Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid

In this paper, we will discuss a multi-camera tracking system, where we place a stress on a highway through vehicle plate recognition. Generally, recognizing a vehicle plate for a toll-gate system or parking system is easier than recognizing a car plate for the highway system. There are many cameras installed on the highway to capture images and every camera has different angles of images. As a result, the images are captured in varied imaging conditions and not focusing on the vehicle itself. Therefore, we need a system that is able to recognize the object first. However, such a system consumes a large amount of time to complete the whole process. To overcome this drawback, we installed this process with grid computing as a solution and a future view of a multi-camera tracking system. We construct a multi-camera tracking system that consists of many cameras, a grid computer system, a data base and a network. Each camera provides a view of some limited partial space out of the vast universe. These partial views obtained by plural cameras re-build the spatial universe in the system. The grid computer system provides with the car plate recognition system. The data base provides the linked information obtained through many cameras and car plate recognition system. The network connects all grid computer subsystems with the data base system.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

Priority-rule based scheduling of chemical batch processes

Robust Process Scheduling under Uncertainty with Regret

The design and scheduling of chemical batch processes: Computational complexity studies

Batch selection, assignment and sequencing in multi-stage multi-product processes

IDR (s) is a family of fast algorithms for iteratively solving large nonsymmetric linear systems. With cluster computing and in particular with Grid computing, the inner product is a bottleneck operation. In this paper, three techniques are investigated for alleviating this bottleneck. First, a recently proposed IDR (s) algorithm that is highly efficient and stable is reformulated in such a way that it has a single global synchronization point per iteration step. Second, the so-called test matrix is chosen so that the work, communication, and storage involving this matrix is minimized in multi-cluster environments. Finally, a methodology is presented for a-priori estimation of the optimal value of s using only problem and machine-based parameters. Numerical experiments applied to a 3D convection–diffusion problem are performed on the DAS-3 Grid computer, demonstrating the effectiveness of our approach. Copyright © 2011 John Wiley & Sons, Ltd.

Grid Computing is a newly emerging technology that enables users to share a large number of computing resources distributed over a network. Due to that the grid computational resources are not storable, the advance reservation of these resources are necessary for users to request resources from multiple scheduling systems at a specific time. In this paper, we use auction-based scheduling method for resource reservation. We propose a variant version of traditional ascending auction for automatic resource reservation and introduce two novel heuristic strategies to guide agents on participating actions. We also compare our bidding strategies with several other different bidding strategies for the computational resource reservation in different scenarios. The results of experiments show that our heuristic bidding strategies outperforms those methods in those cases.

Asynchronous grid computation for American options derivatives

High-throughput proteome annotation refers to the activity of extracting information from all proteins in a particular organism using bioinformatics software on a high performance computing platform such as the grid. The Encyclopedia of Life (EOL) project [1] aims to catalog all proteins in all species for public benefits using an Integrative Genome Annotation Pipeline [2] (iGAP). The intrinsic complexity of the pipeline makes iGAP an ideal life sciences application to drive grid software development. It is a flagship application for the TeraGrid project [3]. The deployment of iGAP on the grid using grid middleware and mediator software has been described previously [4]. The heterogeneous and distributed computing environment on the grid requires an interactive user interface where jobs may be submitted and monitored. Here we describe our international collaborative effort in creating a grid portal solution for grid computing in life sciences under the auspices of PRAGMA [5]. Specifically, the development of GridMonitor for interactive monitor of iGAP workflow, and the use of a GridSpeed [6] generated iGAP application portal are described. The portal solution was part of the EOL demonstration at Supercomputing 2003 (SC'03) [7], where resources from 6 institutions on 5 continents are utilized to annotate more than 36,000 proteins from various species. It is a testimony to the necessity and expediency for international collaboration in an increasingly global grid computational environment to advance life sciences research.

