Flow Of Requests Research Articles

Approximate models of air transport queueing systems with partial mutual assistance between service channels

Approximate analytical models are proposed for two types of multi-channel queuing systems with partial mutual assistance between channels and waiting in limited-size and unbounded queues. The incoming flow of requests is a superposition of several simplest flows. It is assumed that normative numbers of channels for servicing a customer that differ depending on the flow of incoming customer requests are established. In the systems of the first type the normative number of channels is not necessary: a customer is accepted for servicing if there are free channels in the system and the number of channels is smaller than the normative one. Service time is distributed exponentially with the average inversely proportional to the number of allocated channels. In systems of the second type the service of a customer is always performed by the channels of the normative number. Service time can be distributed arbitrarily (not only exponentially) with the average irrespective of the number of allocated channels. The paper presents the results of comparative analysis of the results of dealing with some tasks of designing service systems in a wide range of input data using simulation modelling and calculations based on approximate analytical models that allowed evaluating the application area of the latter and making a conclusion about their satisfactory accuracy. Examples of the use of models of technological processes of airport ground handling as queuing systems of the considered types are given.

Performance Evaluation and Cyberattack Mitigation in a Blockchain-Enabled Peer-to-Peer Energy Trading Framework

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.

The Word “Expert” in Russian Media Discourse: Semantics and Pragmatics in Conditions of Uncertainty

The semantics of the word “expert” in the Russian language media space and the pragmatics of introducing expert commentary into a media text are studied using the method of content analysis and discourse analysis of expert media content. The studied media content includes interviews with experts on YouTube; expert comments introduced into the journalistic text of the leading socio-political mass media and online lifestyle media, which are in the top 10 according to Mediascope, as well as expert channels in Telegram. The method of associative experiment revealed the value-evaluative meanings attached to the word expert in the linguistic consciousness of journalism students. The relevance of the study is conditioned by the explosive increase in the use of the word expert in the Russian-language media space in the last two years, the increasing flow of requests for expert opinions on digital platforms, and the involvement of more and more humanities students in expert discourse in response to the media sphere’s demand for the formation of a stable picture of the world. The novelty of this article is predetermined by the material itself, which has not been systematically analyzed before, as well as by the approach to the research. The study has shown that in the conditions of socio-political instability and uncertainty of the future, the main function of the expert is to interpret events and orient in the modern world, which is expressed in the frequent combination of the word with verbs of speaking: explained, interpreted, analyzed. At the same time, the content of the concept of expert in the young generation of journalists is associated with such notions as high social status, communicative leadership, and realization in the media space, i. e. with those that describe a modern person immersed in the info-newsletter, an information specialist. The pragmatic element of the increase of expert media content in the media is connected, on the one hand, with the problem of media fake news, which was aggravated with the onset of the pandemic and special military operation (SMO), the problem of trust in the media, and, on the other hand, with the need for journalists to resort to the interpretation of an increasingly complex and turbulent world.

Mathematical Model of Technological Equipment while Analyzing the Capacity of Car Service Stations

The paper proposes mathematical models of a single-channel and multi-channel queuing system with un-reliable channels for car service stations operating in a competitive environment. An equation that expresses the throughput of a multi-channel queuing system depending on the number of channels, the intensities of the flow of requests and their services, channel failures and their restoration is obtained. It has been proved by calculation that in general the intensities of requests and services flows have the greatest impact on the QS throughput. The influence of various parameters on the throughput of the queuing system has been studied. The parameters that should be paid special attention to when choosing equipment for car service enterprises are determined.

