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Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework's performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers. [ABSTRACT FROM AUTHOR]

CitcomCu is a numerical simulation software for mantle convection in the field of geodynamics, which can simulate thermo-chemical convection in a three-dimensional domain. Due to the increasing demand for high-precision simulations and larger application scales, larger-scale computing systems are needed to solve this problem. However, the parallel efficiency of CitcomCu on large-scale heterogeneous parallel computing systems is difficult to improve, especially it cannot adapt to the current mainstream heterogeneous high-performance computing architecture with CPUs and accelerators. In this paper, we propose an geodynamics numerical simulation parallel computing framework using heterogeneous computing architecture based on the Tianhe new-generation high-performance computer. Firstly, the data partitioning mode of CitcomCu was optimized based on the large-scale heterogeneous computing architecture to reduce the overall communication overhead. Secondly, the iterative solution algorithm of CitcomCu was improved to speed up the solution process. Finally, the NEON instruction set based on SIMD is used for the sparse matrix operations in the solution process to improve parallel efficiency. Based on our parallel computing framework, the optimized CitcomCu was deployed and tested on the Tianhe new-generation high-performance computer. Experimental data showed that the performance of the optimized program was 3.3975 times higher than that of the unoptimized program on a single node. Compared with 50,000 computational cores, the parallel efficiency of the unoptimized program on one million computational cores was 36.75%, while the parallel efficiency of the optimized program was improved by 16.22% and reached 42.71%. In addition, the optimized program can be executed on 40 million computational cores, with a parallel efficiency of 36.54%. [ABSTRACT FROM AUTHOR]

According to estimates, between 10 and 25% of the grain crop is lost in the post-harvest. The correct drying of the beans is one of the actions to contain this loss. Drying grains is one of the most critical steps in grain processing for its proper conservation after harvest. As the grain mass is a set of solid and empty spaces (holes) through which a fluid can pass, its drying could be considered a problem of the coupled open-porous medium. In this paper, we propose a mathematical and computer simulation model which describes the convection in a free flow with a porous obstacle applied to the grain drying processing. A computational fluid dynamics scheme was implemented in FORTRAN using Finite Volume to simulate and compute the numerical solutions. The code is parallel implemented using OpenMP (loop and teams approach) and OpenACC programming interfaces. We use three case studies with different mesh sizes to evaluate the implementations. As a result, there was a reduction in processing time in the cases. The total simulation time for a multicore architecture (16 physical cores) was 6,14 times less using parallel loops and 8.5 using parallel teams, and 8.38 using a single GPU (Quadro M5000). [ABSTRACT FROM AUTHOR]

The low-rank matrix completion problem has aroused notable attention in various fields, such as engineering and applied sciences. The classical methods approximate the rank minimization problem by minimizing the nuclear norm, therefore obtaining unsatisfactory results, which may deviate from the true solution. In addition, most methods minimize the square error directly, which may be sensitive to the outliers. This paper presents a robust matrix completion model, which is suitable for a low sampling rate. First, the truncated nuclear norm is introduced, which is a more accurate and robust approximation to the rank function. Then, the Lp-norm may be employed as an error function, which provides a robust estimation. Finally, several optimization algorithms are employed to solve the model. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed method. Notably, the algorithm can better approximate rank minimization problems and enhance robustness to outliers, especially when the sampling rate is very low. The method's practical potential is illustrated on the MovieLens-1M dataset. [ABSTRACT FROM AUTHOR]

The task's slack is the key issue to reduce the energy consumed by DVFS-enabled computing platforms. Despite the large number of scheduling algorithms that are presented in the literature, only a unique scaling axiomatic approach (SAA) is utilized in the scaling phase of the algorithms. SAA simply extends the execution of the task within its slack if a suitable scaling frequency is available. Unfortunately, when dependent-tasks applications are scheduled on such platforms, scheduling algorithms minimize the tasks' slacks to reduce the overall completion time of the application tasks. This paper presents a mechanism that can be applied to any schedule produced by a dependent-task scheduling algorithm for both homogeneous and heterogeneous DVFS-enabled computing platforms. The proposed mechanism is called BlackLight. BlackLight attempts to extend the tasks' slacks via rescheduling the application tasks without violating the overall completion time of the application tasks. The proposed mechanism is applied to a large number of dependent-tasks schedules of both random generated application graphs and two real-world application graphs. The experimental results based on a computer simulation show that the proposed mechanism significantly extends the tasks' slacks compared with SAA , which leads to more reduction in the consumed energy. [ABSTRACT FROM AUTHOR]

A coupled nonlinear prey–predator system is presented. The system formulation is based on nonlinear ordinary differential equations with imprecise parameter values. In this paper, we find the equilibrium point and conduct a stability analysis of the prey–predator model using a Lyapunov functional. A comparison of our approximate analytical expressions with numerical simulation using MATLAB software is also presented. Furthermore, the proposed mathematical model is solved analytically by using the VIM and HPM for all possible parameter values in their specified ranges. Excellent agreement is noted on comparisons between the analytical and numerical results. [ABSTRACT FROM AUTHOR]

