On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

Nowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing.     

Maximizing business value by optimal assignment of jobs to resources in grid computing

An important problem that arises in the area of grid computing is one of optimally assigning jobs to resources to achieve a business objective. In the grid computing area, however, such scheduling has mostly been done from the perspective of maximizing the utilization of resources. As this form of computing proliferates, the business aspects will become crucial for the overall success of the technology. Hence, we discuss the grid scheduling problem from a business perspective. We show that this problem is not only strongly NP-hard, but it is also non-approximable. Therefore, we propose heuristics for different variants of the problem and show that these heuristics provide near-optimal solution for a wide variety of problem instances. We show that the execution times of proposed heuristics are very low, and hence, they are suitable for solving problems in real-time. We also present several managerial implications and compare the performance of two widely used models in the real-time scheduling of grid computing.     

A class of on-line scheduling algorithms to minimize total completion time

We consider the problem of scheduling jobs on-line on a single machine and on identical machines with the objective to minimize total completion time. We assume that the jobs arrive over time. We give a general 2-competitive algorithm for the single machine problem. The algorithm is based on delaying the release time of the jobs, i.e., making the jobs artificially later available to the on-line scheduler than the actual release times. Our algorithm includes two known algorithms for this problem that apply delay of release times. The proposed algorithm is interesting since it gives the on-line scheduler a whole range of choices for the delays, each of which leading to 2-competitiveness.We also show that the algorithm is 2α competitive for the problem on identical machines where α is the performance ratio of the Shortest Remaining Processing Time first rule for the preemptive relaxation of the problem.     

Hybrid Flow-Shop: a Memetic Algorithm Using Constraint-Based Scheduling for Efficient Search

The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound algorithm to be employed as the local search engine of the memetic algorithm. Next, we present the new memetic algorithm. We lastly explain the computational experiments carried out to evaluate the performance of three algorithms (genetic algorithm, constraint programming based branch-and-bound algorithm, and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better quality solutions and that it is very efficient.     

Minimizing due date related performance measures on two batch processing machines

This paper considers a scheduling problem in a two-machine flowshop of two batch processing machines. On each batch processing machine, jobs are processed in a batch, and each batch is allowed to contain jobs up to the maximum capacity of the associated machine. The scheduling problem is analyzed with respect to three due date related objectives including maximum tardiness, number of tardy jobs and total tardiness. In the analysis, several solution properties are characterized and based upon these properties, three efficient polynomial time algorithms are developed for minimizing the due date related measures.     

六自由度机械臂的运动规划

为了使六自由度机械臂完成特定的动作,需要设计计算相应的指令序列.首先计算了机械臂位姿与指尖位置之间的关系公式,然后针对机械臂的到达问题、沿曲线运动问题和避障问题,分别提出目标位姿预测、曲线离散到达和受限目标到达三种解决方法,其中涉及的关键算法是自适应搜索法,该方法具有效率高、精度高、适用范围广的特点.在产生指令序列时采用贪心算法.通过以上方法得到的执行结果误差很小(<0.8mm),同时搜索收敛速度也很快.     

An iterative approach for the serial batching problem with parallel machines and job families

In this paper, we consider a parallel machine scheduling problem to minimize the total completion time. Each job belongs to a certain family. All jobs of one family have identical processing times. Major setups occur between jobs of different families, and we include sequence dependencies. Batches of jobs belonging to the same family can be formed to avoid these setups. Furthermore, we assume serial batching and batch availability. Therefore, the processing time of a batch is the sum of the processing times of all jobs grouped into the corresponding batch. An iterative method is developed for solving this specific problem. This approach alternates between varying batch sizes using an efficient heuristic and sequencing batches based on variable neighborhood search (VNS). Computational results demonstrate that the iterative heuristic outperforms heuristics based on a fixed batch size and list scheduling.     

Admit or preserve? Addressing server failures in cloud computing task management

Cloud computing task management has a critical role in the efficient operation of the cloud resources, i.e., the servers. The task management handles critical and complicated decisions, overcoming the inherent dynamic nature of cloud computing systems and the additional complexity due to the large magnitude of resources in such systems (tens of thousands of servers). Due to the fact that servers may fail, task management is required to conduct both task admissions and task preservation decisions. Moreover, both these decisions require considering future system trajectories and the interplay between preservation and admission. In this paper we study the combined problem of task admission and preservation in a dynamic environment of cloud computing systems through analysis of a queueing system based on a Markov decision process (MDP). We show that the optimal operational policy is of a double switching curve type. On face value, the extraction of the optimal policy is rather complicated, yet our analysis reveals that the optimal policy can be reduced to a single rule, since the rules can effectively be decoupled. Based on this result, we propose two heuristic approaches that approximate the optimal rule for the most relevant system settings in cloud computing systems. Our results provide a simple policy scheme for the combined admission and preservation problem that can be applied in a complex cloud computing environments, and eliminate the need for sophisticated real-time control mechanisms.

