A path selection-based algorithm for real-time data staging in Grid applications

Efficient data scheduling is becoming an important issue in distributed real-time applications that produce huge data sets. The Grid environment on which these applications may run seeks to harness the geographically distributed resources for the applications. Scheduling components should account for real-time measures of the applications and reduce communication overhead due to enormous data size experienced, especially in dissemination applications. In this study, we consider the data staging scheme to provide the dissemination of large-scale data sets for the distributed real-time applications. We propose a new path selection-based algorithm for optimizing a criterion that reflects the general satisfiability of the system. The algorithm adopts a blocking-time analysis method combined with a simple heuristic to explore the most likely regions of a search space. Two heuristics are provided for the algorithm to explore these regions of the search space. Simulation results show that the proposed algorithm together with either of the heuristic has higher performance compared to other algorithms in the literature. We also show by simulation that a new optimization criterion we proposed in this study is successful in improving the performance of the individual applications.     

The continuous artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABC_bin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABC_bin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABC_bin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABC_bin is an alternative and simple binary optimization tool in terms of solution quality and robustness.     

Trusted dynamic level scheduling based on Bayes trust model

A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayes method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling (DLS) algorithm, the Trust-Dynamic Level Scheduling (Trust-DLS) algorithm is proposed. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of Grid tasks in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in Grid environment.     

Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems

Optimization techniques, specially evolutionary algorithms, have been widely used for solving various scientific and engineering optimization problems because of their flexibility and simplicity. In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented. Despite many of the optimization algorithms that use nature as the principal source of inspiration, HBBO uses the human behavior as the main source of inspiration. In this paper, first some human behaviors that are needed to understand the algorithm are discussed and after that it is shown that how it can be used for solving the practical optimization problems. HBBO is capable of solving many types of optimization problems such as high-dimensional multimodal functions, which have multiple local minima, and unimodal functions. In order to demonstrate the performance of HBBO, the proposed algorithm has been tested on a set of well-known benchmark functions and compared with other optimization algorithms. The results have been shown that this algorithm outperforms other optimization algorithms in terms of algorithm reliability, result accuracy and convergence speed.

     

Observing the effect of interprocess communication in auto controlled ant colony optimization‐based scheduling on computational grid

Computational grids allow the sharing of geographically distributed computational resources in an efficient, reliable, and secure manner. Grid is still in its infancy, and there are many problems associated with the computational grid, namely job scheduling, resource management, information service, information security, routing, fault tolerance, and many more. Scheduling of jobs on grid nodes is an NP‐class problem warranting for heuristic and meta‐heuristic solution approach. In the proposed work, a meta‐heuristic technique, auto controlled ant colony optimization, has been applied to solve this problem. The work observes the effect of interprocess communication in process to optimize turnaround time of the job. The proposed model has been simulated in Matlab. For the different scenarios in computational grid, results have been analyzed. Result of the proposed model is compared with another meta‐heuristic technique genetic algorithm that has been applied for the same purpose. It is found that auto controlled ant colony optimization not only gives better solution in comparison to genetic algorithm, but also converges faster because initial solution itself is good because of constructive and decision‐based policy adapted by the former. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.     

Security‐aware scheduling model for computational grid

Grid applications with stringent security requirements introduce challenging concerns because the schedule devised by nonsecurity‐aware scheduling algorithms may suffer in scheduling security constraints tasks. To make security‐aware scheduling, estimation and quantification of security overhead is necessary. The proposed model quantifies security, in the form of security levels, on the basis of the negotiated cipher suite between task and the grid‐node and incorporates it into existing heuristics MinMin and MaxMin to make it security‐aware MinMin(SA) and MaxMin(SA). It also proposes SPMaxMin (Security Prioritized MinMin) and its comparison with three heuristics MinMin(SA), MaxMin(SA), and SPMinMin on heterogeneous grid/task environment. Extensive computer simulation results reveal that the performance of the various heuristics varies with the variation in computational and security heterogeneity. Its analysis over nine heterogeneous grid/task workload situations indicates that an algorithm that performs better for one workload degrades in another. It is conspicuous that for a particular workload one algorithm gives better makespan while another gives better response time. Finally, a security‐aware scheduling model is proposed, which adapts itself to the dynamic nature of the grid and picks the best suited algorithm among the four analyzed heuristics on the basis of job characteristics, grid characteristics, and desired performance metric. Copyright © 2011 John Wiley & Sons, Ltd.     