The popularity of diverse IoT-based applications and services continuously generating tremendous amount of data has revealed the significance of data compression (DC). Principal component analysis (PCA) is one of the most commonly employed algorithms for DC. However, when dealing with large-scale matrices, the standard PCA takes a very long time and requires a lot of memory. Therefore, this paper presents a novel distributed stochastic PCA algorithm (DSPCA) for hierarchical sensor network based on gradient-based adaptive PCA (GA-PCA), where the standard PCA is reformulated as a single-pass stochastic setting to find the direction of approximate maximal variance. The step-size in each iteration is obtained by incorporating the stabilized Barzilai-Borwein method with the gradient optimization. This enables DSPCA to be processed with low computational complexity while maintaining a high convergence speed. Computer simulation with two types of datasets displays that the proposed scheme consistently outperforms the representative DC schemes in terms of reconstruction accuracy of original data and explained variance. [ABSTRACT FROM AUTHOR]

An electromagnetic flow sensor applied to the flow measurement of iron ore pipeline transportation will incur a measurement error after being used for a period of time, and the error will increase continuously. In this paper, based on the weight function theory of electromagnetic flow sensors, the main cause of the error is proposed. Specifically, in the process of using the electromagnetic flow meter the insulation lining of the sensor is easily damaged because the measuring pipe of the sensor constantly collides with the solid phase of the fluid, leading to a change in the distribution of the weight function of the electromagnetic flow sensor, which, in turn, changes the contribution of the induced electromotive force generated by each medium in the pipe to the measuring electrode. This paper is based on the PDE computer numerical calculation method, and, according to the analysis of the weight function under different boundary conditions, the rule that the output signal of the electromagnetic flow sensor decreases with increasing insulation lining area is obtained. The computer numerical calculation method used in this paper can provide a new approach to the theoretical analysis of the weight function of electromagnetic flow sensors. Furthermore, the conclusion reached in this paper can provide a theoretical basis for the development and maintenance of electromagnetic flow sensors applied to the flow measurement of iron ore pipelines. [ABSTRACT FROM AUTHOR]

This paper takes the risk management and countering strategies of a project as the basis of theoretical knowledge, IT modeling and simulation technology. By combining this approach with the processes of large international construction projects, we specifically research the political risk, natural risk, economic risk, design risk and risk of construction preparation in the project approval and early preparation stages. We also emphasize the risk identification, risk estimation, risk evaluation, risk response, risk monitoring and risk management decisions for the importance of the practice of large international projects and subsequently indicate the primary issues and countering strategies in international project risk management that should receive attention during a construction project. [ABSTRACT FROM AUTHOR]

The system dynamic model (SD model) can simulate the dynamic change of the system from the overall perspective and carry out dynamic analysis of the structure, behavior and causality of the system by computer simulation under different assumptions. Therefore, to solve the problem of tripartite game between the government, private sector and owners which caused by the application of PPP model in the old community renovation, this paper establishes a three-party game model based on the evolutionary game theory and simulates the evolvement path of each participant by establishing SD Model with relevant computer Software. The research finds that: (1) the government, the community owners and the private sectors will eventually reach a stable and balanced state (supervision, participation, effort); (2) the government is dominant in the renovation, and the strategic choice of the private sectors and the community owners depends on the government's strategy, the variables determined by the government and the relationship between these exogenous variables; (3) although a single variable could influence the strategic choice of each party, the formation of the final stable state depends on the interaction of multiple variables, and the government should handle the relationship between each stakeholders. [ABSTRACT FROM AUTHOR]

The paper presents a three-dimensional cellular automaton model of electrochemical oxidation of the carbon. The sample of the electro-conductive carbon black "Ketjenblack EC-600JD" consisting of granules of carbon is simulated. The electrochemical oxidation of the carbon granules occurs through a few successive stages. Parallel implementation of the three-dimensional cellular automaton model of carbon corrosion is developed. The efficiency and speedup of the parallel code are analyzed. The portions of surface carbon atoms and atoms with different degree of oxidation are computed by the parallel code. Based on the obtained values of atom portions the electrochemical capacity is calculated. The results of computer simulation are compared with the experimental data. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is an efficient machine learning technique widely applied to various classification problems due to its robustness. However, the training time grows dramatically as the number of training data increases. As a result, the applicability of SVM to large-scale datasets is somewhat limited. In SVM, only a few training samples called support vectors (SVs) affect the construction of hyperplane. Therefore, removing training data irrelevant to the SVs does not degrade the performance of SVM. In this paper the clustering-based convex hull (CBCH) scheme is introduced which allows to efficiently remove insignificant data and thereby reduce the training time of SVM. The CBCH scheme initially applies k-mean clustering algorithm to the given training data points, and then, the convex hull of each cluster is obtained. Only the vertices of the convex hulls and the data points relevant to the SVs are included as training data points. Computer simulation over various sizes and types of datasets reveals that the proposed scheme is considerably faster and more accurate than the existing SVM classifiers. The proposed algorithm is based on geometric interpretation of the SVM and applicable to both linearly separable and linearly inseparable datasets. [ABSTRACT FROM AUTHOR]

