树型网格计算环境下的自适应任务调度算法

提出一种基于树型计算网格的自适应调度算法,实现对小粒度独立任务和用户大作业的自适应最优调度。通过对网格环境的实时检测,给出了基于节点负载状况、节点任务执行时间、任务传输时间和任务特性的自适应调度算法,即基于最优任务分配方案的启发式任务调度算法。通过实验与其他调度算法的比较,证明了所提出的任务调度算法在负载平衡和最优跨度方面具有明显的优越性。     

基于MapReduce和Spark的大数据主动学习比较研究

在我们以前的工作中,提出了基于MapReduce的大数据主动学习算法。在本文中,将这一算法移植到Spark环境,提出了基于Spark的大数据主动学习算法,并对基于MapReduce和Spark的2种大数据主动学习算法从运行时间、文件数目、同步数目和内存耗费4个方面进行了比较研究,得出了一些有价值的结论,这些结论将为相关研究人员提供很好的帮助。     

基于GHS的任务调度算法改进

针对非专用网格性能预测调度系统（GHS）中元任务调度算法负载不平衡问题,提出一种启发式动态任务调度策略（H-MM）,结合Min-Min与Max-Min的优点,将H-MM植入GHS,结果表明H-MM在时间跨度与可扩展性方面较以往算法有较大提高。     

Improved social spider algorithm for large scale optimization

Artificial Intelligence Review - Heuristic algorithms can give optimal solutions for low, middle, and large scale optimization problems in an acceptable time. The social spider algorithm (SSA) is...     

基于改进DPSO的网格资源调度算法

基于市场经济模型的网格资源调度问题是一个典型的离散问题及NP-Hard问题,考虑到离散粒子群优化算法在解决离散问题上的有效性,本文在现有算法的研究基础上,提出一种基于改进的离散粒子群优化算法的网格资源分配和任务调度算法,并采用GridSim模拟器对相关算法进行仿真模拟实验和比较。实验结果表明,本文提出的调度算法在作业完成时间、综合性能以及资源的负载平衡方面均具有较大的优势。     

容错的分布式系统通用死锁模型检测解除算法

分布式系统技术为采用低成本购建高性能系统提供了有效的途径,但是由于资源的分配与需求可能产生冲突,造成系统中发生死锁,导致系统运行陷入停滞.在不可靠的分布式系统中,故障会干扰正常的死锁检测,但现有的死锁检测算法不具有容错功能.对失效形式进行了归类,提出一个容错的死锁检测解除算法.算法建立在通用的AND-OR模型基础上,采用扩散计算和集中规约方式,不仅能够检测到死锁,而且能给出死锁环的全部成员.若死锁拓扑处于静态且为环状,算法的消息复杂度的上限为e n-1,时间复杂度为d,其中e为死锁等待图中边的个数,n和d为构成死锁环的节点的个数,分析表明算法性能等于或优于同类算法.     

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta-heuristic algorithms

Cloud computing is becoming a profitable technology because of it offers cost-effective IT solutions globally. A well-designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter-dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem-based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta-heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out-perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.     

Task assignment for minimizing application completion time using honeybee mating optimization

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization (HBMO) algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.     

No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms

This article studies a no-wait two-stage flexible flow shop scheduling problem with setup times aiming to minimize the total completion time. The problem is solved using an adaptive imperialist competitive algorithm (AICA) and genetic algorithm (GA). To test the performance of the proposed AICA and GA, the algorithms are compared with ant colony optimisation, known as an effective algorithm in the literature. The performance of the algorithms are evaluated by solving both small and large-scale problems. Their performance is evaluated in terms of relative percentage deviation. Finally the results of the study are discussed and conclusions and potential areas for further study are highlighted.     

Optimal Parallel Algorithm for the Knapsack Problem Without Memory Conflicts

Abstract The knapsack problem is well known to be NP-complete. Due to its importance in cryptosystem and in number theory, in the past two decades, much effort has been made in order to find techniques that could lead to practical algorithms with reasonable running time. This paper proposes a new parallel algorithm for the knapsack problem where the optimal merging algorithm is adopted. The proposed algorithm is based on an EREW-SIMD machine with shared memory. It is proved that the proposed algorithm is both optimal and the first without memory conflicts algorithm for the knapsack problem. The comparisons of algorithm performance show that it is an improvement over the past researches.     

Task assignment in heterogeneous computing systems using an effective iterated greedy algorithm

A fundamental issue affecting the performance of a parallel application running on a heterogeneous computing system is the assignment of tasks to the processors in the system. The task assignment problem for more than three processors is known to be NP-hard, and therefore satisfactory suboptimal solutions obtainable in an acceptable amount of time are generally sought. This paper proposes a simple and effective iterative greedy algorithm to deal with the problem with goal of minimizing the total sum of execution and communication costs. The main idea in this algorithm is to improve the quality of the assignment in an iterative manner using results from previous iterations. The algorithm first uses a constructive heuristic to find an initial assignment and iteratively improves it in a greedy way. Through simulations over a wide range of parameters, we have demonstrated the effectiveness of our algorithm by comparing it with recent competing task assignment algorithms in the literature.     

