DGridSim: A REAL-TIME DATA GRID SIMULATOR WITH HIERARCHICAL JOB AND DATA SCHEDULING

2011 ◽  
Keyword(s):  
Real Time ◽  
Data Grid ◽  
Time Data ◽  
Data Scheduling ◽  
Real Time Data

scholarly journals Real-Time Data Scheduling of Flexible Job in Papermaking Workshop Based on Deep Learning and Improved Fuzzy Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shihong Dang ◽  
Wei Tang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Operating Cost ◽  
Optimal Solution ◽  
Time Data ◽  
Fuzzy Algorithm ◽  
Data Scheduling ◽  
Fuzzy Scheduling ◽  
Real Time Data ◽  
Scheduling Method

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise.

DGridSim: a multi-model discrete-event simulator for real-time data grid systems

SIMULATION ◽  
2014 ◽  
Vol 90 (11) ◽  
pp. 1209-1230 ◽  
Author(s):  
Atakan Doğan ◽  
Mustafa Müjdat Atanak ◽  
Safai Tandoğan ◽  
Reha Oğuz Altuğ ◽  
Hakan Güray Şenel
Keyword(s):  
Real Time ◽  
Discrete Event ◽  
Data Replication ◽  
Data Grid ◽  
Grid System ◽  
Advance Reservation ◽  
Time Data ◽  
Grid Systems ◽  
Data Scheduling ◽  
System Models

Data grid systems are utilized to share, manage, and process large data sets. On the other hand, an increasing number of applications with real-time constraints arise in several disciplines of science and engineering. The performance of a data grid system for real-time applications is highly dependent on the underlying job scheduling, data scheduling, and data replication algorithms and advance reservation mechanism. Thus, in the literature, there are numerous studies that propose solutions to the job/data scheduling, data replication, and advance reservation problems. In these studies, a number of simulators, emulators, or test beds have been used to evaluate the proposed algorithms. Furthermore, these simulators/emulators usually adopt fixed-grid models, which in turn dictate specific job/data scheduling and data replication mechanisms. In the literature, there is no unified framework for modeling grid systems with different architectures, which can allow researchers to develop new grid system models and evaluate them in a flexible manner. This paper presents a unique framework for modeling real-time data grid systems that attempts to unify a large class of job scheduling, data scheduling, and data replication algorithms based on several system services. Then, in order to enable the development of these algorithms under different system models, DGridSim is realized to be a multi-model discrete-event simulator, and its capabilities are exemplified by means of a set of simulation results. The main contribution of the research is DGridSim, which can model and simulate a variety of different data grid system models by means of several system services and their interactions.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data
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