In many network applications such as surveillance systems, it is crucial to detect the target and estimate its location. Distributed processing algorithms are capable of providing fast, secure, scalable and robust solutions. In this paper, we study the problem of target detection and localization in a wireless sensor network. Most of the current researches have focused on centralized algorithms and the works done on distributed algorithms usually need center assistance and practical issues such as communication link failure is not addressed in them. In this paper, we first propose a distributed consensus-based algorithm for target detection and then propose a distributed consensus-based localization algorithm. We assume that the target transmits a radio signal that is received in sensors equipped with limited computational and power resources. We consider the communication link failure and use the collaboration of sensor nodes to detect the presence of target. In the proposed target localization algorithm, sensor nodes estimate their distance toward the target using the received signal strength. In both the proposed algorithms, sensor nodes exchange information only with their neighbors and each makes an individual decision. We further prove the convergence of the proposed algorithms. Computer simulations confirm that the proposed algorithms are very fast and applicable in high-performance networks. We improve the localization accuracy at least by 25 % in terms of localization error compared with some recent algorithms. [ABSTRACT FROM AUTHOR]

System-level description languages in electronic system level are domain-specific sequential simulation languages using the shared-everything model in discrete-event modeling and simulation terminology. They implement sequential process-interaction worldview to take advantage of event partitioning for ease of programming and modularity. Therefore, their reference simulators can only be executed on a single physical core. Fast and accurate simulation is highly desirable for efficient and effective system design due to the ever-increasing complexity of embedded and cyber physical systems. Parallel discrete-event simulation (PDES) is the main technique to solve this problem for large-scale system-level models. PDES works based on state space partitioning by using the so-called logical process worldview. This paper proposes parallel system modeling and simulation language (PSML), along with its formalized distributed parallel simulation kernel, that provides execution of hardware models in order to improve simulation speed significantly. It will be shown that the proposed framework results in linear, super-linear speedups ranging from 11 × to 32 × for large-scale, complex PSML models in comparison with the SystemC reference simulator on a 12-core host. [ABSTRACT FROM AUTHOR]

This paper presents a human–computer cooperation platform, which permits the coordination between the user and the tool to improve the development of real-time control applications (e.g., mobile robots). These applications have functional (robot objectives) and temporal requirements to accomplish (deadlines guarantee of tasks). The simulation tool has been designed in order to permit the testing and validation of these two requirements. To this end, the tool is composed of two independent simulators interconnected through a shared memory: the robot simulator (functional level) and the real-time task scheduler simulator (task execution level). Robotic applications can be defined with the robot simulator while the real-time scheduler simulator permits to analyze the schedulability of the robotic tasks. The real-time task simulator incorporates a flexible task model where the task temporal parameters (e.g., computation time) adapt to the requirements of the application (e.g., number of objects in scenes); thus, the use of the CPU is not overestimated. A key issue of the framework is the human–computer interface, which allows the monitoring of different parameters of the application: robot objectives, task schedule, robot speed, computation time, CPU utilization, deadline misses. The usefulness of the simulation tool is shown through different robotic navigation experiments. Finally, the simulation tool has been used to evaluate the proposed flexible model of tasks compared to a traditional fixed temporal parameters task model. Results show that the robot fulfills the objectives earlier, about 32% on average, and consumes on average about 15% less CPU to accomplish the objectives. [ABSTRACT FROM AUTHOR]

In trustworthy service discovery for Mobile Social Networking in Proximity (MSNP), conventional trust computation faces a big challenging issue—relatively high latency. To cope with it, trustworthiness determination strategies were proposed in our previous study, aiming at avoiding trust computation under certain conditions, so as to reduce the latency. These strategies are conceived based on the assumption that data of a user's past experience and current profile could be used, and they are incorporated with a set of thresholds from the analysis result of these data. The settings of these thresholds directly affect service quality and user satisfaction on the MSNP service, which in turn becomes an optimization problem. In this paper, we focus on formulating this optimization problem and demonstrating the effectiveness of our proposed solution by designing a simulation experiment. In detail, we establish mathematical models and adjust parameters. We conduct simulations on MATLAB and analyze the results obtained under several different settings. We further compare our work with related works. The results show that our proposed solution is practically feasible and effective in reducing latency under certain conditions. [ABSTRACT FROM AUTHOR]

This paper presents a flexible and scalable network simulation framework (FS-IIoTSim) for performance evaluation of the Industrial Internet of Things (IIoT) system. FS-IIoTSim provides flexibility and scalability for performance analysis of the IIoT system by supporting not only existing legacy industrial sensor network communication models, but also user protocol modification, in order to accommodate the complexity of systems such as communication standards, smart machine traffic, and applications over the complex IIoT environment. The FS-IIoTSim consists of three parts: (1) scenario modeling, (2) performance evaluation, and (3) user interface. Each of these parts interacts and cooperates with each other to simulate a target IIoT system. Scenario modeling utilizes user simulation scenarios to create an industrial sensor network (ISN) system model and perform simulations to generate a trace file recorded with log information about the device’s operations. Performance evaluation is an analytical process of a trace file, generated in the scenario modeling part, which is used to derive a user’s preferred performance metric. The user interface part plays a role in interfacing with the detailed operations of the other parts. Within the user interface part, the user can set and control the FS-IIoTSim framework. In order to prove the effectiveness of this FS-IIoTSim, experiments were conducted in various industrial application scenarios, and simulation performance was compared and analyzed with a dedicated simulator. The experimental results showed that FS-IIoTSim provided more effective approaches to modeling and performance analysis of real ISN environments, compared to a dedicated simulator. [ABSTRACT FROM AUTHOR]