     

Combining Metaheuristics and Exact Methods for Solving Exactly Multi-objective Problems on the Grid

This paper presents a parallel hybrid exact multi-objective approach which combines two metaheuristics – a genetic algorithm (GA) and a memetic algorithm (MA), with an exact method – a branch and bound (B&B) algorithm. Such approach profits from both the exploration power of the GA, the intensification capability of the MA and the ability of the B&B to provide optimal solutions with proof of optimality. To fully exploit the resources of a computational grid, the hybrid method is parallelized according to three well-known parallel models – the island model for the GA, the multi-start model for the MA and the parallel tree exploration model for the B&B. The obtained method has been experimented and validated on a bi-objective flow-shop scheduling problem. The approach allowed to solve exactly for the first time an instance of the problem – 50 jobs on 5 machines. More than 400 processors belonging to 4 different administrative domains have contributed to the resolution process during more than 6 days.      

Optimal scheduling of batch industrial facilities

An increasing interest in batch processing has been evident in recent years. This renewed interest is explained by the inherent flexibility of such plants that permits a high level of response to uncertain market conditions and requirements. This level of response does require the use of efficient tools to help the decision-making process at the design and operational level. This paper presents a Mixed Integer Linear Program (MILP) model to optimise the scheduling of batch facilities subject to changeovers and distribution constraints so as to guarantee a pre-defined objective. Such an objective can be defined as the minimum orders' total lateness or the maximum distribution units loading capacity, among others. A continuous-time representation is used as well as the concept of job predecessor and successor to effectively handle changeovers. Facilities having non-identical parallel units/lines, sequence-dependent orders, finite release times for units and orders, restrictions on the suitability of jobs to lines/units and different possible destinations to available distribution units are also considered. Based on these characteristics the proposed model is able to determine the optimal allocation of jobs to production lines/units, the sequence of jobs on every line/unit and the starting and completion production times of each order. Also, the usage and allocation of the distribution resources (eg trucks) to orders and destinations are obtained based on their availability and suitability to the orders. The model led to the development of a prototype information system that can be used as a tool to help the decision-making process at the operational plant level.Finally, the applicability of the proposed system/formulation is shown through the resolution of an industrial real case where the production of polymers is performed.     

Scheduling open shops with parallel machines to minimize total completion time

This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms.     

Parallel hyperheuristics for the frequency assignment problem

This work presents a set of approaches used to deal with the frequency assignment problem (FAP), which is one of the key issues in the design of GSM networks. The used formulation of FAP is focused on aspects which are relevant for real-world GSM networks. A memetic algorithm, together with the specifically designed local search and variation operators, are presented. The memetic algorithm obtains good quality solutions but it must be adapted for each instance to be solved. A parallel hyperheuristic-based model was used to parallelize the approach and to avoid the requirement of the adaptation step of the memetic algorithm. The model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. The main operation of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this purpose two different criteria have been used in order to select the configurations. The first one is based on the improvements that each configuration is able to achieve along the executions. The second one tries to detect synergies among the configurations, i.e., detect which configurations obtain better solutions when they are cooperating. Computational results obtained for two different real-world instances of the FAP demonstrate the validity of the proposed model. The new designed schemes have made possible to improve the previously known best frequency plans for a real-world network.     

A proposition on memes and meta-memes in computing for higher-order learning

In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.     

求带释放时间的半导体煅烧排序的最短交付时间的一个高效PTAS

本文研究一个目标是最小化最大交付时间的能分批处理的非中断单机排序问题．这个问题来源于半导体制造过程中对芯片煅烧工序的排序．煅烧炉可以看成一个能同时最多加工B（〈n）个工件的处理机．此外，每个工件有一个可以允许其加工的释放时间和一个完成加工后的额外交付时间．该问题就是将工件分批后再依批次的排序加工，使得所有工件都交付后所需的时间最短．我们设计了一个用时O（f（l／ε）n^5/2）的多项式时间近似方案，其中关于1／ε的指数函数厂（1／ε）对固定的ε是个常数.     

