Research on Distributed System Course Under the Background of New Engineering

Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating diverse and innovative outstanding engineering and technology talents.This course conducts research on the teaching content,teaching mode,and evaluation system.It combines the traditional teaching mode with the online teaching mode,as well as problem-driven theories with practice,and adopts a diversified evaluation system.The research of the course has fully mobilized students’learning enthusiasm,improved teaching quality,and achieved significant teaching results.

Novel Voltage Scaling Algorithm Through Ant Colony Optimization for Embedded Distributed Systems

Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don’t depend on the number of modes available.

Challenges and Considerations in Developing and Architecting Large-Scale Distributed Systems

This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.

Multiple UIO-based test sequence generation for distributed systems

In developing distributed systems, conformance testing is required to determine whether an implementation under test （IUT） conforms to its specification. With distributed test architectures involving multiple remote testers, testing approaches may become more complicated because of issues known as controllability and observability problems. Based on a finite state machine （FSM） representation of the system＇s specification, this paper proposes a new method to generate a test sequence utilizing multiple UIO sequences. The method is essentially guided by the way of minimizing the use of external coordination messages and input/output operations. Experiments are given to evaluate the proposed method.

Fault Tolerance Mechanisms in Distributed Systems

The use of technology has increased vastly and today computer systems are interconnected via different communication medium. The use of distributed systems in our day to day activities has solely improved with data distributions. This is because distributed systems enable nodes to organise and allow their resources to be used among the connected systems or devices that make people to be integrated with geographically distributed computing facilities. The distributed systems may lead to lack of service availability due to multiple system failures on multiple failure points. This article highlights the different fault tolerance mechanism in distributed systems used to prevent multiple system failures on multiple failure points by considering replication, high redundancy and high availability of the distributed services.

Average Convergence for Directed&Undirected Graphs in Distributed Systems

Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems.This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions.Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics.Therefore,while sustaining the significance of both directed and undirected graphs,this research emphases on the demonstration of a distributed average consensus algorithm.It uses the harmonic mean in the domain of multi-agent systems with directed and undirected graphs under static topologies based on a control input scheme.The proposed agreement protocol focuses on achieving a constant consensus on directional and undirected graphs using the exchange of information between neighbors to update their status values and to be able to calculate the total number of agents that contribute to the communication network at the same time.The proposed method is implemented for the identical networks that are considered under the directional and non-directional communication links.Two different scenarios are simulated and it is concluded that the undirected approach has an advantage over directed graph communication in terms of processing time and the total number of iterations required to achieve convergence.The same network parameters are introduced for both orientations of the communication graphs.In addition,the results of the simulation and the calculation of various matrices are provided at the end to validate the effectiveness of the proposed algorithm to achieve consensus.

A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System

In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-complete in its general form. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, consisting of processors with varying processing capabilities and network links with varying bandwidths. List scheduling algorithms are generally preferred since they generate good quality schedules with less complexity. But these list algorithms leave a lot of room for improvement, especially when these algorithms are used in specialized heterogeneous environments This paper presents an hybrid genetic task scheduling algorithm for the tasks run on the network of heterogeneous systems and represented by Directed Acyclic Graphs (DAGs). First, the algorithm assigns a coupling factor to each task to present the tasks should be scheduled onto the same processor by avoiding the large communication time. Second, the algorithm generate some high quality initial solution by scheduling the tasks which are strongly coupled with each other onto the same processor, and improve the quality of the solution by using coupling initial solutions, random solution, near optimal solutions obtained by the list scheduling algorithm in the crossover and mutation operator. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms.

Coordination of Overcurrent Relay in Distributed System for Different Network Configuration

This paper presents a study on protection coordination of over current relays (OCRs) in a distributed system by considering its different operating modes. Two different case studies which are considered in present work for protection coordination include: (i) DG interfaced distribution system in grid connected mode and (ii) DG interfaced distribution system in islanded mode of operation. The proposed approach is tested on the Canadian urban benchmark distribution system consisting of 9 buses. On the occurrence of fault, level of fault current changes which in turn changes the operating time of various OCRs. Therefore, it is important to calculate and suggest method of the relay setting in order to minimize the operating time of relays and also to avoid its mal-operation. In this paper, the protection scheme is optimally designed by taking into account the above mentioned conditions. The operating time of relays can be decreased and, at the same time, coordination can be maintained by considering the optimum values of time dial setting (TDS). Genetic Algorithm (GA) has been used for determining the optimum values of TDS and hence operating time.