Optimal parallel algorithms for the knapsack problem without memory conflicts

The knapsack problem is well known to be NP-complete. Due to its importance in cryptosystem and in number theory, in the past two decades, much effort has been made in order to find techniques that could lead to practical algorithms with reasonable running time. This paper proposes a new parallel algorithm for the knapsack problem where the optimal merging algorithm is adopted. The proposed algorithm is based on anEREW-SIMD machine with shared memory. It is proved that the proposed algorithm is both optimal and the first without memory conflicts algorithm for the knapsack problem. The comparisons of algorithm performance show that it is an improvement over the past researches.     

树型网格计算环境下的独立任务调度

任务调度是实现高性能网格计算的一个基本问题,然而,设计和实现高效的调度算法是非常具有挑战性的.讨论了在网格资源计算能力和网络通信速度异构的树型计算网格环境下,独立任务的调度问题.与实现最小化任务总的执行时间不同(该问题已被证明是NP难题),为该任务调度问题建立了整数线性规划模型,并从该线性规划模型中得到最优任务分配方案??各计算节点最优任务分配数.然后,基于最优任务分配方案,构造了两种动态的需求驱动的任务分配启发式算法:OPCHATA(optimization-based priority-computation heuristic algorithm for task allocation)和OPBHATA(optimization-basedpriority-bandwidth heuristic algorithm for task allocation).实验结果表明:在异构的树型计算网格环境下实现大量独立任务调度时,该算法的性能明显优于其他算法.     

A parallel distributive join algorithm for cube-connectedmultiprocessors

This paper presents a parallel distributive join algorithm for cube-connected multiprocessors. The performance analysis shows that the proposed algorithm has an almost linear speedup over the sequential distributive join algorithm as the number of processors increases, and its performance is comparable to that of the parallel hybrid-hash join algorithm. A big advantage of the proposed algorithm over hash-based join algorithms is that it does not have the bucket overflow problem caused by nonuniform hashing of the smaller operand relation. Moreover, the proposed algorithm can easily support the nonequijoin operation, which is very hard to implement by using hash-based join algorithms     

基于Kademlia的负载平衡云存储算法

针对采用主从式结构的主流云存储系统可能出现的性能瓶颈和可扩展问题,基于分布式哈希表(DHT)技术的完全分布式云存储系统成为一种新的选择。解决好节点的负载平衡问题,是此类技术获得推广的关键。研究了Kademlia算法应用于云存储系统的负载平衡性能。考虑到算法在异构环境下负载平衡性能有明显下降,改进算法在Kademlia找出的候选存储节点中根据节点的存储能力来分配负载。仿真结果表明,改进后算法的负载平衡性能有非常明显的提高,在系统模拟运行时间足够长(如1500 h以上)时,过载节点平均下降7.0%(轻载)和33.7%(重载);文件保存成功率平均提高27.2%(轻载)和35.1%(重载),而增加的通信开销可接受。     

A two-stage Ant Colony Optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times

This paper addresses the non-preemptive unrelated parallel machine scheduling problem with machine-dependent and sequence-dependent setup times. All jobs are available at time zero, all times are deterministic, and the objective is to minimize the makespan. An Ant Colony Optimization (ACO) algorithm is introduced in this paper and is applied to this NP-hard problem; in particular, the proposed ACO tackles a special structure of the problem, where the ratio of the number of jobs to the number of machines is large (i.e., for a highly utilized set of machines). Its performance is evaluated by comparing its solutions to solutions obtained using Tabu Search and other existing heuristics for the same problem, namely the Partitioning Heuristic and Meta-RaPS. The results show that ACO outperformed the other algorithms.     

A resource-efficient encryption algorithm for multimedia big data

Nowadays, multimedia is considered to be the biggest big data as it dominates the traffic in the Internet and mobile phones. Currently symmetric encryption algorithms are used in IoT but when considering multimedia big data in IoT, symmetric encryption algorithms incur more computational cost. In this paper, we have designed and developed a resource-efficient encryption system for encrypting multimedia big data in IoT. The proposed system takes the advantages of the Feistel Encryption Scheme, an Advanced Encryption Standard (AES), and genetic algorithms. To satisfy high throughput, the GPU has also been used in the proposed system. This system is evaluated on real IoT medical multimedia data to benchmark the encryption algorithms such as MARS, RC6, 3-DES, DES, and Blowfish in terms of computational running time and throughput for both encryption and decryption processes as well as the avalanche effect. The results show that the proposed system has the lowest running time and highest throughput for both encryption and decryption processes and highest avalanche effect with compared to the existing encryption algorithms. To satisfy the security objective, the developed algorithm has better Avalanche Effect with compared to any of the other existing algorithms and hence can be incorporated in the process of encryption/decryption of any plain multimedia big data. Also, it has shown that the classical and modern ciphers have very less Avalanche Effect and hence cannot be used for encryption of confidential multimedia messages or confidential big data. The developed encryption algorithm has higher Avalanche Effect and for instance, AES in the proposed system has an Avalanche Effect of %52.50. Therefore, such system is able to secure the multimedia big data against real-time attacks.

     

Hybrid solution algorithms for task scheduling problem with moving executors

Heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated. Three hybrid solution algorithms are introduced. As a basis an evolutionary algorithm is assumed that is combined with the procedure that uses simulated annealing metaheuristics. The results of simulation experiments are given in which the influence of parameters of the solution algorithms as well as of the number of tasks on the quality of scheduling and on the time of computation is investigated.     

Novel dynamic load balancing algorithm for cloud-based big data analytics

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements.

     

A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.