LE) is applied to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. This paper proves that the accuracy of READ in a distributed environment is no less than that in the single-node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has high accuracy in a distributed environment, but also has less than 5% of communication burden compared with existing algorithms.

Investigations on optimizing performance of the distributed computing in heterogeneous environment using machine learning technique for large scale data set

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.

Distributed Simulation of Heterogeneous Mission Subsystems Through the Black Lion Framework

This paper describes the design and implementation of Black Lion, a purely software-based, distributed environment for integrated testing of independent spacecraft mission models. The Black Lion simulation environment is architected to be reconfigurable, allowing for any number of heterogenous software models, across one or multiple computing platforms, to be integrated into a single spacecraft simulation. This architecture enables seamless integration of legacy software models that were never designed to work together into mission-wide simulations. A flat-sat scenario is used to showcase the capabilities of Black Lion. In this flat-sat scenario, Black Lion synchronizes and ties together the following components: a ground system model, a spacecraft physical simulation, and a flight processor emulator in which the flight software application executes. The numerical simulation presented in this paper showcases the closed-loop behavior of the entire spacecraft system.

NVIDIA IndeX accelerated computing for visualizing Cholla's galactic winds

Galactic winds – outflows of gas driven out of galaxies by the combined effects of thousands of supernovae – are a crucial feature of galaxy evolution. Despite their importance, a complete theoretical picture of these winds has been elusive. Simulating the complicated interaction between the hot, high pressure gas created by supernovae and the cooler, high density gas in the galaxy disk requires massive computational resources and sophisticated software. For this purpose, Computational Hydrodynamics On Parallel Architectures (Cholla) has been demonstrated to be a scalable and efficient tool that operates in large, distributed multi-GPU environments at high levels of performance. This additional performance results in unprecedented resolution for this type of simulation and massive amounts of generated data. This raises the requirements for analysis tools that can cope with scale and complexity of the simulated physical processes. To address those challenges, we utilize NVIDIA IndeX as a scalable framework to visualize the simulation output: NVIDIA IndeX features a streaming-based architecture to interactively explore simulation results in large-scale, multi-GPU environments. We utilize customized sampling programs for multi-volume and surface rendering to address analysis questions of galactic wind simulations. This combination of massively parallel simulation and analysis allows us to utilize recent supercomputer capabilities and to speed up the exploration of galactic wind simulations.

Optimizing teacher basic need satisfaction in distributed healthcare contexts

Optimizing teacher motivation in distributed learning environments is paramount to ensure high-quality education, as medical education is increasingly becoming the responsibility of a larger variety of healthcare contexts. This study aims to explore teaching-related basic need satisfaction, e.g. teachers’ feelings of autonomy, competence and relatedness in teaching, in different healthcare contexts and to provide insight into its relation to contextual factors. We distributed a digital survey among healthcare professionals in university hospitals (UH), district teaching hospitals (DTH), and primary care (PC). We used the Teaching-related Basic Need Satisfaction scale, based on the Self-Determination theory, to measure teachers’ basic needs satisfaction in teaching. We studied relations between basic need satisfaction and perceived presence of contextual factors associated with teacher motivation drawn from the literature. Input from 1407 healthcare professionals was analyzed. PC healthcare professionals felt most autonomous, UH healthcare professionals felt most competent, and DTH healthcare professionals felt most related. Regardless of work context, teachers involved in educational design and who perceived more appreciation and developmental opportunities for teaching reported higher feelings of autonomy, competence, and relatedness in teaching, as did teachers who indicated that teaching was important at their job application. Perceived facilitators for teaching were associated with feeling more autonomous and related. These results can be utilized in a variety of healthcare contexts for improving teaching-related basic need satisfaction. Recommendations for practice include involving different healthcare professionals in educational development and coordination, forming communities of teachers across healthcare contexts, and addressing healthcare professionals’ intentions to be involved in education during job interviews.

Evaluation of Basic Data Compression Algorithms in a Distributed Environment

Data compression methods aim at reducing the data size in order to make data transfer more efficient. To accomplish data compression, the basic algorithms such as Huffman, Lempel-Ziv (LZ), Shannon-Fano (SF) and Run-Length Encoding (RLE) are widely used. Most of the applications incorporate different variants of these algorithms. This paper analyzes the execution times, compression ratio and efficiency of compression methods in a client-server distributed environment. The data from a client is distributed to multiple processors/servers, subsequently compressed by the servers at remote locations, and sent back to the client. Our experimentation has been carried out using Simgrid Framework. Our results show that the LZ algorithm attains better efficiency/scalability and compression ratio, however, it works slower than other algorithms.

Chronic patient remote monitoring through the application of big data and internet of things.

Chronic patients could benefit from the technological advances, but the clinical approaches for this kind of patients are still limited. This paper describes a system for chronic patients monitoring both, in home and external environments. For this purpose, we used novel technologies as big data, cloud computing and internet of things (IoT). Additionally, the system has been validated for three use cases: cardiovascular disease (CVD), hypertension (HPN) and chronic obstructive pulmonary disease (COPD), which were selected for their incidence in the population. This system is innovative within e-health, mainly due to the use of a big data architecture based on open-source components, also it provides a scalable and distributed environment for storage and processing of biomedical sensor data. The proposed system enables the incorporation of non-medical data sources in order to improve the self-management of chronic diseases and to develop better strategies for health interventions for chronic and dependents patients.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.