A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid

Data Grids provide environment for huge, data-intensive applications that produce and process enormous data. Such environments are thus asked to manage data and schedule jobs at the same time. These two important operations have to be tightly coupled to achieve the best results. Replication techniques are widely used to increase the availability of data, improving performance of query latency and load balancing in Data Grid. Also effective resource scheduling is a challenging research issue. In this paper we propose a job scheduling policy, called Parallel Job Scheduling (PJS), and a dynamic data replication strategy, called Threshold-based Dynamic Data Replication (TDDR), to improve the data access efficiencies in a hierarchical Data Grid. The PJS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. The main idea of TDDR strategy is using a threshold value to determine if the requested replica needs to be copied to the node. The TDDR determines this threshold dynamically based on data request arrival rates and available storage capacities. Then, in order to overcome the problem of limited storage space in each node, we design an efficient replica replacement strategy, which is developed as a two stages process. First, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. Results from the simulation show that our proposed algorithms have better performance in comparison with other algorithms in terms of Mean Job Time, Number of Intercommunications, Number of Replications, Computing Resource Usage, and Effective Network Usage.     

Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training

Background  

Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.     

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.     

Density estimation using a new dimension adaptive particle swarm optimization algorithm

Current Particle Swarm Optimization (PSO) algorithms do not address problems with unknown dimensions, which arise in many applications that would benefit from the use of PSO. In this paper, we propose a new algorithm, called Dimension Adaptive Particle Swarm Optimization (DA-PSO) that can address problems with any number of dimensions. We also propose and compare three other PSO-based methods with DA-PSO. We apply our algorithms to solve the Weibull mixture model density estimation problem as an illustration. DA-PSO achieves better objective function values than other PSO-based algorithms on four simulated datasets and a real dataset. We also compare DA-PSO with the recursive Expectation-Maximization (EM) estimator, which is a non-PSO-based method, obtaining again very good results.     

A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.     

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.     

Research on Location Characteristics and Algorithms based on Frequency Domain for a 2D Underwater Active Electrolocation Positioning System

Weakly electric fish has an ability to generate a low-frequency electric field actively to locate the surrounding object in complete darkness by sensing the change of the electric field.This ability is called active electrolocation.In this paper,we designed a two-dimensional (2D) experimental platform of underwater active electrolocation system by simulating weakly electric fish.On the platform,location characteristics based on frequency domain were investigated.Results indicated that surface shape of 3D location characteristic curves for the 2D underwater active electrolocation positioning system was convex upwards or concave down which was influenced by the material of probed objects and the frequency of the electric field excitation signal.Experiments also confirmed that the amplitude of the electric field excitation signal and the size of the probed object will only influence the amplitude corresponding to 3D location characteristic curves.Based on above location characteristics,we present three location algorithms including Cross Location Algorithm (CLA),Stochastic Location Algorithm (SLA) and Particle Swarm Optimization (PSO) location algorithm in frequency domain and achieved the task of the underwater positioning system.Our work may have reference value for underwater detection study.     

Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.     

A Comprehensive Review of Swarm Optimization Algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.     

基于粒子群优化算法的脑磁图源定位

脑磁图作为一种新型的脑探测技术,具有较高定位精度和毫秒级时间分辨率的特点。快速准确地利用脑磁图技术对三维空间中的脑神经活动源进行定位,对于脑功能研究和医学临床应用都具有重要的应用价值。可是,目前的脑磁图源定位广泛采用了多信号分类方法,它要求对三维大脑空间进行全局扫描,需要大量的计算,存在速度慢的缺点。针对这一问题,提出了一种基于粒子群优化算法的脑磁图源定位方法。先利用粒子群优化算法全局搜索能力强的特点寻找出目标函数的全局最优值,进行初步的脑磁图源定位;然后,再在小范围内进行小网格的搜索,进一步实现精确的定位。实验结果表明,基于粒子群优化算法的脑磁图源定位能够很好地解决上述问题,具有计算速度快、定位精度高的特点。     