Many fault-tolerant routing algorithms for Network-on-Chip (NoC) are developed to overcome faulty components and provide reliable transmission. However, few works have considered the traffic load around the faulty region. To solve this problem, this paper presents a Fault-Tolerant Odd-Even (FTOE) turn model and Load-Balancing Fault-Tolerant (LBFT) routing algorithm that do not use virtual channels for 2D mesh NoC. Applying FTOE rules and routing packets around faulty regions in advance, LBFT balances the traffic load around the faulty regions, and then, the fault-tolerant paths are shortened. From theory analysis, LBFT is proved to be deadlock and livelock free. Simulation results show that LBFT’s saturation throughput can be improved than related works by 5.38 and 14.23%, and the power consumption can be reduced than related works by 2 and 5%, respectively. [ABSTRACT FROM AUTHOR]

The high density, low power consumption non-volatile memory (NVM) provides a promising DRAM alternative for the in-memory big-data processing applications, e.g., Spark, It is significant to simulate the behaviors when NVMs are deployed into the area of big-data processing before their widespread use in market. However, existing simulation approaches are not applicable for big-data processing due to two reasons. First, some approaches require complicated hardware and/or OS supports. Second, cycle-level or function-level simulations are too time-consuming to simulate the whole software stack of big-data processing. Therefore, the complexity and expensive time cost in NVM simulation have dramatically dragged down the integrated research of big data with NVM. This paper proposes a fast and reconfigurable simulation method, called NVM Streaker, which does not need complex hardware or OS supports. It simulates NVM access costs using disturbed DRAM accesses and commonly configurable hardware parameters. It is fast since we use DRAM accesses and change its access costs to simulate NVM access costs, thus enabling to simulate the whole software stack to run Spark applications. It is reconfigurable since we enable users to configure the disturbed memory access costs, in order to simulate different NVM access costs. The experimental results show that we can simulate Spark applications with almost negligible cost and high efficiency. [ABSTRACT FROM AUTHOR]

Depth estimation is one of the essential components in many computer vision applications such as 3D scene reconstruction and stereo-based object detection. In such applications, the overall quality of the system highly depends on the quality of depth maps. Recently, several depth estimation methods based on semi-global matching (SGM) have been proposed because SGM provides a good trade-off between runtime and accuracy in depth estimation. In addition, non-parametric matching costs have drawn a lot of attention because they tolerate all radiometric distortions. In this paper, a depth estimation method based on the Census transform with adaptive window patterns and stripe-based optimization has been proposed. For finding the most accurate depth value, adaptive length optimization paths via multiple stripes are used. A modified cross-based cost aggregation technique is proposed which adaptively creates the shape of the cross for each pixel distinctly. In addition, a depth refinement algorithm is proposed which fills the holes of the estimated depth map using the surrounding background depth pixels and sharpens the object boundaries by applying a trilateral filter to the generated depth map. The trilateral filter uses the curvature of pixels as well as texture and depth information to sharpen the edges. Simulation results indicate that the proposed methods enhance the quality of depth maps while reducing the computational complexity compared to the existing SGM-based depth estimation methods. [ABSTRACT FROM AUTHOR]

This paper proposes a method to reduce overhead packets and end-to-end delay and improve reachability over an ad hoc network that uses a location-aided routing protocol. Location-aided routing is useful in emergency situations where both the infrastructure network and consistent address assignment mechanisms do not work. The existing location-aided routing protocols, however, have several defects such as needing many overhead packets to capture the location information, and difficulty in handling void zones. The proposed technique consists of location-aided route discovery based on a two-hop neighbor information and forwarding mechanism. By knowing more about the location of other nodes, the proposal can solve the existing problems. This paper compares our approaches with existing approaches through computer simulation and analysis, showing that our proposed method achieves better performance than those of the existing approaches. [ABSTRACT FROM AUTHOR]

Recent advances in neuroscience have shown that the neuropathological disorders are closely related with diseases such as Alzheimers. Those damages are particularly associated with the intermediate visual perceptual processing which can cause the motion perception defects and abnormal visuospatial functions in daily living of patients. In this paper, we propose virtual reality-based assessment tools for measuring human perceptual sensitivity to dynamic erroneous motions, particularly designed to assess possible early stage of brain damages and its associated visual dysfunctions. The main thrust of this paper is on perceptually tuned virtual reality system that can produce realistic natural behavior. The proposed method contains multiple assessment layers to check the awareness of erroneous motion in natural scenes at various severities. Our VR-based game-type environment provides an effective test bed for various dynamic motion-based perceptual sensitivity experiments. Our initial human subject tests show that game-based test environment produces more coherent and consistent data, preferable to survey-based methods. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become one of the most important fields in computing arena. The environments of IoT require highly efficient, immediate and worldwide communication services. Accordingly, efficient multicast routing architecture is a fundamental premise for IoT. This paper proposes a mixed multicast architecture for IoT environments that employs the centric, hierarchical, and distributed traditional multicast architectures. The aim is to determine the most suitable traditional multicast architecture, relative to the current state of the IoT system. First, an algorithm to manage the proposed multicast architecture is introduced. Then, an IoT case study for each traditional multicast architecture is demonstrated. Finally, a simulation environment is established, using the network simulator package NS2, to measure the performance of the proposed architecture. The considered performance metrics are end-to-end delay, packet loss, throughput, energy consumption, and transformation rate between traditional multicast architectures. The results demonstrate the superiority of the proposed architecture relative to individual traditional multicast architectures. [ABSTRACT FROM AUTHOR]