DEVELOPMENT OF THE CONCEPT FOR THE COMPUTATIONAL RESOURCES MANAGEMENT SOFTWARE IN THE CUSTOMER SERVICE SYSTEMS

Background. To date, there is no customer service system that does not involve information and computer systems. One of the most important issues in ensuring the reliability and reliability of such systems is the task of dynamic scaling and providing the required amount of computing resources at any time. This study was focused on the planning and deployment of computing infrastructure that is able to respond to significantly increased volumes of request flows, changes in the dynamics of load intensity, strict requirements for the quality of their service, etc.
 Objective. The purpose of the paper is to create a concept of virtual computing space to meet the needs of distributed customer service system, which takes into account the peculiarities of service, the computing node load nature, service quality requirements, and provides energy efficient. Developed models, methods will control the performance of distributed computing infrastructure and flow maintenance processes, reduce downtime of computing resources and provide services to end users at a given level of quality.
 Methods. Analysis of the operation of the node load assessment mechanism, which consists in a dynamic change in the intensity of control of the state of function nodes, showed the effectiveness of planning for a group of computing nodes..
 Results. The proposed approach to managing a heterogeneous computation environment to improve the efficiency of the service maintenance process in new generation systems is a unified solution for highly loaded distributed systems. The developed concept made it possible to avoid a decrease in the quality of service during surges of congestion and to maintain the indicators of the quality of service at a given level, provided that the resource utilization ratio is kept within the given limits
 Conclusions. In summary, a mathematical model of the problem of determining the maximum allowable load volume with a QoS level guarantee for a service node in a heterogeneous telecommunications environment was proposed.

Single channel queuing system utilization factor model

Under the assumption of a low intensity of the flow of incoming requests and a high speed of their service, the M|M|1 system utilization factor model is considered. It is estimated for a small part of the observation time. Approximate relations are obtained for the system utilization factor. Its simple probabilistic model in the form of a probabilistic mixture is proposed. A comparison with the results of simulation is carried out.

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems' impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively.

A lightweight DDoS detection scheme under SDN context

Software-defined networking (SDN), a novel network paradigm, separates the control plane and data plane into different network equipment to realize the flexible control of network traffic. Its excellent programmability and global view present many new opportunities. DDoS detection under the SDN context is an important and challenging research field. Some previous works attempted to collect and analyze statistics related to flows, usually recorded in switches, to address DDoS threats. In contrast, other works applied machine learning-based solutions to identify DDoS and achieved promising results. Generally, most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions. Nevertheless, the request for flow rules is very time-consuming and CPU-consuming; moreover may congest the communication channel between the controller and the switches. Therefore, we present FORT, a lightweight DDoS detection scheme, which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state. A time-series algorithm, ARIMA, is utilized to determine the port statistics adaptively, and an SVM algorithm is applied to detect whether a DDoS attack does occur. Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy. Referring to the false alarm rate of 1.24% in the comparison scheme, the false alarm rate of this scheme is only 0.039%, which significantly reduces the probability of false alarm. Besides, by introducing the alarm mechanism, this scheme can reduce the load of the southbound channel by more than 60% in the normal state.

Scalable and Flexible Traffic Steering for Service Function Chains

Network Function Virtualization (NFV) has inspired numerous orchestration algorithms to decide Virtualized Network Function (VNF) placement and routing paths for service requests with Service Function Chain (SFC) demands. With different optimization goals, these algorithms may select various routing paths for request flows. Nevertheless, existing traffic steering solutions either fail to fully support NFV Orchestrator (NFVO) in path selection, or result in low scalability in the underlay Software Defined Network (SDN). In this paper, we propose STAR to tackle both problems simultaneously. STAR divides the entire routing path for SFC request into several path segments, then performs Output-Port-based, Default-Path-based or RSP-ID-based traffic steering on each path segment. With the idea of path division and these traffic steering mechanisms, STAR achieves flexible traffic steering along any paths selected by NFVO and enables different Rendered Service Paths (RSPs) with the same path segments to share the same forwarding rules. In this way, STAR ensures the correct enforcement of path selection decisions from NFVO and significantly reduces forwarding rule consumption and control overhead. We evaluate STAR with the experiments on a real testbed and large-scale simulations. The results show that our framework is scalable in the SDN data plane and control plane (e.g., reducing rules in hardware switches by more than 70% compared with NSH-based solutions) while retaining the flexibility of steering traffic along any SFC routing paths to provide full support for path decision enforcement (e.g., achieving the highest path decision enforcement ratio in 11 of the 12 cases) with acceptable overhead.

