Heterogeneous Computer Networks Research Articles

Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters the challenge of energy constraints. In this paper, we investigate the energy efficiency (EE) optimization problem in heterogeneous computing-assisted non-orthogonal multiple access mobile edge computing (NOMA-MEC) network model. Edge devices are configured with CPU-NPU heterogeneous computing processors to improve the AI task processing, and NOMA is applied to edge task offloading to enable massive connectivity. The system-centric energy efficiency maximization problem is studied. We formulate a system-centric energy efficiency maximization problem. To solve this problem, we decouple the original problem into two subproblems, i.e., the optimization problems of heterogeneous computing workload splitting and task offloading. A heuristic algorithm and binary search algorithm are proposed to optimize NOMA user pairing and task offloading delay, respectively. Furthermore, we propose a multi-objective alternative iterative algorithm to optimize system energy efficiency. Numerical results show that the proposed scheme can significantly improve the energy efficiency of heterogeneous computing-assisted NOMA-MEC networks compared to the existing baselines.

Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture

The theoretical and practical progress that has occurred in the field of computer networks during the past fifteen years has enhanced the economical efficiency and social relevance of related real-world use cases. Nevertheless, this ubiquitous usage has also introduced numerous security risks. Therefore, monitoring hardware and software resources represents one of the main instruments used in order to prevent potential attacks and to ensure the security and reliability of a network. Various solutions have been reported in the related scientific literature. In essence, most of the existing approaches are not suitable to implement a real-time hardware monitoring and management solution, particularly in heterogeneous networks. Therefore, the main contribution of this paper is represented by an architectural and implementational model, which is effective in order to build an interconnected system that can help system and network administrators to secure a network. This requirement is met by considering symmetrical design and implementation features related to various operating systems. Thus, the existing symmetrical relationships among identified parameters allow for the data to be wrapped into the same custom network packages, which are transported over the communication medium or are stored using the same data structures or tables. The system has been thoroughly assessed considering several real-world use case scenarios, and the results demonstrate that the proposed model can be applied to software-defined networks, which can be protected by relevant intrusion detection systems (IDS).

Mathematical model of quantitative assessment of stability of heterogeneous of computer networks in the conditions of the propagation of the influence of a logical obstacle on the basis of the percolation theory

The trends of recent years, especially with the beginning of the aggression of the Russian Federation against Ukraine, testify to the significantly increasing requirements for increasing the efficiency of the management processes of state authorities, with a significant reduction in the management cycle time. At the same time, increasing the efficiency of task performance is impossible without the introduction of modern information technologies into work at all levels of state administration, and, as a result, the formation of a single information space. The article examines the peculiarities of the construction of modern automated control systems, which require the construction of territorially distributed, at the same time, heterogeneous (heterogeneous) computer networks, which must ensure the availability and reliability of the transmission of the necessary information, including in the conditions of the spread of the influence of logical interference (intentional — cyber-attacks on the part of hackers and unintentional actions of the user, system administrator, etc.), which in turn affect the creation of a single information space. Developed approaches to ensure the stability of heterogeneous computer networks that, in turn, use the equipment of Internet service providers in conditions of external and internal destabilizing factors. A mathematical model for assessing the stability of heterogeneous computer networks of critical purpose, under the influence of logical interference, is proposed. At the same time, the theory of percolation is used as a theoretical basis. This approach makes it possible to take into account the lack of reliable information about the stability indicators of network components that belong to various Internet service providers (not under the control of state administration bodies). Computer modeling based on the proposed mathematical model indicates an increase in the efficiency of the computer network as a whole.

A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms

Individuals and businesses are currently involved in the administration of remote sensing data that was previously handled only by government agencies. There is a lot more information in remote sensing data than go through the eye, and retrieving it is time-consuming and computationally expensive. Clusters, distributed networks, and specialized hardware devices are essential to speeding up remote sensing data extraction calculations. HPC advances in remote sensing applications are examined in this research. High-performance computing (HPC) concepts for instance FPGAs and GPUs as well as large-scale and heterogeneous computer networks are examined (GPUs). Using HPC paradigms, remote sensing applications are examined in these sections.