Stimulated Brillouin scattering (SBS) is one of the most important nonlinear phenomena because it limits the maximum transmission power in modern optical communication systems. Unfortunately, the simulation of SBS is time-consuming, since it requires estimating the solutions for a set of complex differential equations that describe this phenomenon. In this paper, a novel high-performance computing model intended to analyze the main dispersive effects present in modern fiber communication systems is proposed. A full optical characterization of the simulation results is included and compared with the efficiency and improved speed of our parallel implementation versus a previous sequential model for SBS. Also, an evaluation of the throughput of our parallel implementation using both central processing unit multi-core and graphics processing unit is presented. Results show that parallelism increases the performance of the simulation tenfold. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a network architecture which tries to provide communication services in clouds through virtualization techniques. Actually, NFV combines server and service and replaces a lot of network devices. NFV deploys software applications instead of hardware devices and therefore reduces network provider’s financial costs and facilities manageability. One of the services that NFVs present is virtualized firewalls in clouds. As other services in clouds, firewalls should be dynamically scaled to the needs of any business and adapt as demands increase. In this paper, a method is proposed for dynamic auto-scalability of the firewall service in cloud environments. The proposed method also balances incoming load among different virtualized firewalls which are installed as a software on virtual machines and are located in one pool. We consider a queuing model for each virtual machine. The goal here is to determine the number of active virtualized firewalls required in different time steps according to the intensity of incoming load and the proportion of total requests that goes to each firewall. Decisions are made regarding the utilization of firewall virtual machines so that QoS requirements can be met; at the same time, the resources will be saved in order to balance the performance with the cost of allocated firewall virtual machines. To solve the problem, we propose a hybrid genetic algorithm and reinforcement learning-based approach, namely GARLAS (genetic algorithm and reinforcement learning-based autonomic scaling), implemented in a cloud manager. The results of simulation with MATLAB on different realistic workloads demonstrate that the approach is able to find an optimal policy in both scalability and load balancing aspects. Also, it leads to 87.91 and 85.15% of lower average response time and 9.93 and 11.77% of improvement in utilization in comparison with static and threshold-based approaches, respectively. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) has an enormous computational complexity which has confined its practical applications. In this paper, we present an implementation based on field programmable gate array (FPGA) for the most computationally intensive portion of STAP, which is the adaptive weights calculation. This involves solving a set of linear equations that uses the radar return data. In the proposed architecture, QR decomposition block is the most computationally part which is parameterized by vector size to create a trade-off between the hardware resources consumption and delay. To achieve an efficient and high-speed structure, the architecture is simulated and implemented in two cases: single-vector and multi-vector. Results show that the calculation time of weights in single-vector design is less than that of multi-vector case. The delay of weights for 6 × 8 × 120 data cube using vector size of 17 and the maximum clock frequency of 259 MHz is 139 μs, and GFLOPs/Watt is 3.89 for implementation on Arria 10 floating point FPGA. Therefore, the presented approach can realize the real-time requirement and floating point computation of the adaptive weights for STAP. [ABSTRACT FROM AUTHOR]

To mitigate the interference in co-located wireless body area networks (WBANs), this paper proposes an inter-WBAN priority-based capacity allocation scheme based on the Nash bargaining game, and an intra-WBAN priority-based power control scheme based on the Stackelberg game. Moreover, under the network capacity imposed by the Nash bargaining solution, two pricing mechanisms: non-uniform pricing and uniform pricing, are introduced in the Stackelberg game and the Stackelberg equilibrium under each mechanism is achieved analytically. Additionally, owing to the special features of WBANs, the players priorities indicated by exigency of the sensed data and the energy consumption of sensors are considered in the design of utility functions. Extensive simulations show that the proposed schemes are energy efficient and can improve the network quality of service in terms of real time and reliability of critical data transmission. [ABSTRACT FROM AUTHOR]

The increasing demand on efficient intra-chip communication of multicore systems has driven the interconnection structure to evolve from bus/ring to Network-on-Chip (NoC). NoC design is fundamentally based on network topology generation and floorplanning. This paper proposes a joint optimization method to generate network topologies based on the floorplanning of heterogeneous IP cores for a given application-specific NoC. This method starts with clustering the heterogeneous IP cores according to their communication workloads using fuzzy clustering. It proceeds to apply a genetic algorithm to optimize the floorplanning by minimizing power consumption and/or chip area. By adding a router to each cluster of IP cores, network topologies are further generated via connecting routers based on the principles of scale-free networks. Experiments with a video processing application show that the optimized floorplanning of IP cores can be achieved by either minimizing the power consumption or chip area. An OPNET simulator is used to evaluate the performance of the NoC designed based on the proposed method. Experimental results demonstrate that the performance requirements of the application-specific NoC can be satisfied. [ABSTRACT FROM AUTHOR]

