An Adaptive Scheduling Mechanism for Analytical Workflow Model

2017 ◽  
pp. 31-45
Author(s):  
Yan Yao ◽  
Jian Cao
Keyword(s):  
Adaptive Scheduling ◽  
Workflow Model

Exploring Cloud-Based Distributed Disaster Management With Dynamic Multi-Agents Workflow System

2018 ◽  
pp. 167-195
Author(s):  
Mansura Habiba ◽  
Shamim Akhter
Keyword(s):  
Disaster Management ◽  
System Architecture ◽  
Dropout Rate ◽  
Adaptive Scheduling ◽  
Workflow Model ◽  
Workflow System ◽  
Disaster Situation ◽  
Multi Agent ◽  
Distributed Cloud ◽  
Dynamically Configurable

Natural disaster is one of the important topics in current researches. Disaster Management System (DMS) is a complex system and needs to perform a collection of tasks collaboratively along with the potentiality to change the configurations of the system dynamically. In the research era of workflow model, existing models mainly deal with temporal and static constrains. However they cannot be used to keep pace with an uncertainly dynamic system like disaster management. Considering all these significant DMS attributes we have designed a new dynamically configurable and changeable workflow model with the support of adaptive scheduling, for both successful and failed situations, and implemented in a distributed cloud system to maintain the rescue and reorganization activities of disaster situation. In order to simplify the system architecture, we have used Multi Agent System (MAS) for our design. The proposed system achieves a comparatively higher rate of successful job completion-higher rescheduling success rate and comparatively lower dropout rate.

Exploring Cloud-Based Distributed Disaster Management With Dynamic Multi-Agents Workflow System

2019 ◽  
pp. 165-194
Author(s):  
Mansura Habiba ◽  
Shamim Akhter
Keyword(s):  
Disaster Management ◽  
System Architecture ◽  
Dropout Rate ◽  
Adaptive Scheduling ◽  
Workflow Model ◽  
Workflow System ◽  
Disaster Situation ◽  
Multi Agent ◽  
Distributed Cloud ◽  
Dynamically Configurable

Natural disaster is one of the important topics in current researches. Disaster Management System (DMS) is a complex system and needs to perform a collection of tasks collaboratively along with the potentiality to change the configurations of the system dynamically. In the research era of workflow model, existing models mainly deal with temporal and static constrains. However they cannot be used to keep pace with an uncertainly dynamic system like disaster management. Considering all these significant DMS attributes we have designed a new dynamically configurable and changeable workflow model with the support of adaptive scheduling, for both successful and failed situations, and implemented in a distributed cloud system to maintain the rescue and reorganization activities of disaster situation. In order to simplify the system architecture, we have used Multi Agent System (MAS) for our design. The proposed system achieves a comparatively higher rate of successful job completion-higher rescheduling success rate and comparatively lower dropout rate.

A Study on Adaptive Scheduling Priority Control for Layered Cell Configuration

2017 ◽  
Vol E100.B (2) ◽  
pp. 372-379
Author(s):  
Atsushi NAGATE ◽  
Teruya FUJII ◽  
Masayuki MURATA
Keyword(s):  
Adaptive Scheduling ◽  
Cell Configuration

scholarly journals Towards workload-adaptive scheduling for HPC clusters

2020 ◽  
Author(s):  
Alexander V. Goponenko ◽  
Ramin Izadpanah ◽  
Jim M. Brandt ◽  
Damian Dechev
Keyword(s):  
Adaptive Scheduling

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

Challenges and Opportunities in Collaborative Vulnerability Research Workflows

2020 ◽  
Vol 64 (1) ◽  
pp. 420-424
Author(s):  
Ryan Mullins ◽  
Deirdre Kelliher ◽  
Ben Nargi ◽  
Mike Keeney ◽  
Nathan Schurr
Keyword(s):  
Human Performance ◽  
Collaborative Work ◽  
Research Process ◽  
Workflow Model ◽  
Human Vulnerability ◽  
Research Teams ◽  
Reasoning System ◽  
Challenges And Opportunities ◽  
New Research ◽  
Future Work

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.

Adaptive scheduling in the cloud - SLA for Hadoop job scheduling

2015 ◽  
Author(s):  
Deveeshree Nayak ◽  
Venkata Swamy Martha ◽  
David Threm ◽  
Srini Ramaswamy ◽  
Summer Prince ◽  
...  
Keyword(s):  
Job Scheduling ◽  
Adaptive Scheduling

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

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