scholarly journals Job Scheduling and Resource sharing on cloud platform based on Improved Bees Algorithm.

2018 ◽  
Author(s):  
Tsitsi Mubaiwa ◽  
Chiedza Hwata ◽  
Wellington Makondo ◽  
Gladman Jekese ◽  
Tendai Marengereke ◽  
...  
Keyword(s):  
Resource Sharing ◽  
Job Scheduling ◽  
Bees Algorithm ◽  
Cloud Platform

Current Trends in Cloud Computing for Data Science Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 80-99
Author(s):  
Syed Imran Jami ◽  
Siraj Munir
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Data Science ◽  
Job Scheduling ◽  
Resource Scheduling ◽  
Data Intensive ◽  
Organizational Issues ◽  
Current Trends ◽  
Recent Trends ◽  
And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

scholarly journals Research on Information Resource Sharing and Big Data of Sports Industry in the Background of OpenStack Cloud Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chuan Mou ◽  
Ye Cheng
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Data Analysis ◽  
Resource Sharing ◽  
Positive Impact ◽  
Rapid Development ◽  
Information Resources ◽  
Cloud Platform ◽  
Positive Role ◽  
Sports Industry

The rapid development of information technology and Internet makes the sports information resources retrieval service more convenient and quick; sports policy in recent years lays a foundation for the development of the Internet + sports, the development of sports industry in the process of our country economy level of development status, and the development of sports industry into the era of information and big data. This paper takes OpenStack cloud platform as the research basis (1) to realize the sharing of sports industry information resources in OpenStack cloud technology and (2) to realize big data analysis of sports industry and (3) empirical research on big data of sports industry. The main content is to realize the construction of sports resources informatization based on the OpenStack cloud platform. Through the analysis and empirical study of the big data of the sports industry, the influence of the development of the sports industry in the process of China’s economic development is discussed. In this paper, the experimental results show that the sports industry showed a positive impact in the process of economic development, the sports economy for the development of the economy, the contribution rate reached 11.77%, the sports industry for the development of the economy, the pull rate of 1.056%, based on the cloud platform of information resources sharing of data analysis, sports industry for the development of the economy has a positive role in promoting.

Research on Across-Cultural Communication in College English Teaching Based on Cloud Platform

2017 ◽  
Vol 14 (1) ◽  
pp. 89-93 ◽  
Author(s):  
Shuli Cui
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
College Education ◽  
Cultural Communication ◽  
Cloud Platform ◽  
Cultural Backgrounds ◽  
College English ◽  
English Teaching ◽  
Cross Cultural Communication ◽  
Computing Services

Cross-cultural communication is a field of study that looks at how people from differing cultural backgrounds communicate. It is applied in college English teaching more and more. Cloud computing has an important value and significance to solve the problem of college education resource sharing and construction. The construction of cloud platform in universities is to integrate all sorts of educational resources and it can provide rapid and convenient resource storage, sharing, learning and computing services. In this paper we analyse the advantages of cloud platform in the college English teaching, further build a college network English teaching system based on cloud computing, and probe into the construction of an autonomous learning mode in the light of the problems existing in across-cultural communication.

An Instructional Assistant Collaboration Server System Based on Cloud Platform

2016 ◽  
Vol 835 ◽  
pp. 816-820
Author(s):  
Wei Hong Wang ◽  
Qing Zhang ◽  
Yu Hui Cao ◽  
Dian Yuan Shi
Keyword(s):  
Education System ◽  
Resource Sharing ◽  
Service System ◽  
Experimental System ◽  
Cloud Platform ◽  
Experimental Platform ◽  
Personalized Services ◽  
Rate Improvement ◽  
Instructional Assistant ◽  
Server System

In order to take full advantage of available resources in existed campus experimental platform and be able to integrate and optimize resources of the platform, increase the rate of resource sharing, and promote the reform of the existing education system, this paper built an instructional assistant collaboration service system based on cloud platform, named briefly IACSS, in campus for teaching assistant. At first, the existed experimental system was discussed deeply. Then, aiming at the lacking of efficiency and resource sharing degree and so on, the system platform of IACSS based on cloud and mobile technology introduces the data fusion and interaction. The architecture of IACSS was given after that. Then, the system platform was built. The system platform of IACSS supports the optimization of resources and personalized services customized mechanism. And it also supports the accessed and cooperated with each other at anytime and anywhere. Nowadays, the system has been successfully deployed in the campus and the test run, the practice shows that the platform can reasonably integrate resources, improve resource sharing rate, improvement of the existing education system.