When a tsunami occurred on a sea area, prediction of its arrival time is critical for evacuating people from the coastal area. There are many problems related to tsunami to be solved for reducing negative effects of this serious disaster. Numerical modeling of tsunami wave propagation is a computationally intensive problem which needs to accelerate its calculations by parallel processing. The method of splitting tsunami (MOST) is one of the well-known numerical solvers for tsunami modeling. We have developed a tsunami propagation code based on MOST algorithm and implemented different parallel optimizations for GPU and FPGA. In the latest study, we have the best performance of OpenCL kernel which is implemented tsunami simulation on AMD Radeon 280X GPU. This paper targets on design and evaluation on FPGA using OpenCL. The performance on FPGA design generated automatically by Altera offline compiler follows the results of GPU by several kernel modifications. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand Internet-based computing service, where computing resources are shared among the users via the Internet and its usage based on the pay-for-use model. Virtualization of computing resources allows the system to use the resources efficiently. One of the challenging issues in virtualization is the placement of virtual machine (VM) on the physical machines (PMs) in order to utilize computing resources efficiently. Furthermore, imbalanced usage of resources also leads to overall resource wastage of an IaaS cloud. In this paper, we propose a new VM placement algorithm called RVMP for IaaS cloud. The first objective of the proposed algorithm is to minimize the power consumption of the IaaS cloud by reducing the number of active PMs. We devise a new technique called resource usage factor to place a VM on a suitable PM so that resources of the PM can utilize efficiently. The second objective is to minimize the unbalanced utilization of resources among the active PMs. We propose a new resource usage model by which one can successfully figure out unbalanced utilization of resources on the active PMs. By using the proposed model, we adopt a limited migration of VMs to minimize the unbalanced utilization of resources. Finally, the proposed algorithm is compared with the existing algorithms in terms of various performance metrics. The simulation results demonstrate the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As flash memory becomes popular, flash memory-based solid-state drive (SSD) has been the major storage device. SSD has numerous merits such as high I/O speed, low energy consumption, strong shock resistance and small form factor. Meanwhile, some shortcomings still exist including erase-before-write and different cost for read, write and erase operation. Aiming at efficient buffer management of SSD, this paper proposes a novel approach based on particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the Predict Hot Fitness value of each logical page in the buffer to correctly identify them either as hot or cold by properly reflecting the spatial and temporal locality. The pages predicted as hot are kept in the buffer to maximize the hit ratio and utilization of the SSD buffer. Simulation with real workload shows that the proposed scheme significantly outperforms the representative SSD buffer management schemes in terms of hit ratio and throughput. [ABSTRACT FROM AUTHOR]

Durable P2P data storage is a research focus in recent years. It aims at providing data for requesters anytime and anywhere. Considering the fact that any peer could join and leave P2P network freely and randomly, how to improve the data availability is a crucial issue when implementing a P2P data storage scheme. To guarantee data availability, data replication and replica management have been found as the effective strategies by distributing the replicas in different nodes. However, how to determine the data redundancy is a key issue which should be solved carefully. In this paper, according to the existing researches, we first calculate peers' online probabilities in each time interval, and then we group the peers which have the complementary online patterns to create the so-called generalized peers with high online probability in each time interval. With the created generalized peers, we present the durable P2P data storage scheme. The simulation results show that our P2P data storage scheme outperforms the existing strategies in terms of the data retrieval success rate and the response time in dynamic P2P networks. [ABSTRACT FROM AUTHOR]

Content-centric network (CCN) has been considered for VANETs, due to its scalability, flexibility and security. In this paper, an extended CCN architecture is proposed to reduce critical data access time in vehicular content centric networks by storing them in the number of vehicles around the source. The critical data include sensitive data such as accidents, traffic that require quick and reliable access. The aim is to reduce access time for this type of data in the discovery phase. Each vehicle stores or updates critical data in cooperation with other vehicles in the preparation phase. During the preparation phase, the critical data spread around the source by a combination of physical movement and radio propagation. This gives the requesting vehicle a better chance to find the data in a few hops, with minimal network overhead and lower delay, during the discovery phase. The efficiency of this architecture is evaluated by simulation and shown that interest packet delay and number of duplicated data packets are generally lower than the existing methods. [ABSTRACT FROM AUTHOR]

In recent years, cloud services have been used not only as storage but also to increase computational power. Cloud computing systems perform computations using distributed computation across a large number of computers connected to the Internet. Therefore, cloud computing systems are designed to compute correct results even if failures occur on some of the computers. Many cloud computing systems have mechanisms for redundant calculation for this purpose, which makes it difficult to estimate the computing power of cloud services. In this paper, we propose a method that can estimate the computing power of cloud computing systems. First, we define a simple model of cloud computation. Assumptions about hardware and software control of the model are shown. We introduce a concept called the round number. We show that the round number is crucial for the overall computation time, and derive a formula for the distribution function of the round number. We show that the overall computation time of cloud computing systems can be approximated using the distribution function of the round number combined with an approximation of the frequency of each particular round number's computation time. Simulation of distributed computation is performed for an experimental problem, and the validity of the proposed method is confirmed. [ABSTRACT FROM AUTHOR]

As a typical Gauss-Seidel method, the inherent strong data dependency of lower-upper symmetric Gauss-Seidel (LU-SGS) poses tough challenges for shared-memory parallelization. On early multi-core processors, the pipelined parallel LU-SGS approach achieves promising scalability. However, on emerging many-core processors such as Xeon Phi, experience from our in-house high-order CFD program show that the parallel efficiency drops dramatically to less than 25%. In this paper, we model and analyze the performance of the pipelined parallel LU-SGS algorithm, present a two-level pipeline (TL-Pipeline) approach using nested OpenMP to further exploit fine-grained parallelisms and mitigate the parallel performance bottlenecks. Our TL-Pipeline approach achieves 20% performance gains for a regular problem $$(256\times 256\times 256)$$ on Xeon Phi. We also discuss some practical problems including domain decomposition and algorithm parameters tuning for realistic CFD simulations. Generally, our work is applicable to the shared-memory parallelization of all Gauss-Seidel like methods with intrinsic strong data dependency. [ABSTRACT FROM AUTHOR]

