E-MOGWO Algorithm for Computation Offloading in Fog Computing

Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resource-con-strained devices is called fog computing.End devices can offload the task to close-by fog nodes to improve the quality of service and experience.Since com-putation offloading is a multiobjective problem,we need to consider many factors before taking offloading decisions,such as task length,remaining battery power,latency,communication cost,etc.This study uses the multiobjective grey wolf optimization(MOGWO)technique for optimizing offloading decisions.This is thefirst time MOGWO has been applied for computation offloading in fog com-puting.A gravity reference point method is also integrated with MOGWO to pro-pose an enhanced multiobjective grey wolf optimization(E-MOGWO)algorithm.Itfinds the optimal offloading target by taking into account two parameters,i.e.,energy consumption and computational time in a heterogeneous,scalable,multi-fog,multi-user environment.The proposed E-MOGWO is compared with MOG-WO,non-dominated sorting genetic algorithm(NSGA-II)and accelerated particle swarm optimization(APSO).The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption,computational time and the number of tasks successfully executed.

An Adaptive Time-Step Backward Differentiation Algorithm to Solve Stiff Ordinary Differential Equations: Application to Solve Activated Sludge Models

A backward differentiation formula (BDF) has been shown to be an effective way to solve a system of ordinary differential equations (ODEs) that have some degree of stiffness. However, sometimes, due to high-frequency variations in the external time series of boundary conditions, a small time-step is required to solve the ODE system throughout the entire simulation period, which can lead to a high computational cost, slower response, and need for more memory resources. One possible strategy to overcome this problem is to dynamically adjust the time-step with respect to the system’s stiffness. Therefore, small time-steps can be applied when needed, and larger time-steps can be used when allowable. This paper presents a new algorithm for adjusting the dynamic time-step based on a BDF discretization method. The parameters used to dynamically adjust the size of the time-step can be optimally specified to result in a minimum computation time and reasonable accuracy for a particular case of ODEs. The proposed algorithm was applied to solve the system of ODEs obtained from an activated sludge model (ASM) for biological wastewater treatment processes. The algorithm was tested for various solver parameters, and the optimum set of three adjustable parameters that represented minimum computation time was identified. In addition, the accuracy of the algorithm was evaluated for various sets of solver parameters.

Efficiency analysis of numerical integrations for finite element substructure in real-time hybrid simulation

Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.

An Accurate and Computationally Efficient Explicit Friction Factor Model

The implicit Colebrook equation has been the standard for estimating pipe friction factor in a fully developed turbulent regime. Several alternative explicit models to the Colebrook equation have been proposed. To date, most of the accurate explicit models have been those with three logarithmic functions, but they require more computational time than the Colebrook equation. In this study, a new explicit non-linear regression model which has only two logarithmic functions is developed. The new model, when compared with the existing extremely accurate models, gives rise to the least average and maximum relative errors of 0.0025% and 0.0664%, respectively. Moreover, it requires far less computational time than the Colebrook equation. It is therefore concluded that the new explicit model provides a good trade-off between accuracy and relative computational efficiency for pipe friction factor estimation in the fully developed turbulent flow regime.

Quorum systems for intrusion-tolerance based on trusted timely computing base

Quorum systems have been used to solve the problem of data consistency in distributed fault-tolerance systems. But when intrusions occur, traditional quorum systems have some disadvantages. For example, synchronous quorum systems are subject to DOS attacks, while asynchronous quorum systems need a larger system size （at least 3f＋1 for generic data, and f fewer for self-verifying data）. In order to solve the problems above, an intrusion-tolerance quorum system （ITQS） of hybrid time model based on trust timely computing base is presented （TTCB）. The TTCB is a trust secure real-time component inside the server with a well defined interface and separated from the operation system. It is in the synchronous communication environment while the application layer in the server deals with read-write requests and executes update-copy protocols asynchronously. The architectural hybridization of synchrony and asynchrony can achieve the data consistency and availability correctly. We also build two kinds of ITQSes based on TTCB, i.e., the symmetrical and the asymmetrical TTCB quorum systems. In the performance evaluations, we show that TTCB quorum systems are of smaller size, lower load and higher availability.