Experiences Analyzing Faults in a Hybrid Distributed System with Access Only to Sanitized Data

In this paper we report on a work in progress assessing the faults observed and reported in a distributed, safety-critical, largely embedded system with both electrical and mechanical components. We illustrate why standard software testing techniques are not sufficient and indicate some of the technical and non-technical problems encountered in examining the faults and the initial results obtained. While the application domain is elevator operation, the techniques described here are general enough to apply to many other domains. Much of the data analyzed here would be considered imprecise in the software industry if it were used in software testing or to help increase fault tolerance. The paper includes a discussion of the use of multiple views of data, assessment of missing data, and analysis of informal information to produce its conclusions about fault avoidance and fault tolerance.

On the Dependability of Highly Heterogeneous and Open Distributed Systems

This paper introduces an architecture of distributed systems that facilitates the implementation of a substantial range of dependable system properties, i.e., properties that span an entire system, or a set of components dispersed throughout it. This architecture, called GDS, for governed distributed system, governs the system by controlling the flow of messages between its actors, independently of the internals of the interacting actors. This governance is done via an enforced collection of interaction laws organized into a modular and conflict free hierarchical ensemble. This ensemble of laws is sensitive to the history of interaction;and it is enforced in a decentralized manner, and is thus scalable. The dependable system properties that can be implemented under GDS can have the following beneficial consequences, among others: a) the ability to establish regularities over the system, rendering it more coherent, and easier to reason about;b) the ability to provide a degree of trust among the disparate actor of the system;and c) the ability to ensure compliance with interaction protocols that are essential for distributed computing. Consequently, the GDS architecture can have a significant impact on the following important system qualities: security, fault tolerance, auditability, and manageability.

Assigning Real-Time Tasks in Environmentally Powered Distributed Systems

Harvesting energy for execution from the environment (e.g., solar, wind energy) has recently emerged as a feasible solution for low-cost and low-power distributed systems. When real-time responsiveness of a given application has to be guaranteed, the recharge rate of obtaining energy inevitably affects the task scheduling. This paper extends our previous works in?[1] [2] to explore the real-time task assignment problem on an energy-harvesting distributed system. The solution using Ant Colony Optimization (ACO) and several significant improvements are presented. Simulations compare the performance of the approaches, which demonstrate the solutions effectiveness and efficiency.

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Cooperative Distributed Beamforming Design for Multi-RIS Aided Cell-Free Systems

Cell-free systems significantly improve network capacity by enabling joint user service without cell boundaries,eliminating intercell interference.However,to satisfy further capacity demands,it leads to high-cost problems of both hardware and power consumption.In this paper,we investigate multiple reconfigurable intelligent surfaces(RISs)aided cell-free systems where RISs are introduced to improve spectrum efficiency in an energy-efficient way.To overcome the centralized high complexity and avoid frequent information exchanges,a cooperative distributed beamforming design is proposed to maximize the weighted sum-rate performance.In particular,the alternating optimization method is utilized with the distributed closed-form solution of active beamforming being derived locally at access points,and phase shifts are obtained centrally based on the Riemannian conjugate gradient(RCG)manifold method.Simulation results verify the effectiveness of the proposed design whose performance is comparable to the centralized scheme and show great superiority of the RISs-aided system over the conventional cellular and cell-free system.

Providing Global Awareness in Distributed Dynamic Systems

The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.

Design and Implementation of Key-Value Database for Ship Virtual Test Platform Based on Distributed System

The virtual test platform is a vital tool for ship simulation and testing.However,the numerical pool ship virtual test platform is a complex system that comprises multiple heterogeneous data types,such as relational data,files,text,images,and animations.The analysis,evaluation,and decision-making processes heavily depend on data,which continue to increase in size and complexity.As a result,there is an increasing need for a distributed database system to manage these data.In this paper,we propose a Key-Value database based on a distributed system that can operate on any type of data,regardless of its size or type.This database architecture supports class column storage and load balancing and optimizes the efficiency of I/O bandwidth and CPU resource utilization.Moreover,it is specif-ically designed to handle the storage and access of largefiles.Additionally,we propose a multimodal data fusion mechanism that can connect various descrip-tions of the same substance,enabling the fusion and retrieval of heterogeneous multimodal data to facilitate data analysis.Our approach focuses on indexing and storage,and we compare our solution with Redis,MongoDB,and MySQL through experiments.We demonstrate the performance,scalability,and reliability of our proposed database system while also analysing its architecture’s defects and providing optimization solutions and future research directions.In conclu-sion,our database system provides an efficient and reliable solution for the data management of the virtual test platform of numerical pool ships.