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees-loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min-max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Due to a growing interest in chemical and biological phenomena, simulation of reaction-diffusion processes on micro level becomes urgently wanted. Asynchronous cellular automata (ACA) are promising mathematical models to be used as a base for creating computer simulation programs, which gives reason for investigation of the models capability. In particular, micro-level simulation requires to deal with very large ACA size. So, parallel implementation is inevitable, and, hence, achieving good parallelization efficiency is essential. Since parallelization efficiency depends on stochasticity (the degree of randomness) of the process under simulation, it is important to investigate their relations in order to create methods of developing ACA models with proper stochasticity values. In the paper the interrelation between stochasticity and parallelization efficiency is studied in the context of reaction-diffusion processes simulation on supercomputer with distributed memory. The results are illustrated by simulation a Large-scale process of wave front propagation. [ABSTRACT FROM AUTHOR]

Simulation is often used in order to evaluate the behavior and the performance of computing systems. Specifically, in the field of high-performance interconnection networks for HPC clusters the simulation has been extensively considered to verify and validate network operation models and to evaluate their performance. Nevertheless, experiments conducted to evaluate network performance using simulation tools should be fed with realistic network traffic from real benchmarks and/or applications. This approach has grown in popularity because it allows to evaluate the simulation model under realistic traffic situations. In this paper, we propose a family of tools for modeling realistic workloads which capture the behavior of MPI applications into self-related traces called VEF traces. The main novelty of this approach is that it replays the MPI collective operations with their corresponding messages, offering an MPI message-based task simulation framework. The proposed framework neither provides a network simulator nor depends on any specific simulation platform. Besides, this framework allows us to use the generated traces by any third-party network simulator working at message level. [ABSTRACT FROM AUTHOR]

A novel bio-inspired optimization algorithm called elephant search algorithm (ESA) has been applied to solve NP-hard problems including the classical traveling salesman problem (TS) in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the behavioral characteristics of elephant herds; hence, the name Elephant Search Algorithm which divides the search agents into two groups representing the dual search patterns. The male elephants are search agents that outreach to different dimensions of search space afar; the female elephants form groups of search agents doing local search at certain close proximities. By computer simulation, ESA is shown to outperform other metaheuristic algorithms over the popular benchmarking optimization functions which are NP-hard in nature. In terms of fitness values in optimization, ESA is ranked after Firefly algorithm showing superior performance over the other ones. The performance of ESA is most stable when compared to all other metaheuristic algorithms. When ESA is applied to the traveling salesman problem, different ratios of gender groups yield different results. Overall, ESA is shown to be capable of providing approximate solutions in TSP. [ABSTRACT FROM AUTHOR]

Information fusion is a very important technology which can use multisensor network data to get a better performance than single one; therefore, it is widely used in the filed of target recognition, target tracking, automatic control, decision making and so on. However, because of noise and interference, sometimes the sensors may obtain erroneous, inaccurate or heterogeneous data, which will produce the conflict information among different sensors and get the wrong result after information fusion. In this paper, based on the Dempster-Shafer (D-S) theory, we introduce how to set up the model of multisensor network information fusion. And then, we discuss the problem of conflict information fusion in the framework of evidence and several improved methods are introduced. Finally, based on Mahalanobis distance, an improved solution method is presented. The numerical simulation results prove that this new improved method can get the same result as traditional methods, beyond which it can make a reasonable decision with high conflict information. Therefore, this new improved method can be used in the filed of high noise and interference. [ABSTRACT FROM AUTHOR]

We are studying a daily life-support system for the general public that consists of a community care system (CCS) application and community information network (CIN). CIN provides CCS with connectivity between an individual's smartphone and city government servers, and at the same time it provides access to not only members of the public but also Internet visitors. This paper describes the concept of the CCS, the prototype development of one of the CCS functions (services), and the network performance of broadcast and unicast communication among APs for a model of CIN using network simulation. [ABSTRACT FROM AUTHOR]

Existing communication infrastructures may be unavailable in the aftermath of a disaster. Under these circumstances, it is difficult to share information, such as text, image, and audio data, in the areas where the communication infrastructure is unavailable. Since these contents are usually composed of multiple packets, existing communication methods cannot be applied to share them. To enable information sharing without using the existing communication infrastructure in these areas, we have proposed a novel system 'Broadcast-Based Information Sharing System (BBISS)'. The paper evaluates the performance of BBISS using network simulations. It can be concluded from the simulation results that the proposed method achieves high information reachability without significantly increasing the number of packet exchanges. Also, we optimize the parameters for BBISS. [ABSTRACT FROM AUTHOR]