算法时间的考辨与文化否思

时间是社会建构的产物,算法嵌入的平台社会产生了新的时间制度——算法时间。算法时间构成了一种新的、强大的时间性,成为人们平台生活的计量器和调度器。从媒介考古学的视野来考察,技术会形成嵌入物质世界的文化干预,内嵌算法技术的计算装置会改变人的“内在时间感”。在算法时间的加速逻辑下,越来越精细的计算模型创造出越来越丰富的文化事实,与此同时,算法时间的离散机制通过对个性化时刻的强推,结合平台商业目的,制造出符合用户偏好的适当时机。当人们把时间的生产拱手让给算法机器时,就会成为算法时间生态系统中最脆弱的环节。增加算法透明度,打破平台资本主义与计算主义文化之间的勾连,是摆脱技术时间操控、重拾多样态时间节律的关键。

Efficient Numerical Scheme for Solving Large System of Nonlinear Equations

A fifth-order family of an iterative method for solving systems of nonlinear equations and highly nonlinear boundary value problems has been developed in this paper.Convergence analysis demonstrates that the local order of convergence of the numerical method is five.The computer algebra system CAS-Maple,Mathematica,or MATLAB was the primary tool for dealing with difficult problems since it allows for the handling and manipulation of complex mathematical equations and other mathematical objects.Several numerical examples are provided to demonstrate the properties of the proposed rapidly convergent algorithms.A dynamic evaluation of the presented methods is also presented utilizing basins of attraction to analyze their convergence behavior.Aside from visualizing iterative processes,this methodology provides useful information on iterations,such as the number of diverging-converging points and the average number of iterations as a function of initial points.Solving numerous highly nonlinear boundary value problems and large nonlinear systems of equations of higher dimensions demonstrate the performance,efficiency,precision,and applicability of a newly presented technique.

Stable Computer Method for Solving Initial Value Problems with Engineering Applications

Engineering and applied mathematics disciplines that involve differential equations in general,and initial value problems in particular,include classical mechanics,thermodynamics,electromagnetism,and the general theory of relativity.A reliable,stable,efficient,and consistent numerical scheme is frequently required for modelling and simulation of a wide range of real-world problems using differential equations.In this study,the tangent slope is assumed to be the contra-harmonic mean,in which the arithmetic mean is used as a correction instead of Euler’s method to improve the efficiency of the improved Euler’s technique for solving ordinary differential equations with initial conditions.The stability,consistency,and efficiency of the system were evaluated,and the conclusions were supported by the presentation of numerical test applications in engineering.According to the stability analysis,the proposed method has a wider stability region than other well-known methods that are currently used in the literature for solving initial-value problems.To validate the rate convergence of the numerical technique,a few initial value problems of both scalar and vector valued types were examined.The proposed method,modified Euler explicit method,and other methods known in the literature have all been used to calculate the absolute maximum error,absolute error at the last grid point of the integration interval under consideration,and computational time in seconds to test the performance.The Lorentz system was used as an example to illustrate the validity of the solution provided by the newly developed method.The method is determined to be more reliable than the commonly existing methods with the same order of convergence,as mentioned in the literature for numerical calculations and visualization of the results produced by all the methods discussed,Mat Lab-R2011b has been used.

A fast multipole boundary element method for three-dimensional potential flow problems

A fast multipole methodology (FMM) is developed as a numerical approach to reduce the computational cost andmemory requirements in solving large-scale problems. It is applied to the boundary element method (BEM) for three-dimensional potential flow problems. The algorithm based on mixed multipole expansion and numerical integration isimplemented in combination with an iterative solver. Numerical examinations, on Dirichlet and Neumann problems,are carried out to demonstrate the capability and accuracy of the present method. It has been shown that the methodhas evident advantages in saving memory and computing time when used to solve huge-scale problems which may beprohibitive for the traditional BEM implementation.

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset granularities with the mean absolute percentage error (MAPE)of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suitedfor a real-time energy monitoring application.

Anonymous Authentication for Secure Mobile Agent Based Internet Business

Nowadays, mobile agents are an effective paradigm for accessing the information in distributed applications, especially in a dynamic network environment such as Internet businesses. In such kind of Internet based applications, access must be secure and authentication takes a vital role to avoid malicious use of the system. This kind of security has been provided by several previously proposed algorithms based on RSA digital signature cryptography. However, the computational time for performing encryption and decryption operations in the past literatures is very high. In this paper, we propose an anonymous authentication scheme which potentially reduces the overall computation time needed for verifying the legitimacy of the users. Comparing with previous anonymous authentication schemes, our proposed scheme provides more security and it is effective in terms of computation cost. The experimental results show that the proposed method authenticates the users with low computational time significantly.