HIPA: A hybrid load balancing method in SSDs for improved parallelism performance

NAND Flash-based SSDs have been widely adopted as underlying storage devices in storage systems thanks to high random performance in the SSDs. Because of inherent rich parallelism resources (channel, chip, plane, and die) inside an SSD, many researchers pay much attention on parallelism improvement. Page allocation policies (static and dynamic page allocation) in an SSD aim to translate logical addresses of I/O requests on the host into physical addresses in flash memory space. Existing static page allocation policies suffer from severe load unbalance inside SSDs. Dynamic allocation policies employed in high-performance SSDs address the load balancing issue. However, dynamic allocation-based I/O requests flow into fewer parallelism resources, deteriorating read I/O-requests parallelism. Recognizing that SSD performance can be optimized by reducing read parallelism loss, we propose a hybrid page allocation strategy named HIPA to fully exploit the advantages of dynamic and static allocation to achieve read/write parallelisms inside an SSD. HIPA embraces a load prediction mechanism to monitor die-level load in real-time, thereby determining a page allocation policy based on the status of die-level load. A static page allocation policy is triggered in case of balanced die-level load. When performance degrades due to imbalance in die-level load, HIPA dynamically changes the gear to a dynamic allocation policy, aiming to balance the load on dies. Thus, HIPA achieves the maximum read parallelism rather than crumbling write performance. Conducting the extensive experiments, we validate the advantages of HIPA in terms of average response time, plane-level write, and erase standard deviation. The findings unveil that compared with the four popular page allocation policies (CWDP, CHIP-D, DIE-D, and PLANE-D), HIPA slashes the average response time by up to 58% with an average of 20% and 81% with an average of 33% compared with the dynamic allocation (DIE-D) with the lowest response time and the static strategy, respectively; (2) drives up the write standard deviation degree of the static strategy by up to 44%; (3) improves erase standard deviation rate by up to 43%.

Planning the Number of Aircraft in a Group Flight with their Survivability and the Required Observation Duration of Ground Objects

In the interests of improving the quality of a group aircraft flight planning, the developing algorithms problem formulation for the operational determination of the permissible observation duration for ground objects is formulated. An algorithm for determining the observation duration when servicing the next request to be described in the form of a fuzzy logic procedure is proposed. To implement the algorithm for determining the duration of observation, a specialized expert system has been developed. The input of the expert system receives values that describe the influence of the factors in assessing the priority of servicing the next object. At the output of the expert system, an alternative is formed to continue searching for an object or to stop. A new approach to solving the target distribution problem between aircraft in a group flight is proposed. It is based on the joint use of two dynamic priorities in the minimax algorithm for selecting observation objects and for assigning service aircraft. An original approach to determine the rational aircrafts composition in one flight is proposed. This is done with the help of the queuing theory apparatus, which takes into account the random nature of the dynamic situation. To estimate the required aircrafts composition when servicing the flow of requests, the countermeasures process is described using two nonlinear differential equations (of the Riccati type). A general formula for determining aircrafts composition in one flight has been obtained. Mathematical model of the aircraft survivability loss in the form of the Bernoulli equation is formed. Computer simulation of the aircraft survivability losses in one flight was carried out for three cases: with weak interference, with equal opposing forces, with strong counteraction.

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs. Currently, the Fourth-Generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a Blockchain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted Cloud Service Provisioning (CSP) to the CU through the FCP at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored in the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement of 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 ​at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches.

A Novel Scheme for Controller Selection in Software-Defined Internet-of-Things (SD-IoT).