PSciLab: An Unified Distributed and Parallel Software Framework for Data Analysis, Simulation and Machine Learning—Design Practice, Software Architecture, and User Experience

In this paper, a hybrid distributed-parallel cluster software framework for heterogeneous computer networks is introduced that supports simulation, data analysis, and machine learning (ML), using widely available JavaScript virtual machines (VM) and web browsers to accommodate the working load. This work addresses parallelism, primarily on a control-path level and partially on a data-path level, targeting different classes of numerical problems that can be either data-partitioned or replicated. These are composed of a set of interacting worker processes that can be easily parallelized or distributed, e.g., for large-scale multi-element simulation or ML. Their suitability and scalability for static and dynamic problems are experimentally investigated regarding the proposed multi-process and communication architecture, as well as data management using customized SQL databases with network access. The framework consists of a set of tools and libraries, mainly the WorkBook (processed by a web browser) and the WorkShell (processed by node.js). It can be seen that the proposed distributed-parallel multi-process approach, with a dedicated set of inter-process communication methods (message- and shared-memory-based), scales up efficiently according to problem size and the number of processes. Finally, it is demonstrated that this JavaScript-based approach for exploiting parallelism can be used easily by any typical numerical programmer or data analyst and does not require any special knowledge about parallel and distributed systems and their interaction. The study is also focused on VM processing.

A Platform Programming Paradigm for Heterogeneous Systems Integration

To cope with growing computing performance requirements, cyber-physical systems architectures are moving toward heterogeneous high-performance computer architectures and networks. Such architectures, however, incur intricate side effects that challenge traditional software design and integration. The programming paradigm can take a key role in mastering software design, as experience in automotive design demonstrates. To cope with the integration challenge, this industry has started introducing a programming paradigm that efficiently preserves application data flow under platform integration and changes with minimum performance loss. This article will revisit this paradigm that is currently used for lock-free multicore programming and explain its extension to the system level. It will then explore its application to two important developments in industrial design. This article will conclude with an evaluation of its properties, its overhead, and its application toward a robust design process.

Геометрические диграфы как модели гетерогенных компьютерных сетей

New mathematical models of networks are considered — geometrical oriented graphs. Such models adequately reflect the structure of heterogeneous wireless computer, sensor and other networks. The strongly connected components of geometric digraphs, their dependence on the number of network nodes in a given area, on the distributions of the radii of the zones of reception and transmission of signals are investigated. The probabilistic characteristics of random geometric digraphs, the features of the dependences of the probability of strong connectivity on the number of nodes in the digraphs for various combinations of radii are analyzed.

Управление информационными рисками сложной системы с использованием механизма «когнитивной игры»

Purpose of the article: development of mechanisms for solving problems of information risk management of complex systems in conditions of uncertainty and mutual influence of system elements on each other. Research method: game-theoretic mathematical modeling of risk management processes in complex systems based on arbitration schemes and multistep games on cognitive maps. The result: a general model of a complex system (for example, a heterogeneous computer network) is considered, within which the risk manager (risk-manager) carries out effective risk management by distributing the resource at his disposal among its elements (nodes of a computer network). To assess the state of the system elements, functions of local risk are proposed that satisfy certain specified requirements, and to assess the state of the system as a whole, an integral risk function is proposed. It is shown that in the case of independence (absence of mutual influence on each other) of the system elements to find an effective resource allocation, a game-theoretic approach can be used based on an arbitration scheme based on the principles of stimulation and non-suppression (MS-solution). For the case when changes in the level of risk for one element of the system can have a significant impact on the levels of risks of other elements, it is proposed to use game-theoretic models based on the MS-solution and a multistep “cognitive game”.

