LOAD BALANCING OF A MULTIPROCESSOR COMPUTER SYSTEM USING THE METHOD PARTICLE SWARM OPTIMIZATION

The subject of the research is to improve the features of the organization of information security of multiprocessor computing systems. The goal of this work is to identify and implement measures to secure information that can be effective when using multiprocessor modular computing systems or in parallel calculations on multithreaded systems. To achieve this goal, the following tasks are solved: comparison of data protection methods in multiprocessor and sequential systems, as well as study of the specifics of applying various cryptographic methods to the methods of implementing protection; in accordance with some aspects of constructing multiprocessor systems, consideration and identification of key elements that require special attention in the development of a security system; to show that the main choice of data protection methods in multiprocessor systems is determined by differences from sequential systems in the theoretical and hardware implementation. The methods used are: main provisions of the theory of computing systems, theory of parallel computing, theory of the construction of operating systems, methods and algorithms for data protection. The following results were obtained. The main aspects in definition and use of information protection measures that can be effective when using multiprocessor modular computing systems or in parallel calculations on multithreaded systems are identified. Comparison of methods of data protection with sequential systems is carried out, and also the influence of application of various cryptographic methods on methods of data protection implementation is considered. It is shown that the main choice of data protection methods in multiprocessor systems is determined by differences from sequential systems in theoretical and hardware implementation. The conclusions . In accordance with some aspects of the construction of multiprocessor systems, the key elements that require special attention in the development of a security system are examined and identified. The paper shows that a parallel system requires both more hardware and software, and with each additional calculation module the system becomes more complex. And this, in turn, further complicates the protection system as a whole, which may entail some slowdown in the execution of application programs with the help of multiprocessor computing systems. However, in the long term, the proposed approach makes it possible to provide increased security for the operation of multiprocessor systems. To protect data in such systems, a number of methods are considered and analyzed.

The scope of this research is the use of artificial neural network models and meta-heuristic optimization of Particle Swarm Optimization (PSO) for the prediction of ambient air pollution parameter data at air quality monitoring stations in the city of Semarang, Central Java. The observed parameter is an indicator of ambient air quality, Suspended Particulate Matter (SPM). Based on air quality parameter data in previous times which is a time series data, modeling is done using Neural Networks (NN). Estimation of weights from NN is done using a hybrid method between meta-heuristic and gradient optimization. The meta-heuristic optimization method used is Particle Swarm Optimization (PSO) while the gradient based method is the Conjugate Gradient. Optimization with PSO is done first, then proceed with optimization using the Conjugate Gradient. Four scenarios of iteration selection at the PSO stage are 10, 25, 50 and 100. At the Conjugate Gradient, stage iteration is carried out up to 1000 epohs. The predicted results were compared with the PSOs and Conjugate Gradient respectively. The results show that the hybrid method provides better predictions. The number of iterations needed at the PSO stage is not too much so it is efficient in combining the two methods.

Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation. The study employs PSO techniques to optimize CNN in order to address the investigation of algorithmic optimization in DL. The results show that the suggested model outperforms the other models like CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), and PSO –SVM(Support Vector Machine) to increase the performance and efficacy of the cloud systems. The experiment is implemented using Python and assessed using Google Cluster dataset that is accessible to the public. Findings: The use of ML and DL techniques are found to be more efficient in cloud infrastructure than the conventional methods. The study examines the performance of the RF, PSO and CNN and the hybrid RF-PSO-CNN models. The accuracy, precision, and F1. Score metrics were used to assess the performance of the classification models. The recommended model RF-PSO-CNN outperforms them with an accuracy of 90% than the contrasted methods like CNN-LSTM, CNN- GRU and PSO-SVM. As a result, both the classification assessment metrics and resource consumption show that the proposed model performs effectively. Novelty: The novel ensemble approach suggests the combined RF-PSO-CNN for LB in cloud Computing. The task predicted by RF is assigned to the resource chosen by PSO and CNN, thereby improving the efficiency of task prediction and resource allocation. Most of the research uses any two ML or DL methods for either predicting the tasks to be scheduled or which resource to allocate. The study uses a combination of the ML (RF) method, bio-inspired algorithm (PSO) and a DL (CNN) model for both task and resource prediction concurrently and it examines the effectiveness of LB in the cloud context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, Random Forest (RF), Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO)

The paper identifies ways to increase the multiprocessor computing system performance by reorganizing the architecture of its network interface. It is shown that the computational parallelization performance significantly depends on many factors, the most essential of which is the data transfer between the boundary nodes of a multiprocessor system, which is the algorithm's slowest part and can significantly reduce the effect of increasing the number of processors. Hence, it was established that improving the multiprocessor systems performance by reorganizing the network interface structure is relevant interesting nowadays, and its study is at the active development stage. The research aims at the aggregation arrangement of network interface channels in multiprocessor computing systems. It is shown that the main channel aggregation mode advantage is that the data interchange speed is significantly increased, as well as the reliability of the cluster system.