面向数据联邦的安全多方θ-连接算法

近年来,多个国家地区出台了一系列数据安全相关的法律,例如欧盟的《通用数据保护条例》等.这些相关法律法规的出台,加剧了各企业机构等多方之间数据共享难的数据孤岛问题.数据联邦(data federation)正是解决该问题的可能出路.数据联邦是指多个数据拥有方在不泄露各自原始数据的前提下,结合安全多方计算等隐私计算技术,联合完成查询任务的计算.这一概念已成为近年来的研究热点,并涌现出一系列相关的代表性系统工作,如SMCQL、Conclave.然而,针对关系数据库系统中核心的连接查询,现有数据联邦系统还存在如下问题:首先,连接种类单一,难以满足复杂连接条件下的查询需求;其次,算法性能低下,由于现有系统往往直接调用安全工具库,其运行时间与通信开销高昂.因此,针对以上问题进行研究,提出了数据联邦下连接算法.主要贡献如下:首先,设计实现了面向多方的联邦安全算子,能够支持多种运算;其次,提出了支持θ-连接的联邦连接算法与优化策略,显著减少了连接查询所需安全计算代价;最后,基于基准数据集TPC-H,验证了该算法的性能.实验结果表明,与现有数据联邦系统SMCQL、Conclave相比,该算法能够将运行时...     

A Non-monotone Line Search Algorithm for?Unconstrained Optimization

The monotone line search schemes have been extensively used in the iterative methods for solving various optimization problems. It is well known that the non-monotone line search technique can improve the likelihood of finding a global optimal solution and the numerical performance of the methods, especially for some difficult nonlinear problems. The traditional non-monotone line search approach requires that a maximum of recent function values decreases. In this paper, we propose a new line search scheme which requires that a convex combination of recent function values decreases. We apply the new line search technique to solve unconstrained optimization problems, and show the proposed algorithm possesses global convergence and R-linear convergence under suitable assumptions. We also report the numerical results of the proposed algorithm for solving almost all the unconstrained testing problems given in CUTEr, and give numerical comparisons of the proposed algorithm with two famous non-monotone methods.     

A Multi-Objective Integer Melody Search Algorithm

There are a number of algorithms for the solution of continuous optimization problems. However, many practical design optimization problems use integer design variables instead of continuous. These types of problems cannot be handled by using continuous design variables-based algorithms. In this paper, we present a multi-objective integer melody search optimization algorithm (MO-IMS) for solving multi-objective integer optimization problems, which take design variables as integers. The proposed algorithm is a modified version of single-objective melody search (MS) algorithm, which is an innovative optimization algorithm, inspired by basic concepts applied in harmony search (HS) algorithm. Results show that MO-IMS has better performance in solving multi-objective integer problems than the existing multi-objective integer harmony search algorithm (MO-IHS). Performance of proposed algorithm is evaluated by using various performance metrics on test functions. The simulation results show that the proposed MO-IMS can be a better technique for solving multi-objective problems having integer decision variables.     

Ensemble of multi-objective metaheuristic algorithms for multi-objective unconstrained binary quadratic programming problem

Metaheuristics have been widely utilized for solving NP-hard optimization problems. However, these algorithms usually perform differently from one problem to another, i.e., one may be effective on a problem but performs badly on another problem. Therefore, it is difficult to choose the best algorithm in advance for a given problem. In contrast to selecting the best algorithm for a problem, selection hyper-heuristics aim at performing well on a set of problems (instances). This paper proposes a selection hyper-heuristic based algorithm for multi-objective optimization problems. In the proposed algorithm, multiple metaheuristics exhibiting different search behaviors are managed and controlled as low-level metaheuristics in an algorithm pool, and the most appropriate metaheuristic is selected by means of a performance indicator at each search stage. To assess the performance of the proposed algorithm, an implementation of the algorithm containing four metaheuristics is proposed and tested for solving multi-objective unconstrained binary quadratic programming problem. Experimental results on 50 benchmark instances show that the proposed algorithm can provide better overall performance than single metaheuristics, which demonstrates the effectiveness of the proposed algorithm.     

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

The continuous growth of computation power requirement has provoked computational Grids, in order to resolve large scale problems. Job scheduling is a very important mechanism and a better scheduling scheme can greatly improve the efficiency of Grid computing. A lot of algorithms have been proposed to address the job scheduling problem. Unfortunately, most of them largely ignore the security risks involved in executing jobs in such an unreliable environment as Grid. This is known as security problem and it is a main hurdle to make the job scheduling secure, reliable and fault-tolerant. In this paper, we present a Genetic Algorithm with multi-criteria approach, in terms of job completion time and security risks. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, proposing the Accelerated Genetic Algorithm. We also present the Accelerated Genetic Algorithm with Overhead which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristic algorithms. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.     

自适应邻域的多目标网格任务调度算法研究

针对网格计算中的多目标网格任务调度问题,提出了一种基于自适应邻域的多目标网格任务调度算法。该算法通过求解多个网格任务调度目标函数的非劣解集,采用自适应邻域的方法来保持网格任务调度多目标解集的分布性,尝试解决网格任务调度中多目标协同优化问题。实验结果证明,该算法能够有效地平衡时间维度和费用维度目标,提高了资源的利用率和任务的执行效率,与Min-min和Max-min算法相比具有较好的性能。     

Evolutionary Algorithms for Minimax Problems in Robust Design

Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem.     