Computational uncertainty principle in nonlinear ordinary differential equations——Ⅱ.Theoretical analysis

The error propagation for general numerical method in ordinarydifferential equations ODEs is studied. Three kinds of convergence, theoretical, numerical and actual convergences, are presented. The various components of round-off error occurring in floating-point computation are fully detailed. By introducing a new kind of recurrent inequality, the classical error bounds for linear multistep methods are essentially improved, and joining probabilistic theory the “normal” growth of accumulated round-off error is derived. Moreover, a unified estimate for the total error of general method is given. On the basis of these results, we rationally interpret the various phenomena found in the numerical experiments in part I of this paper and derive two universal relations which are independent of types of ODEs, initial values and numerical schemes and are consistent with the numerical results. Furthermore, we give the explicitly mathematical expression of the computational uncertainty principle and expound the intrinsic relation between two uncertainties which result from the inaccuracies of numerical method and calculating machine.

Recent Advances in Evolutionary Computation

Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.

Numerical analysis of residence time distribution of solids in a bubbling fluidized bed based on the modified structure-based drag model

The residence time distribution （RTD） of solids and the fluidized structure of a bubbling fluidized bed were investigated numerically using computational fluid dynamics simulations coupled with the modified structure-based drag model. A general comparison of the simulated results with theoretical values shows reasonable agreement. As the mean residence time is increased, the RTD initial peak intensity decreases and the RTD curve tail extends farther. Numerous small peaks on the RTD curve are induced by the back- mixing and aggregation of particles, which attests to the non-uniform flow structure of the bubbling fluidized bed. The low value of t50 results in poor contact between phases, and the complete exit age of the overflow particles is much longer for back-mixed solids and those caught in dead regions. The formation of a gulf-stream flow and back-mixing for solids induces an even wider spread of RTD.

Particle swarm optimization for time-optimal control design

Abstract：control law for both linear and nonlinear systems. By introducing a penalty function, the method can be mod- ified to deal with systems with constraints. Compared with existing computational methods, the proposed method can be implemented in a straightforward manner. The convergent solutions can be achieved by selecting suitable PSO parameters regardless of the initial guess of the switching times. A double integrator and a third-order nonlinear system are used tO demonstrate the effectiveness and robustness of the proposed method. The method is applied to obtain the time-optimal control law for a high performance linear motion positioning system. The results show the practicality of the proposed algorithm.

Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure

With the increasing computing demand of train operation control systems,the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years.How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems.Because the cloud computing platform is in an open network environment,it can face many security loopholes and malicious network at-tacks.Therefore,it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform,thereby enhancing its safety and reliability.Firstly,a private cloud safety computer platform architecture based on dynamic heterogeneous redundant(DHR)structure is proposed,and a dynamic migration mechanism for heterogeneous executives is designed.Then,a generalized stochastic Petri net(GSPN)model of a private cloud safety computer platform based on DHR is established,and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model(CTMC)to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance.Finally,through experimental verifcation,the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform,thereby improving its safety and reliability.

Reduced-dimensional MPC controller for direct thrust control

With the development of the aircraft gas turbine engine, a control system should be able to achieve effective thrust control to gain better operability. The main contribution of this paper is to develop a novel direct thrust control approach based on an improved model predictive control method through a strategy that reduces the dimension of control sequence. It can not only achieve normal direct thrust control tasks but also maximize the thrust level within the safe operation boundaries. Only the action of switching the objective functions is required to achieve the switch of these two thrust control modes while there is no modification to the control structure. Besides,a shorter control sequence is defined for multivariable control by updating only one control variable at every simulation time instant. Therefore, the time requirement for the solving process of the optimal control sequence is reduced. The proposed controller is implemented to a twin-spool engine.Simulations are conducted in the wide flight envelope, and results show that the average timeconsumption can be reduced up to 65% in comparison with the standard model predictive control,and the thrust can be increased significantly when maximum thrust mode is implemented by using engine limit margins.

Investigation into improving the efficiency and accuracy of CFD/DEM simulations

The Euler-Lagrange approach combined with a discrete element method has frequently been applied to elucidate the hydrodynamic behavior of dense fluid-solid flows in fluidized beds. In this work, the efficiency and accuracy of this model are investigated. Parameter studies are performed; in these studies, the stiffness coefficient, the fluid time step and the processor number are varied under conditions with different numbers of particles and different particle diameters. The obtained results are compared with measurements to derive the optimum parameters for CFD/DEM simulations. The results suggest that the application of higher stiffness coefficients slightly improves the simulation accuracy. However, the average computing time increases exponentially. At larger fluid time steps, the results show that the average computation time is independent of the applied fluid time step whereas the simulation accuracy decreases greatly with increasing the fluid time step. The use of smaller time steps leads to negligible improvements in the simulation accuracy but results in an exponential rise in the average computing time. The parallelization accelerates the DEM simulations if the critical number for the domain decomposition is not reached. Above this number, the performance is no longer proportional to the number of processors. The critical number for the domain decomposition depends on the number of particles. An increase in solid contents results in a shift of the critical decomposition number to higher numbers of CPUs.