The paper is devoted to the approach development related to methodology definition for evaluation of the modular multiprocessor computing systems efficiency. At the same time, the main attention is focused on the impact peculiarities on this network interface value. The formation analysis of the multiprocessor system network interface architecture and the basic modes of its operation have been analyzed. To evaluate the processes occurring in the system during the information flows transmission, the network system bandwidth and the switch throughput were compared; which allowed determining the preconditions for optimal components selection of the multiprocessor computing system network interface. The performed researches also allowed deducing analytical relations for determining the optimal number of system nodes with different functioning modes. The selected processors coherency coefficient, network interface and value of the computing area are deduced. The derived analytical relationships also showed that the optimal number of blades in a multiprocessor computing system, that provide its highest speed, decreases with increasing computing power of the processors included. It is shown that the network data interchange among the multiprocessor computing system nodes the more likely to impede the overall computation process; the less time will be spent directly on solving a specific problem.

Context. In modern terms problem of constructing of the multiprocessor systems the special value acquires the base of standard popular technologies and components. It is caused by that such systems became popular and cheap vehicle platforms for highperformance calculations. In addition, practice pulls out problems complete decision of which in most cases possibly only due to application of high-performance calculations. Consequently, a theme of constructing of the cluster multiprocessor systems for today is actual, interesting and is on the stage of the active development. At the same time, the new high-quality stage of development of the multiprocessor cluster systems lies in area of the use of new modern network technologies. Presently the problem of choice and analysis of network technologies for the module multiprocessor cluster systems did not get due development, as well as problem of reorganization of structure ofnetwork interfaceby aggregating of channels of network interface.
 Objective. An aim is in-process put improvement of structure and increase of the productivity of the multiprocessor computer system by the multidimensional aggregating of channels of network interface, adapted to the decision of tasks of the investigated class.
 Method. The task of increase of efficiency of the module multiprocessor computer system is decided due to multidimensional aggregating of channels of network interface. Offered approach allowed not only to promote efficiency of parallelization but also substantially to decrease time of calculations. Such results succeeded to be attained due to diminishing to time of border exchange of data between the calculable knots of the cluster system.
 Results. A feature offered approach is that he allowed to realize a direct exchange data between main memory of knots of the multiprocessor system, that promotes the fast-acting of calculations and provides high-speed access to memory of her slave -nodes. Thus during an exchange by data between the knots of the system the system CPU gets unloaded and loading of channel which passes between the knots of the computer system goes down, that assists diminishing of time of border exchange of data between the calculable knots of the system.
 Conclusions. The results of the conducted experiments showed that the worked out multiprocessor system was used for creation of new technological processes. So, she is used in a fluidizer intensification of the сфероидизируещего annealing of long-length steelwork. Directly the technological process of heat treatment of metal acquires such advantages, as a high yield, substantial mionectic energy consumption and allows to carry out control of technological parameters in the modes of unisothermal treatment of metal.

Big Unstructured Mesh Processing on Multiprocessor Computer Systems

At present, multiprocessor systems of a critical nature are widely used. Such systems are used for tracking, aiming, observing, etc. Such tasks, as a rule, require maximizing productivity and reducing the time to solve a problem. For these purposes, the initial selection of non-dependent linear, conditional and cyclic sections of sequential programs is used [1]. This is done to release fragments of programs that can be assigned to execution on processors in such a way that during execution they exchange data with neighboring processors as little as possible. Due to this, it is possible to partially improve the performance of a multiprocessor computing system, together with a decrease in the overall execution time of the entire task as a whole. For systems of the considered nature of the processor of the entire system, it is desirable to assign program fragments so that they are constantly loaded throughout the solution of the entire problem. This is another way to improve the performance of a multiprocessor system. It is obvious that the use of software for this purpose is not real due to the criticality of the time parameter. Therefore, it is relevant to use methods and corresponding hardwareoriented algorithms for scheduling processor loads, which is the subject of research in this article. The article shows the relevance of the constant loading of processors of multiprocessor systems with a high availability factor. The necessity of drawing up a plan for loading processors to support this coefficient is substantiated. An appropriate method and algorithm for multiprocessor systems for critical purposes (tracking systems, surveillance, aiming, etc.) are proposed.