Re-sampled inheritance search: high performance despite the simplicity

This paper proposes re-sampled inheritance search (RIS), a novel algorithm for solving continuous optimization problems. The proposed method, belonging to the class of Memetic Computing, is very simple and low demanding in terms of memory employment and computational overhead. The RIS algorithm is composed of a stochastic sample mechanism and a deterministic local search. The first operator randomly generates a solution and then recombines it with the best solution detected so far (inheritance) while the second operator searches in an exploitative way within the neighbourhood indicated by the stochastic operator. This extremely simple scheme is shown to display a very good performance on various problems, including hard to solve multi-modal, highly-conditioned, large scale problems. Experimental results show that the proposed RIS is a robust scheme that competitively performs with respect to recent complex algorithms representing the-state-of-the-art in modern continuous optimization. In order to further prove its applicability in real-world cases, RIS has been used to perform the control system tuning for yaw operations on a helicopter robot. Experimental results on this real-world problem confirm the value of the proposed approach.     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

Adapting grid applications to safety using fault-tolerant methods: Design, implementation and evaluations

Grid applications have been prone to encountering problems such as failures or malicious attacks during execution in recent years, due to their distributed and large-scale features. The application itself, however, has limited power to address these problems. This paper presents the design, implementation, and evaluation of an adaptive framework— Dynasa, which strives to handle security problems using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of applications according to the status of the Grid environments. We evaluate our adaptive framework experimentally using the Grid5000 testbed and the experimental results have demonstrated that Dynasa enables the application itself to handle the security problems efficiently. The starting of the adaptive component is less than 1 s and the adaptive action is less than 0.1 s with the checkpoint interval of 20 s. Compared with non-adaptive method, experimental results demonstrate that Dynasa achieves better performance in terms of execution time, network bandwidth consumed, and CPU load, resulting in up to a 50% lower overhead.     

An Adaptive Bumble Bees Mating Optimization algorithm

The finding of the suitable parameters of an evolutionary algorithm, as the Bumble Bees Mating Optimization (BBMO) algorithm, is one of the most challenging tasks that a researcher has to deal with. One of the most common used ways to solve the problem is the trial and error procedure. In the recent few years, a number of adaptive versions of every evolutionary and nature inspired algorithm have been presented in order to avoid the use of a predefined set of parameters for all instances of the studied problem. In this paper, an adaptive version of the BBMO algorithm is proposed, where initially random values are given to each one of the parameters and, then, these parameters are adapted during the optimization process. The proposed Adaptive BBMO algorithm is used for the solution of the Multicast Routing Problem (MRP). As we would like to prove that the proposed algorithm is suitable for solving different kinds of combinatorial optimization problems we test the algorithm, also, in the Probabilistic Traveling Salesman Problem (PTSP) and in the Hierarchical Permutation Flowshop Scheduling Problem (HPFSP). Finally, the algorithm is tested in four classic benchmark functions for global optimization problems (Rosenbrock, Sphere, Rastrigin and Griewank) in order to prove the generality of the procedure. A number of benchmark instances for all problems are tested using the proposed algorithm in order to prove its effectiveness.     

Tabu search for a class of single-machine scheduling problems

In this paper we develop a tabu search-based solution procedure designed specifically for a certain class of single-machine scheduling problems with a non-regular performance measure. The performance of the developed algorithm is tested for solving the variance minimization problem. Problems from the literature are used to test the performance of the algorithm. This algorithm can be used for solving other problems such as minimizing completion time deviation from a common due date.Scope and purposeScheduling problems with non-regular performance measures has gained a great importance in modern manufacturing systems. These problems are found to be hard to solve and analyze. The purpose of this paper is to present a tabu search approach for solving a certain class of single-machine scheduling problems with non-regular performance measure. Minimizing the variance of completion times and the total deviation from a common due date are two examples of such problems. The proposed approach is found to perform better than the simulated annealing approach for the variance minimization problem.     

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.     

基于人工蜂群算法的DV-Hop定位改进

针对无线传感器网络无需测距依赖的DV-Hop定位算法节点定位精度不高的问题,将鲁棒性强、收敛速度快且全局寻优性能优异的人工蜂群算法引入到DV-Hop算法的设计中,提出了一种ABDV-Hop(Artificial Bee ColonyDV-Hop)算法。该算法在传统DV-Hop算法的基础上,利用节点间的距离和锚节点的位置信息,在DV-Hop算法的最后阶段,通过建立目标优化函数,实现对未知节点坐标的估计。仿真结果表明,与传统DV-Hop算法相比,在不增加传感器节点的硬件开销的基础上,改进算法能有效降低定位误差。