The software-defined networking (SDN) standard decouples the data and control planes. SDN is used in the Internet of Things (IoT) due to its programmability, central view and deployment of innovative protocols, and is known as SD-IoT. However, in SD-IoT, controller selection has never been studied. Controllers control the network and react to dynamic changes in SD-IoT. As sensors communicate frequently with the controller in SD-IoT, there is a degradation in performance with scalability and an increase in flow requests. Hence, the controller performance and selection are critical for SD-IoT. However, one controller’s support for certain functions is high while another’s is poor. There are various SD-IoT controllers, and choosing the best one might be a multi-criteria choice. An analytical network decision making process- (ANDP) based technique is employed here to identify feature-based optimal controllers in SD-IoT. The experimental analysis quantifies the high-weight controller from the feature-based comparison. An ANDP-based feature-based controller selection strategy is suggested, which selects the controller with the best feature set first, before comparing performance. This paper’s main contribution is to evaluate the ANDP for SD-IoT controller selection based on its features and performance validation in the SD-IoT environment. The simulation results suggest that the proposed controller outperforms the controller selected with previous schemes. Choosing an optimal controller in SD-IoT reduces the delay in both normal and heavy traffic scenarios. The suggested controller also increases throughput while using the central processing unit (CPU) efficiently and reduces the recovery latency in case of failures in the network.

Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare

Patient signals produced from a physical device, such as electrocardiography (ECG), which records the electrical activity of the heart, are vulnerable to keep noise due to various physical constraints of acquisition devices. It is critical for the detection and diagnosis of a variety of diseases. An ECG signal should be displayed as clean and clear as feasible due to its relevance in assisting physicians and doctors in making appropriate judgments. ECG is vulnerable to many types of noise because it is an electrical signal. To improve the quality of assistance for consumers Info a surveillance system by cooperative surveillance systems, high-quality data processing by the observed surveillance systems is required, which predetermines the requirement for high noise immunity of the latter. At the same time, the basics of building cooperative surveillance systems as a network of two-channel asynchronous information communication systems which include numeral of transmitting and receiving process using diverse frequency limits for reception and transmission, failure-prone open single-channel queuing systems, and request signals do not allow to provide the required the noise resistance of the systems under consideration. This paper first gives a characterization of request and interference signal flows in cooperative surveillance systems and briefly examines the features of request signals and their effect on the immunity of cooperative surveillance systems. Then, it calculates the noise resistance of the demand signals of the cooperative surveillance systems for the probabilities of errors: signal skipping; First- and second-degree false alarms in the case of unrelated events, unintended and intra-system impulse interferences in the request channel

SDNShield: NFV-Based Defense Framework Against DDoS Attacks on SDN Control Plane

Software-defined networking (SDN) is increasingly popular in today’s information technology industry, but existing SDN control plane is insufficiently scalable to support on-demand, high-frequency flow requests. Weaknesses along SDN control paths can be exploited by malicious third parties to launch distributed denial-of-service (DDoS) attacks against the SDN control plane. Recently proposed solutions only partially solve the problem, by protecting either the SDN network edges or the centralized controller. We propose SDNShield, a solution based on emerging network function virtualization (NFV) technologies, which enforces more comprehensive defense against potential DDoS attacks on SDN control plane. SDNShield incorporates a three-stage overload control scheme. The first stage statistically identifies legitimate flows with low complexity and performance overhead. The second stage further performs in-depth TCP handshake verification to ensure good flows are eventually served. The third stage intellectually salvages the misclassified legitimate flows that are falsely dropped from the first two stages. Prototype tests and real data-driven simulation results show that SDNShield can achieve high resilience against brute-force attacks, and maintain good flow-level service quality at the same time.

DITIS: A Distributed Tiered Storage Simulator

This paper presents DITIS, a simulator for distributed and tiered file-based storage systems. In particular, DITIS can model a distributed storage system with up to three levels of storage tiers and up to three additional levels of caches. Each tier and cache can be configured with different number and type of storage media devices (e.g., HDD, SSD, NVRAM, DRAM), each with their own performance characteristics. The simulator utilizes the provided characteristics in fine-grained performance cost models (which are distinct for each device type) in order to compute the duration time of each I/O request processed on each tier. At the same time, DITIS simulates the overall flow of requests through the different layers and storage nodes of the system using numerous pluggable policies that control every aspect of execution, ranging from request routing and data redundancy to cache and tiering strategies. For performing the simulation, DITIS adapts an extended version of the Actor Model, during which key components of the system exchange asynchronous messages with each other, much like a real distributed multi-threaded system. The ability to simulate the execution of a workload in such an accurate and realistic way brings multiple benefits for its users, since DITIS can be used to better understand the behavior of the underlying file system as well as evaluate different storage setups and policies.