Методи автоматизованого проектування гетерогенних комп’ютерних систем та мереж критичного застосування

The article provides a review of design methods for heterogeneous computer information systems and critical application networks, analyzes requirements, provides a description of information technologies, their components, conditions of use, results of their application. A method for the computer-aided design of heterogeneous computer systems and critical application networks is proposed when operational tasks are included in the control system. The analysis of features arising during reconfiguration is carried out. The modern basic technical and software of computer systems for critical applications are analyzed. The general requirements for computer systems of critical applications and their components are formulated. The development trends and existing needs of computer systems and networks of critical application are determined. Based on the analysis of heterogeneous computer networks and theoretical generalization of work in the field of computer-aided design of critical application systems, the authors have developed a method for the computer-aided design of heterogeneous computer systems and critical-use networks.

VERIFICATION OF NONSTATIONARY POLYNOMIAL TRAFFIC MODELS

In this paper, we perform an experimental study of models for the optimal estimation of polynomial traffic in heterogeneous computer networks. To achieve the goal of the study, the tasks of verification of traffic models with different dimensions of polynomial traffic are set and solved. The verification procedure is understood as checking the adequacy of traffic models to reference models using selected criteria and adequacy indicators, in the form of which target functionals are used, which determine the accuracy and reliability of traffic modeling. The main criteria are point and interval estimates of concordance (consistency) among themselves of model and reference values of moments of polynomial traffic. Models of non-stationary traffic in heterogeneous computer networks are built using the quantization and Markov approximation methods and the least squares method. The multidimensional normal distribution of all values of aggregated traffic in the nonstationary interval is assumed to be Gaussian. To assess the accuracy of the approximation of traffic, the concept of reference traffic is introduced and a comparison of the first two moment characteristics of the bypass model and reference traffic is used. This allows you to compare multidimensional Gaussian distributions of reference and model unsteady traffic. An increase in the dimensionality of the models is accompanied by an increase in the accuracy of approximation of non-stationary traffic. The proposed system of indicators and methods of verification of models of non-stationary traffic with a different number of traffic states allow you to verify models with the necessary degree of completeness. The calculations of the verification criteria for models of nonstationary polynomial traffic of dimension n = 3 and n = 4 in heterogeneous computer networks in the discrete case using the Euclidean and Hilbert space metrics in the continuous case. The obtained results make it possible to identify general patterns of growth in the accuracy of traffic approximation (degree of model adequacy) depending on the increase in the dimension of the models and sample sizes.

Extended Balanced Scheduler with Clustering and Rep- lication for Data Intensive Scientific Workflow Applications in Cloud Computing

Cloud computing is an advance computing model using which several applications, data and countless IT services are provided over the Internet. Task scheduling plays a crucial role in cloud computing systems. The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks. With the enlargement of users of cloud the tasks need to be scheduled. Cloud’s performance depends on the task scheduling algorithms used. Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers. The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method. As scientific workflow is combination of fine grain and coarse grain task. Every task scheduled to VM has system overhead. If multiple fine grain task are executing in scientific workflow, it increase the scheduling overhead. To overcome the scheduling overhead, multiple small tasks has been combined to large task, which decrease the scheduling overhead and improve the execution time of the workflow. Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined. The proposed scheduling algorithm improves the performance metrics such as execution time and cost. Further this research can be extended with improved clustering technique and replication methods.

High Available Fault Tolerant Technique in Distributed Shared Memory

distributed systems, that are based on constructing a network of heterogeneous computers, suffer from the problem of failing components during the system run time. In case of failure, the distributed applications must be restarted from the scratch. The main goal of this research is to add the dynamic failure recovery technique to the JavaSpaces server. So, the client continues its jobs while failures occur in the system. Also, the new technique in JavaSpaces is evaluated by analyzing and testing. General Terms Parallel systems, Distributed shared memory, Fault Tolerant.

Protocol Transparency in Heterogeneous Computer Networks

Protocol Transparency in Heterogeneous Computer Networks

Research on Heterogeneous Network Data Communication

With the continuous development of network applications, the new network structure emerging. To solve the interconnection of heterogeneous computer networks is essential, real-time network traffic is the current focus of the study of computer network. Articles focus on these two aspects of the problem and put forward a thought of heterogeneous network with the structure, in the network-level and application-level implementation of a heterogeneous network interfaces isomorphism. We discusses the implementation of real-time communication network principle and method based on the client / server model, gives a heterogeneous network to solve the problem of real-time communication process model, programs and implementation framework.