The issues connected with estimating service time of queries (transactions) during the information exchange in multiprocessor systems with a unibus interface and shared memory are analyzed and studied in the article. The article aims at developing and making research of models based on systems and queueing networks, the processor-memory subsystem, as well as estimating the queries service time during the information exchange in multiprocessor systems with shared memory. The subject matter of the study is the analysis of time delays associated with conflict situations occured during the realization of interprocessor exchange when many processors turn to the exchange unibus and memory. The object of the article research is the processor-memory subsystem of existing multiprocessor systems and well-known versions of the architecture of this subsystem . The main task defined by the authors of the scientific article is to develop and make research of mathematical models of the processor-memory subsystem of the mentioned systems and to estimate the processing time of inputting queries during the information exchange in systems with shared memory. Mathematical models for carrying out queries service time research have been proposed. Equations for determining the main probabilistic-temporal characteristics of the processor-memory subsystem have been presented. The mentioned probabilistic-temporal models have been developed using the theory of queueing networks and probability theory. In conclusion the authors make the main judgements about the work done. The mathematical models studied in the article make it possible to estimate the main probabilistic-temporal characteristics of multiprocessor systems without developing real models or prototypes. As a result some effect is achieved, because it is possible to estimate the characteristics of new multiprocessor computer systems and choose the most optimal ones without creating a real expensive system.

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource allocation technique. The levy flight algorithm is a metaheuristic algorithm. It offers high efficiency and success rate because of its longer step length and fast convergence rate. Thus, it treats global optimization problems more efficiently and naturally. A system framework for fog computing is presented, followed by the proposed resource allocation scheme in the fog computing environment. Experimental evaluation and comparison with the firefly algorithm (FA), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm using GA and PSO (GAPSO) have been conducted to validate the effectiveness and efficiency of the proposed algorithm. Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service (QoS). The proposed algorithm reduces average waiting time, average execution time, average turnaround time, processing cost and energy consumption and increases resource utilization and task success rate compared to FA, GAPSO, PSO and GA.

High-Performance Computers and Multiprocessor Computer Systems Development Trends

The paper covers problems of solving tasks of water ecology on multiprocessor computer system (MCS). We proposed a new model of biological rehabilitation of shallow waters in view the factors that have a significant influence on the water quality. Its discretization was performed with using the balance method and the implicit scheme with central differences. The proposed numerical method for the solution of the model problem is most common and is suitable to the study of hydrobiological processes occurring in shallow waters. Since it allows to correctly design computational algorithms on the boundary between the integration domain and environments. One of the objectives of the work is reducing the calculation time and saving the accuracy of the results of solving problem of biological rehabilitation of shallow waters by using a multiprocessor computer system. Two algorithms have been developed in the implementation of the parallel algorithm for solving problem on the MCS for the distribution of data between the processors. There is the algorithm on the basis of the k-means method, based on the minimization of the functional of the total sample variance of scatter elements about the center of gravity of the subdomains, which allows increasing the efficiency of the parallel algorithm of the hydrobiology problem of shallow water. Using the MCS can significantly reduce the calculation time while saving the accuracy of the solution. The latter fact provides the fast and qualitative interpretation of hydrobiological data.

An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm

Pipeline and Multiprocessor computer organizations potentially offer attractive computational gains along many design dimensions. This paper describes the results of an analytic study of pipeline and multiprocessor computer systems to obtain bounds on the relative performance of designs. It investigates the effects of the depth of pipeline and of multiprocessing on performance of systems. It is shown that the optimum payoff in design of pipelined machines occurs with pipes that have only a few instructions of overlap. It is demonstrated that Multiprocessor systems may be effectively constructed from structures that exhibit lesser degree of tight coupling.

The tasks connected with the construction of a dynamic model of real-time systems (RTSs) are considered on the basis of two models: a model of the program load and a model of the multiprocessor computing system (MCS). Attention is focused on a problem concerning model coordination for a uniform dynamic model of projected system reception. The optimization statement of a resource-use plan-finding task by means of a criterion on the minimal total volume of the data sent in a local-network MCS is given as the task of graph cutting on minimally-connected subgraphs, and the search technique for this decision is offered. The technique of a variant-choice, economically-preferable criterion for the MCS architecture is offered, based on two basic parameters $the number of stations in the MCS and the delay time in performing the applied functions.