Модели массового обслуживания с простейшими потоками для низкоорбитальной спутниковой системы

The relevance of the work is associated with the active deployment of low-orbit communication systems and the expansion of research in the field of corresponding satellite systems. A promising low-orbit communication system based on relay satellites with the function (RSRFs) of routing message packets is considered. The low earth orbit communications systems use the BGP protocol and the AAA functionality at the ground station. For assessing the characteristics of RSRF inter-satellite paths, a scenario was created for the message packets arrival from a group of inter-satellite paths to one subscriber path. The corresponding analytical models have been developed using the mathematical apparatus of queuing systems with the simplest flows of requests and exponential distribution of the service time. The RSRF characteristics of a promising low-orbit communication system are predicted. It is proposed to make the mathematical apparatus of analytical models more complicated to take into account the dynamics of displacements and failures of the RSRF in a low-orbit communication system.

Joint Dynamical VNF Placement and SFC Routing in NFV-Enabled SDNs

Due to that Service Function Chain (SFC) permits the forwarding of flows along a predetermined sequence chain of Virtual Network Functions (VNFs), it has become a common service in Network Function Virtualization (NFV)-enabled Software Defined Networks (SDNs). Generally, since there are multiple same VNF-instances in NFV-Enabled SDNs, this brings a great challenge for selecting or placing the required VNF-instances to satisfy the routing of SFC Request flows (SRs). In this paper, we study the routing problem for SRs by jointly considering dynamical VNF placement and multiple Resources and Quality of Service (QoS) constraints in NFV-Enabled SDNs. Specifically, we first define two optimization problems: one is the Dynamical VNF Placement and Routing Problem for SRs (DVPRP) and the other is the Delay, packet Loss and Jitter Aware Dynamical VNF Placement and Routing Problem for SRs (DLJA-DVPRP). We then formulate the two problems as Integer Linear Programming (ILP) problems. Next, we creatively devise an auxiliary edge-weight graph and propose two efficient algorithms to solve the problems with the aim of minimizing the resource consumption costs as well as ensuring multiple QoS constraints. Especially, we utilize the shortest path algorithm based on Lagrange relaxation method to solve the DLJA-DVPRP with multiple QoS constraints. Compared with existing algorithms, simulation results demonstrate our proposed algorithms have better performance in terms of throughput, traffic acceptance rate and load balance.

ТОПОЛОГІЯ ПОБУДОВИ ОПЕРАЦІЙНОЇ СИСТЕМИ РЕАЛЬНОГО ЧАСУ АВТОМАТИЗОВАНОЇ СИСТЕМИ УПРАВЛІННЯ ВІЙСЬКОВО-МОРСЬКИМИ СИЛАМИ ЗБРОЙНИХ СИЛ УКРАЇНИ

This paper considers the problem creation of an operating system automated control system for the Naval Forces of the Armed Forces of Ukraine from the point of view of the peculiarities of scenarios of its load in accordance with the real situation. The authors focused on the fact that modern technical solutions that implement the most advanced developments in science and technology regarding the construction of modern automated control systems, in their content, do not cover the assessment of the ability of existing operating systems as a basic complex of interconnected programs for full-fledged control of the hardware component of the control system, ensuring control of the computing process and organizing its full-fledged interaction with users. Special attention was focused on the architecture of the real-time operating system, the main purpose of which is to provide the necessary and sufficient set of functions for the design, development and operation of real-time systems on specific hardware in our case, in a conditional data processing center (of the corresponding level) of the automated control system of the Naval Forces of the Armed Forces of Ukraine. The real-time operating system of the automated control system (ACS) of the Naval Forces of Ukraine must have a hybrid core that implements the connection between application processes and hardware, a basic set of applications, system libraries and maintenance programs. The advantages of this particular architecture are: increased reliability (each service is essentially an independent process that is easier to debug and track errors that occur), improved scalability (unnecessary services can be excluded from the system without material damage to its performance), increased fault tolerance and others. In this aspect, when the ACS data processing center will operate with a sufficiently large flow of service requests, it is quite appropriate to use the apparatus of the Queuing system theory.