Benchmarking the Clustering Performances of Evolutionary Algorithms: A Case Study on Varying Data Size

Background and objectiveClustering is a widely used popular method for data analysis within many clustering algorithms for years. Today it is used in many predictions, collaborative filtering and automatic segmentation systems on different domains. Also, to be broadly used in practice, such clustering algorithms need to give both better performance and robustness when compared to the ones currently used. In recent years, evolutionary algorithms are used in many domains since they are robust and easy to implement. And many clustering problems can be easily solved with such algorithms if the problem is modeled as an optimization problem. In this paper, we present an optimization approach for clustering by using four well-known evolutionary algorithms which are Biogeography-Based Optimization (BBO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).Methodthe objective function has been specified to minimize the total distance from cluster centers to the data points. Euclidean distance is used for distance calculation. We have applied this objective function to the given algorithms both to find the most efficient clustering algorithm and to compare the clustering performances of algorithms against different data sizes. In order to benchmark the clustering performances of algorithms in the experiments, we have used a number of datasets with different data sizes such as some small scale, medium and big data. The clustering performances have been compared to K-means as it is a widely used clustering algorithm for years in literature. Rand Index, Adjusted Rand Index, Mirkin's Index and Hubert's Index have been considered as parameters for evaluating the clustering performances.ResultAs a result of the clustering experiments of algorithms over different datasets with varying data sizes according to the specified performance criteria, GA and GWO algorithms show better clustering performances among the others.ConclusionsThe results of the study showed that although the algorithms have shown satisfactory clustering results on small and medium scale datasets, the clustering performances on Big data need to be improved.     

V - Channel magnification enabled by hybrid optimization algorithm: Enhancement of video super resolution

Although being a really active area of research, television super-resolution remains a difficult problem. Additionally, it is noted that the blur motion and computational crisis hinder the enhancement. As a result, the goal of this research is to present a brand-new smart SR framework for the camera shot. To create High Resolution (HR) videos, first frames in RGB format are converted to HSV and then the V-channel is enhanced. In order to create enriched video frames, a high - dimension grid with enhanced pixel intensity is then created. This paper introduces a particular progression to enable this: Motion estimation, Cubic Spline Interpolation, and Deblurring or Sharpening are the three methods. By carefully adjusting the parameters, the cubic spline interpolation is improved during operation. A brand-new hybrid technique dubbed Lion with Particle Swarm Velocity Update (LPSO-VU), which combines the principles of the Lion Algorithm (LA) and Particle Swarm Optimization (PSO) algorithms, is presented for this optimal tuning purpose. Finally, using the BRISQUE, SDME, and ESSIM metrics, the adequacy of the method is contrasted to other traditional models, and its superiority is demonstrated. Accordingly, the analysis shows that the suggested LPSO-VU model for video frame 1 is 16.6%, 25.56%, 26.2%, 26.2%, and 27.2% superior to the previous systems like PSO, GWO, WOA, ROA, MF-ROA, and LA, respectively, in terms of BRISQUE.     

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimization functions.     

A new approach to the job scheduling problem in computational grids

Job scheduling is one of the most challenging issues in Grid resource management that strongly affects the performance of the whole Grid environment. The major drawback of the existing Grid scheduling algorithms is that they are unable to adapt with the dynamicity of the resources and the network conditions. Furthermore, the network model that is used for resource information aggregation in most scheduling methods is centralized or semi-centralized. Therefore, these methods do not scale well as Grid size grows and do not perform well as the environmental conditions change with time. This paper proposes a learning automata-based job scheduling algorithm for Grids. In this method, the workload that is placed on each Grid node is proportional to its computational capacity and varies with time according to the Grid constraints. The performance of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with those of several existing methods. Numerical results confirm the superiority of the proposed algorithm over the others in terms of makespan, flowtime, and load balancing.     

Distributed scalable multi-robot learning using particle swarm optimization

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed online robotic learning in groups of robots with access to only local information. The effectiveness of the learning technique on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and compared in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods. To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a group of 10 real robots, generating results which support the findings from our simulations.     