scholarly journals Research and Exploit of Resource Sharing Strategy at IHEP

2019 ◽  
Vol 214 ◽  
pp. 03014
Author(s):  
Xiaowei JIANG ◽  
Jingyan Shi ◽  
Jiaheng Zou ◽  
Qingbao Hu ◽  
Ran Du ◽  
...  
Keyword(s):  
Resource Sharing ◽  
High Energy Physics ◽  
Job Scheduling ◽  
High Energy ◽  
Server Side ◽  
Typical Situation ◽  
Sharing Strategy ◽  
Resource Group ◽  
Group Information ◽  
Client Side

At IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), computing resources are contributed by different experiments including BES, JUNO, DYW, HXMT, etc. The resources were divided into different partitions to satisfy the dedicated experiment data processing requirements. IHEP had a local Torqu&Maui cluster with 50 queues serving for above 10 experiments. The separated resource partitions leaded to imbalance resource load. In a typical situation, BES resource partition was quite busy without free slot but still with lots of jobs in idle, while JUNO resources are free and wasted seriously. After moving resources from Torque&Maui to HTCondor in 2016, job scheduling efficiency has been improved a lot. In order to balance the imbalance resource load, we designed an efficient sharing strategy to improve the overall resourceutilization. We created an unified pool shared by all experiments. For each experiment, resources are divided into two parts: dedicated resource and sharing resource. The slots in dedicated resource only run jobs from its own experiment, and the slots in sharing resource are shared by jobs from all experiments. Default ratio of dedicated resource to sharing resource is 1:4. To maximize the sharing effectiveness, the ratio is dynamically adjusted between 0:5 and 4:1 based on the number of jobs submitted by each experiment. We have developed a central control system to decide how many resources can be allocated to each experiment group. This system is implemented at two sides: server side and client side. A management database is built at server side, which is storing resource, group and experiment information. Once the sharing ratio needs to be adjusted, resource group will be changed and updated into database. The resource group information is published to the server buffer in real-time. The client periodically pulls resource group information from server buffer via https protocol And resource scheduling configuration at client side is changed based on the resource group information. With this method, share ratio can be modified and deployed dynamically. Sharing strategy is implemented with HTCondor. ClassAd mechanism and accounting-group in HTCondor facilitate to utilizethe sharing strategy at IHEP computing cluster. With the sharing strategy, resource usage has been improved dramatically.

Research on Navigation Information Resources Platform of Maritime Departments Based on Cloud Computing

2014 ◽  
Vol 687-691 ◽  
pp. 2933-2937
Author(s):  
Zhang Hai Zhou
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Information Resources ◽  
Cloud Platform ◽  
Application System ◽  
Application Mode ◽  
Maritime Navigation ◽  
Business Data ◽  
New Generation ◽  
Navigation Information

Nowadays, cloud computing is widely concerned, it is a new generation of information resource sharing and application mode. It provides IT resource services in way of virtualization and resource pool, which is the extension and development of network distributed computation, and greatly improves the sharing and utilization of information resources. This paper describes the significance of constructing current maritime navigation information resources of cloud platform, presents the research methods, and designs the platform construction structure. By the cloud platform, we can realize the integration of existing maritime information resources, the analysis of business process, the cleaning, selection and aggregation of business data, and finally achieve the unified management of navigation application system and business data of maritime Departments.

The Design of Universities Resource-Sharing Network Teaching System Based on Cloud Computing

2013 ◽  
Vol 397-400 ◽  
pp. 2430-2434
Author(s):  
Qiu Yan Guo
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Capital Investment ◽  
Online Teaching ◽  
System Development ◽  
Cloud Model ◽  
Cloud Platform ◽  
Teaching Resources ◽  
Network Teaching ◽  
New Ideas

This paper based on the ideology of cloud computing, using the cloud platform of Google App Engine and Web Services technology, utilizing the eclipse, Google Plugin for Eclipse and Java Web, building a sharing platform for university network teaching resources. The design and implementation of the system reduce the capital investment and the burden of system development and maintenance costs, and will bring a great convenience to teachers "teaching" and students "learning. Practice shows that the cloud platform of teaching resources has high stability and scalability than traditional services technology, and can provide new ideas for the system design, so online teaching resource sharing cloud model can be achieved.

Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization

2018 ◽  
Vol 11 (4) ◽  
pp. 72-86
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Particle Swarm Optimization ◽  
Resource Sharing ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Grid ◽  
Swarm Optimization ◽  
Extremal Optimization ◽  
Comparison Results

Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.

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