Optimal methods for batch processing problem with makespan and maximum lateness objectives

In this paper we consider the problem of scheduling n jobs on a single batch processing machine in which jobs are ordered by two customers. Jobs belonging to different customers are processed based on their individual criteria. The considered criteria are minimizing makespan and maximum lateness. A batching machine is able to process up to b jobs simultaneously. The processing time of each batch is equal to the longest processing time of jobs in the batch. This kind of batch processing is called parallel batch processing. Optimal methods for three cases are developed: unbounded batch capacity, b > n, with compatible job groups and bounded batch capacity, b  n, with compatible and non compatible job groups. Each job group represents a different class of customers and the concept of being compatible means that jobs which are ordered by different customers are allowed to be processed in a same batch. We propose an optimal method for the problem with incompatible groups and unbounded batches. About the case when groups are incompatible and bounded batches, our proposed method is considered as optimal when the group with maximum lateness objective has identical processing times. We regard this method, however, as a heuristic when these processing times are different. When groups are compatible and batches are bounded we consider another problem by assuming the same processing times for the group which has the maximum lateness objective and propose an optimal method for this problem.     

Promoting cooperation in selfish computational grids

In distributed computing, the recent paradigm shift from centrally-owned clusters to organizationally distributed computational grids introduces a number of new challenges in resource management and scheduling. In this work, we study the problem of Selfish Load Balancing which extends the well-known load balancing (LB) problem to scenarios in which each processor is concerned only with the performance of its local jobs. We propose a simple mathematical model for such systems and a novel function for computing the cost of the execution of foreign jobs. Then, we use the game-theoretic framework to analyze the model in order to compute the expected result of LB performed in a grid formed by two clusters. We show that, firstly, LB is a socially-optimal strategy, and secondly, for similarly loaded clusters, it is sufficient to collaborate during longer time periods in order to make LB the dominant strategy for each cluster. However, we show that if we allow clusters to make decisions depending on their current queue length, LB will never be performed. Then, we propose a LB algorithm which balances the load more equitably, even in the presence of overloaded clusters. Our algorithms do not use any external forms of compensation (such as money). The load is balanced only by considering the parameters of execution of jobs. This analysis is assessed experimentally by simulation, involving scenarios with multiple clusters and heterogeneous load.     

A single machine batch scheduling problem with bounded batch size

We address a single-machine batch scheduling problem to minimize total flow time. Processing times are assumed to be identical for all jobs. Setup times are assumed to be identical for all batches. As in many practical situations, batch sizes may be bounded. In the first setting studied in this paper, all batch sizes cannot exceed a common upper bound. In the second setting, all batch sizes share a common lower bound. An optimal solution consists of the number of batches and their (integer) size. We introduce an efficient solution for both problems.     

A hybrid two-stage transportation and batch scheduling problem

We study the coordinated scheduling problem of hybrid batch production on a single batching machine and two-stage transportation connecting the production, where there is a crane available in the first-stage transportation that transports jobs from the warehouse to the machine and there is a vehicle available in the second-stage transportation to deliver jobs from the machine to the customer. As the job to be carried out is big and heavy in the steel industry, it is reasonable assumed that both the crane and the vehicle have unit capacity. The batching machine processes a batch of jobs simultaneously. Each batch occur a setup cost. The objective is to minimize the sum of the makespan and the total setup cost. We prove that this problem is strongly NP-hard. A polynomial time algorithm is proposed for a case where the job transportation times are identical on the crane or the vehicle. An efficient heuristic algorithm for the general problem is constructed and its tight worst-case bound is analyzed. In order to further verify the performance of the proposed heuristics, we develop a lower bound on the optimal objective function. Computational experiments show that the heuristic algorithm performs well on randomly generated problem instances.     

Scheduling on a Batch Processing Machine with Split Compatibility Graphs

We consider the problem of minimizing the makespan on a batch processing machine, in which jobs are not all compatible. Only compatible jobs can be included into the same batch. This relation of compatibility is represented by a split graph. All jobs are available at the same date. The capacity of the batch processing machine is finite or infinite. The processing time of a batch is given by the processing time of the longest job in the batch. We establish the NP-hardness of the general problem and present polynomial algorithms for several special cases.     

A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints

This study concerns the domain of cyclic scheduling. More precisely we consider the cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are cyclic and are subjected to linear precedence constraints. First we review some approaches in the field of cyclic scheduling and present the cyclic job shop scheduling problem definition, which has an open complexity. Then we present a general approach for solving it, based on the coupling of a genetic algorithm and a scheduler. This scheduler utilises a Petri-net modelling the linear precedence constraints between cyclic tasks. The goal of this genetic algorithm is to propose an order of priority for jobs on the machines, to be used by the scheduler for solving resource conflicts. Finally a benchmark and some preliminary results of this approach are presented.