In this paper, several efficient migration algorithms have been proposed to improve existing non-preemptive sub-mesh allocation strategies in multiprocessors which are two-row boundary migration algorithm (TRBMA) and different types of combined migration mechanisms. These methods are presented to solve the external fragmentation problem in contiguous allocation. In this process, the mentioned methods contain the algorithms, called the minimum threshold and prime number (MT&MPN), which determine the best sub-mesh size for the incoming job. These algorithms are used to increase continuity in multiprocessor systems. Using extensive simulations, we evaluated the proposed methods and compared them with previous strategies. Simulation outcomes clearly showed the TRBMA and the MT&MPN procedure produced the best average job execution time and average system utilization. Also, hybrid migration methods produced the best average job waiting time and average number of waiting jobs in comparison with the previous algorithms. Meanwhile, most of the traditional methods had better average job response time. [ABSTRACT FROM AUTHOR]

A mobile ad hoc network (MANET) has been studied in order to achieve network capability in disaster situations. However, MANET has many issues to be addressed. One of the issues is security. For example, MANET is vulnerable to network sniffing because it uses wireless communication, and also existing security approaches are very difficult to apply to MANET due to the absence of a trusted third party. In this paper, we propose a novel communication method that cannot easily be subjected to network sniffing, thereby addressing the issue of security. In addition, we evaluate the proposed method using a network simulator and show its effectiveness. [ABSTRACT FROM AUTHOR]

Incentive schemes are designed to promote cooperation in distributed systems, in which peers are free to decide how much to contribute. A variety of incentive schemes have been proposed in peer-to-peer file-sharing systems, touted to have special advantages in certain scenarios. However, only little research has been done on comparing these incentive schemes under an identical scenario. Fairness is an important indicator to evaluate an incentive scheme, because good fairness can enable proper rewarding to collaborative peers and punishment to strategic peers. In this paper, the correlation map between return and investment of peers is used to depict the fairness degree of different incentive schemes. The fairness and efficiency of several representative incentive schemes are compared under rate-based metrics (all chunks have an identical value) and value-based metrics (different chunks have different values). [ABSTRACT FROM AUTHOR]

When a disaster happens, a mobile ad hoc network is a useful and temporary network. In this situation, it is difficult to assign a unique IP address to each node. Although simple flooding can be used without addresses, it causes redundant message transmission and wastes battery resources. This paper proposes effective flooding based on neighbor information exchange-revised, which reduces power consumption by decreasing the number of redundant messages. Through computer simulation, we show the effectiveness of our method compared with the existing Simple Flooding method. [ABSTRACT FROM AUTHOR]

The use of wireless sensor networks (WSNs) in any real-world application requires a certain level of security. To provide security of operations such as message exchange, key management schemes have to be well adapted to the particularities of WSNs. This paper proposes a novel key management scheme called SKM for sequence-based key management in WSNs. In SKM, sensor nodes are pre-distributed with the first term and the recursive formula of a numerical sequence. This two tiny pre-distributed information will ensure the establishment of pairwise keys to each sensor node with its neighbors after its deployment with a small amount of computation. The security analysis of SKM shows its efficiency. Simulation results confirm that SKM is lightweight in term of node's resources and has a good resilience against node compromising attacks compared to the main existing schemes. [ABSTRACT FROM AUTHOR]

One of the main research challenges faced in Wireless Sensor Networks (WSNs) is to preserve continuously and effectively the coverage of an area (or region) of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes. In this paper, we propose a protocol, called distributed lifetime coverage optimization protocol (DiLCO), which maintains the coverage and improves the lifetime of a wireless sensor network. First, we partition the area of interest into subregions using a classical divide-and-conquer method. Our DiLCO protocol is then distributed on the sensor nodes in each subregion in a second step. To fulfill our objective, the proposed protocol combines two effective techniques: a leader election in each subregion, followed by an optimization-based node activity scheduling performed by each elected leader. This two-step process takes place periodically, to choose a small set of nodes remaining active for sensing during a time slot. Each set is built to ensure coverage at a low energy cost, allowing to optimize the network lifetime. Simulations are conducted using the discrete event simulator OMNET++. We refer to the characteristics of a Medusa II sensor for the energy consumption and the computation time. In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides improved coverage performances. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to propose a hybrid congestion control algorithm to prevent congestion in 2-D broadcast-based multiprocessor architectures with multiple input queues. Our algorithm utilizes a node's both input queue and output channel parameters to detect and prevent congestion. The intermediate node selection procedure and the bypass operation have also been developed as part of the proposed algorithm. The performance of the algorithm is tested with several synthetic traffic patterns on the 2-D simultaneous optical multiprocessor exchange bus. The performance of the algorithm is compared with that of the algorithms which use only input and only output parameters and it is shown that the proposed congestion control algorithm using hybrid parameters performs better than the other algorithms. The proposed algorithm is able to decrease the average network response time by 33.63 %, average input waiting time by 29.13 % and increase average processor utilization by 7.57 % on the average. [ABSTRACT FROM AUTHOR]

This paper proposes an off-line dynamic voltage scaling (DVS) scheme that can be integrated with EDF $$^{(k)}$$ , which is a global real-time scheduling algorithm for symmetric multiprocessor systems. The scheme computes the static execution speed for each individual task assuming the task's worst-case execution. Based on the individual speed, it determines static dual speeds off-line for each task to make use of the gap between actual execution demand and the worst-case execution demand. The simulation results show that the proposed scheme combined with an existing online DVS technique can reduce energy consumption by up to 37 % compared with a uniform speed technique when the number of processors is 32. [ABSTRACT FROM AUTHOR]