The Oil and Gas Pipeline SCADA System Based on Cloud Computing and the Scheduling Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 1406-1410
Author(s):  
Liu Miao ◽  
Man Cang Yuan ◽  
Cheng Qiang Wang ◽  
Rui Qiang Gao
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Supervisory Control ◽  
Oil And Gas ◽  
High Reliability ◽  
Scheduling Algorithm ◽  
Gas Pipeline ◽  
Application Framework ◽  
Time Data ◽  
Scada System

For improving the reliability and resource utilization of oil and gas pipeline Supervisory Control and Data Acquisition (SCADA) system, the application framework of oil and gas pipeline SCADA system based on cloud computing and a high reliability scheduling algorithm is proposed in the paper. Simulation results show the SCADA system based on cloud computing with new scheduling algorithm can realize history data backup in many computers and real-time data processed with primary-backup copy. So, the new application framework and new scheduling algorithm can provide better reliability and improve the resource utilization of system.

The Design of the Logistics Information Sharing Platform Based on Cloud Computing

2013 ◽  
Vol 734-737 ◽  
pp. 3220-3223
Author(s):  
Ya Bing Jiao
Keyword(s):  
Cloud Computing ◽  
Information Sharing ◽  
Resource Utilization ◽  
High Reliability ◽  
Computing Technology ◽  
Technology Application ◽  
High Resource ◽  
Key Technologies ◽  
High Scalability ◽  
Logistics Information

Cloud computing is high scalability, high reliability, high resource utilization Characteristics. In this paper, according to the characteristics of the logistics information, combined with cloud computing technology to build the model of logistics information sharing platform based on cloud computing. On cloud computing technology application of key technologies in the logistics information sharing platform is analyzed. Finally, this paper has carried on the design of logistics information sharing platform based on cloud technology. With the use of logistics information sharing platform, the logistics information development play a powerful role in promoting.

scholarly journals Development and Application of Methane Leakage Monitoring System for Gas Transmission Pipeline

2021 ◽  
Vol 5 (6) ◽  
pp. 44-49
Author(s):  
Qijun Wang ◽  
Shiqi Wei
Keyword(s):  
Wireless Network ◽  
Oil And Gas ◽  
High Reliability ◽  
Gas Pipeline ◽  
Sensor System ◽  
Ultra Low Power ◽  
Methane Gas ◽  
Wide Range ◽  
Transmission Pipeline ◽  
Leakage Monitoring

Oil and gas pipeline transportation, as a relatively safe way of oil and gas transportation, undertakes most of the transportation tasks of crude oil and natural gas. Oil and gas pipeline accidents affect a wide range of consequences. Therefore, the oil and gas pipeline leakage detection is paid more and more attention. In this paper, ultra-low power methane gas sensor is selected to collect methane gas concentration in the air, and wireless network technology is used to build a wireless network sensor system with 4G function. Through the sensor distribution along the pipeline, it can intuitively and accurately judge whether there is a micro-leakage in the pipeline, and understand the diffusion situation after the leakage. The sensor system has high reliability and stability, and has high value of popularization and application.

scholarly journals Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1514
Author(s):  
Aroosa Mubeen ◽  
Muhammad Ibrahim ◽  
Nargis Bibi ◽  
Mohammad Baz ◽  
Habib Hamam ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Problem Size ◽  
Cloud Data ◽  
Sla Violation ◽  
Task Scheduling Algorithm ◽  
User Tasks ◽  
Load Balanced

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

Migration issues of SCADA systems to the cloud computing environment (review)

2020 ◽  
pp. 155-164
Author(s):  
R.G. Alakbarov ◽  
◽  
M.A. Hashimov ◽  
Keyword(s):  
Cloud Computing ◽  
Supervisory Control ◽  
Lower Cost ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Cloud Environment ◽  
Computing Environment ◽  
Gas Industry ◽  
Cloud Computing Environment ◽  
Scada Systems

The paper deals with the migration of SCADA (Supervisory Control and Data Acquisition) systems widely used in the monitoring and management of the oil and gas industry to the cloud computing environment. There arise various problems in data collection, transmission, and processing because of traditional SCADA systems being very expensive, inflexible, and complicated scalability. The transferring of the SCADA system's applications to the cloud environment reduces costs and improves scalability. The purchase of hardware and software is carried out at a lower cost than its installation and maintenance. In the article, the usage of cloud-based SCADA systems has been proposed for easy, safe, reliable and quick collection and processing of data from facilities installed in the oil and gas industry.

Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

2019 ◽  
Vol 14 (1) ◽  
pp. 1-26
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Cloud Computing ◽  
Decision Maker ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Platform ◽  
Start Time ◽  
Current Time ◽  
Task Scheduling Algorithm ◽  
First Come First Serve

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

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