High Performance Computing for Hyperspectral Remote Sensing

Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.

Recent Developments in High Performance Computing for Remote Sensing: A Review

Remote sensing data have become very widespread in recent years, and the exploitation of this technology has gone from developments mainly conducted by government intelligence agencies to those carried out by general users and companies. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenge. For this purpose, high performance computing (HPC) infrastructure such as clusters, distributed networks or specialized hardware devices provide important architectural developments to accelerate the computations related with information extraction in remote sensing. In this paper, we review recent advances in HPC applied to remote sensing problems; in particular, the HPC-based paradigms included in this review comprise multiprocessor systems, large-scale and heterogeneous networks of computers, grid and cloud computing environments, and hardware systems such as field programmable gate arrays (FPGAs) and graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art and most recent developments in those areas, and offer a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to remote sensing problems.

Agent-based platform to support the execution of parallel tasks

Parallel computing has been radically evolving in the recent years from the supercomputer multi-processor centralised point of view to the modern distributed approaches such as grid computing. The availability of relatively obsolete or underused hardware and the increasing LAN and WAN interconnection speed have motivated the success of those new paradigms. In this paper, we propose the use of agent technology to improve the management, flexibility and reusability of grid-like parallel computing architectures. We present a general purpose agent-based architecture which is able to manage and execute independent parallel tasks through one or several heterogeneous computer networks – or even Internet nodes – exploiting state of the art agent mobility capabilities. A particular application of the proposed architecture to support the execution of a complex knowledge acquisition task is also introduced, showing a high scalability and a very low overhead.

Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis

AbstractThe purpose of content‐based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by latest‐generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever‐growing data archives. To address this need, this paper develops a new parallel CBIR system that has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub‐pixel precision using spectral mixture analysis concepts, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations and a Beowulf cluster at NASA's Goddard Space Flight Center. Our experimental results indicate that the proposed parallel system can efficiently retrieve hyperspectral images from complex image databases by efficiently adapting to the underlying parallel platform on which it is run, regardless of the heterogeneity in the compute nodes and communication links that form such parallel platform. Copyright © 2009 John Wiley & Sons, Ltd.

Parallel Processing of Remotely Sensed Hyperspectral Images On Heterogeneous Networks of Workstations Using HeteroMPI

The development of efficient techniques for transforming massive volumes of remotely sensed hyperspectral data into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for information extraction and mining from hyperspectral imagery assume homogeneity in the underlying computing platform, heterogeneous networks of computers (HNOCs) have become a promising cost-effective solution, expected to play a major role in many on-going and planned remote sensing missions. In this paper, we develop a new morphological parallel algorithm for hyperspectral image classification using HeteroMPI, an extension of MPI for programming high-performance computations on HNOCs. The main idea of HeteroMPI is to automate and optimize the selection of a group of processes that executes a heterogeneous algorithm faster than any other possible group in a heterogeneous environment. In order to analyze the impact of many-to-one (gather) communication operations introduced by our proposed algorithm, we resort to a recently proposed collective communication model. The parallel algorithm is validated using two heterogeneous clusters at University College Dublin and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center.

Data Partitioning with a Functional Performance Model of Heterogeneous Processors

In this paper, we address the problem of optimal distribution of computational tasks on a network of heterogeneous computers when one or more tasks do not fit into the main memory of the processors and when relative speeds vary with the problem size. We propose a functional performance model of heterogeneous processors that integrates many essential features of a network of heterogeneous computers having a major impact on its performance such as the processor heterogeneity, the heterogeneity of memory structure, and the effects of paging. Under this model, the speed of each processor is represented by a continuous function of the size of the problem whereas traditional models use single numbers to represent the speeds of the processors. We formulate a problem of partitioning of an n-element set over p heterogeneous processors using this model and design an algorithm of the complexity O(p × log2n) solving the problem.