A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm

Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the Sine Cosine Algorithm (SCA) and Genetic Algorithm (GA), called SCAGA. Typically, optimization methods have two main search strategies; exploration of the search space and exploitation to determine the optimal solution. The proposed SCAGA resulted in better performance when balancing between exploitation and exploration strategies of the search space. The proposed SCAGA has also been evaluated using the following evaluation criteria: classification accuracy, worst fitness, mean fitness, best fitness, the average number of features, and standard deviation. Moreover, the maximum accuracy of a classification and the minimal features were obtained in the results. The results were also compared with a basic Sine Cosine Algorithm (SCA) and other related approaches published in literature such as Ant Lion Optimization and Particle Swarm Optimization. The comparison showed that the obtained results from the SCAGA method were the best overall the tested datasets from the UCI machine learning repository.

     

An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems

In large-scale heterogeneous cluster computing systems, processor and network failures are inevitable and can have an adverse effect on applications executing on such systems. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing scheduling algorithms for precedence constrained tasks in heterogeneous systems only consider scheduling length, and not efficiently satisfy the reliability requirements of task. In recognition of this problem, we build an application reliability analysis model based on Weibull distribution, which can dynamically measure the reliability of task executing on heterogeneous cluster with arbitrary networks architectures. Then, we propose a reliability-driven earliest finish time with duplication scheduling algorithm (REFTD) which incorporates task reliability overhead into scheduling. Furthermore, to improve system reliability, it duplicates task as if task hazard rate is more than threshold \(\theta \) . The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm can shorten schedule length and improve system reliability significantly.     

Energy efficient scheduling for parallel applications on mobile clusters

During the past decade, cluster computing and mobile communication technologies have been extensively deployed and widely applied because of their giant commercial value. The rapid technological advancement makes it feasible to integrate these two technologies and a revolutionary application called mobile cluster computing is arising on the horizon. Mobile cluster computing technology can further enhance the power of our laptops and mobile devices by running parallel applications. However, scheduling parallel applications on mobile clusters is technically challenging due to the significant communication latency and limited battery life of mobile devices. Therefore, shortening schedule length and conserving energy consumption have become two major concerns in designing efficient and energy-aware scheduling algorithms for mobile clusters. In this paper, we propose two novel scheduling strategies aimed at leveraging performance and power consumption for parallel applications running on mobile clusters. Our research focuses on scheduling precedence constrained parallel tasks and thus duplication heuristics are applied to schedule parallel tasks to minimize communication overheads. However, existing duplication algorithms are developed with consideration of schedule lengths, completely ignoring energy consumption of clusters. In this regard, we design two energy-aware duplication scheduling algorithms, called EADUS and TEBUS, to schedule precedence constrained parallel tasks with a complexity of O(n ²), where n is the number of tasks in a parallel task set. Unlike the existing duplication-based scheduling algorithms that replicate all the possible predecessors of each task, the proposed algorithms judiciously replicate predecessors of a task if the duplication can help in conserving energy. Our energy-aware scheduling strategies are conducive to balancing scheduling lengths and energy savings of a set of precedence constrained parallel tasks. We conducted extensive experiments using both synthetic benchmarks and real-world applications to compare our algorithms with two existing approaches. Experimental results based on simulated mobile clusters demonstrate the effectiveness and practicality of the proposed duplication-based scheduling strategies. For example, EADUS and TABUS can reduce energy consumption for the Gaussian Elimination application by averages of 16.08% and 8.1% with merely 5.7% and 2.2% increase in schedule length respectively.

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.     

基于微粒群算法的辽宁省可持续农业产业结构优化配置研究

针对辽宁省农业产业结构中存在的问题,从经济、生态、社会三方面综合考察,建立了该区域的可持续农业产业结构优化模型,并利用改进的微粒群多目标优化算法对模型进行了求解,为辽宁省以及相似区域的农业产业结构调整提供了理论依据.