China 
Africa 
Explore chapters and articles related to this topic
Resource Management for MapReduce Jobs Performing Big Data Analytics
Published in Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, Big Data Management and Processing, 2017
Norman Lim, Shikharesh Majumdar
Figure 6.11 presents a diagram showing an environment using MRCP-RM. Users submit MapReduce jobs to the system, which are placed in a job queue. If the resource manager is available (i.e., not busy mapping the previous set of jobs), it invokes MRCP-RM to map the set of jobs in the job queue. MRCP-RM uses IBM CPLEX [42] to generate an OPL Model, which is an implementation of the CP Model using IBM's Optimization Programming Language (OPL). More specifically, an OPL Model that has new constraints added for each of the tasks that have started but not completed executing is created. To solve the OPL Model, MRCP-RM invokes IBM CPLEX's CP Optimizer solving engine. Note that if this is not the first time that MRCP-RM is invoked, the set of jobs to process includes the new jobs retrieved from the job queue as well as the jobs that are scheduled or currently executing, but have not completed yet. MRCP-RM schedules all the newly submitted jobs (i.e., jobs in the job queue), but also remaps the tasks of jobs that have not started executing to provide the most flexibility in scheduling in order to minimize the number of late jobs. For example, the tasks of a new job with an earlier deadline may need to be mapped in the place of the tasks of a previously scheduled job that has a later deadline. Once a solution to the OPL Model is found, the resource manager is able to determine the tasks to assign to a particular resource (matchmaking) and when the tasks assigned to a particular resource should start to execute (scheduling).
Algebraic Modeling and Optimization
Published in Mariano Martín Martín, Introduction to Software for Chemical Engineers, 2019
Ricardo M. Lima, Ignacio E. Grossmann
IBM ILOG CPLEX Optimization Studio This software features a specific language denoted by Optimization Programming Language (OPL), and it has interfaces to several programming languages and applications to help on the deployment of business solutions.
Filter-and-fan approaches for scheduling flexible job shops under workforce constraints
Published in International Journal of Production Research, 2022
In order to assess the performance of our F&F approaches, we conducted extensive computational tests. They were performed on a PC with an Intelő Core™ i7-4770 CPU, running at 3.4 GHz, with 16 GB of RAM under a 64-bit version of Windows 8. All algorithms were implemented in Java (JRE 1.8.0_191), using Eclipse (Eclipse IDE for Java Developers, Oxygen 4.7). We used IBM's Optimization Programming Language (OPL) to implement the CP model and applied the ILOG CPLEX CP Optimizer in version 12.7 as a CP solver.
COTS software integration for simulation optimization coupling: case of ARENA and CPLEX products
Published in International Journal of Modelling and Simulation, 2019
Valeria Borodin, Jean Bourtembourg, Faicel Hnaien, Nacima Labadie
IBM® ILOG® CPLEX® Optimization Studio2 is an analytical decision support toolkit dedicated to optimization models development and deployment, by using mathematical and constraint programming. As illustrated in Figure 4, this toolkit incorporates an Integrated Development Environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizers, based on Concert Technology.
Solution algorithms for shortest path network interdiction with symmetric and asymmetric information
Published in International Journal of Systems Science: Operations & Logistics, 2023
In this Section, numerical experiments have been reported to examine the performance of the proposed solution methodologies and compare the results with those provided using the duality based approach. The models have been implemented with Optimization Programming Language (OPL) and solved using CPLEX 12.8 installed on a PC with an Intel Core i5-7400, 3.00 GHz processor and 8 GB of RAM.