Parallel asynchronous iterative algorithms exhibit features that are extremely well-suited for Grid computing, such as lack of synchronization points. Unfortunately, they also suffer from slow convergence rates. In this paper we propose using asynchronous methods as a coarse-grained preconditioner in a flexible iterative method, where the preconditioner is allowed to change in each iteration step. A full implementation of the algorithm is presented using Grid middleware that allows for both synchronous and asynchronous communication. Advantages and disadvantages of the approach are discussed. Numerical experiments on heterogeneous computing hardware demonstrate the effectiveness of the proposed algorithm on Grid computers, with application to large 2D and 3D bubbly flow problems.

The scheduling of batch processes is a widely researched field of chemical engineering. Over the last few decades, a great number of tools have been developed for industrial examples and literature problems. These methods vary not only in their applied model, but in their representation of the problem inputs as well. The most well-known representations are the State-Task Network, the Resource-Task Network, the State Sequence Network, and the S-graph. While the latter also serves as the mathematical model for the related optimization approaches, the others only act as an intermediate model between the raw problem data and the model used for optimization, e.g., a mixed-integer linear programming model. The eS-graph model is a generalization of the S-graph, where the one-to-one relation between nodes and tasks has been relaxed, allowing a much wider range of scheduling problems to be tackled. Processes may simultaneously occupy several units, and release them at different stages of execution, and certain stages of separate processes can be forced to overlap in time. As in the case of the S-graph, the eS-graph models can be solved to optimality by specially designed combinatorial algorithms or serve as a basis for precedence based linear programming formulations. In this work, the modelling capacities of the eS-graph framework are illustrated via a Polymer production case study, where complex timing constraints are present.

The scheduling of batch process is a kind of NP-complete problem and has been the interest of research for many years. Various methods have been applied to solve such problems and simulated annealing (SA) is the most efficient algorithm. But SA is very slow when the problem size is large. In this work, particle swarm optimization (PSO) was applied to solve the scheduling problem of multiproduct batch process. The results show that PSO is a powerful method for solving the batch process scheduling problems and is superior to the widely used simulated annealing.

In Chap. 4, a novel mathematical approach to the simultaneous production planning and scheduling of continuous parallel units producing a large number of final products that can be classified into product families is developed. This problem appears in many stages of operation in the process industries, including packing in batch and continuous production facilities. Thus, it is quite important since it arises in a number of different production environments (e.g., food and beverage industry, consumer packed goods). The proposed approach has been used to solve a complex real-world problem in the continuous bottling stage of the Cerveceria Cuauhtemoc Moctezuma beer production facility, located in Mexico.

Autonomic Computing is an emerging research area which deals with systems capable of self-managing their tasks in an intelligent, dynamic and automatic manner. It addresses the increasing complexity of current computing systems, which become more and more di cult to integrate and maintain, mainly due to their scale growth and the need for specialized labor. Grid computing requires self-manageable features, essentially due to the complexity associated to its task dynamical behavior, information sharing, di erent user pro les, geographically distributed resources and heterogeneity. The need for those features has encouraged many research e orts, most of them focused on proposing autonomic architectures (structuring components to provide services) and the integration of tools and services. Few research initiatives address the study, design and evaluation of techniques and models to support and improve grid autonomic aspects. This scenario has motivated the Middleware for High Performance Computing (MidHPC) project which investigates self-manageable supports for data distribution, fault tolerance, security and process scheduling. Promising results were obtained by considering historical resource consumption averages. Results encouraged additional e orts to assess process behavior variations and their application to improve autonomic aspects of grid computing. Comprehension of those variations in behavior motivated this thesis, which employs dynamical system concepts and intelligent techniques to model and predict process behavior, aiming at optimizing scheduling operations in grid computing environments. Process communication operations were predicted and used to parameterize a new process scheduling policy which attempts to minimize communication and processing costs. This policy was compared to others from the literature. Results con rm relevant contributions and motivate the adoption of the proposed approach to improve other grid autonomic aspects.