A Global Grid for Analysis of Arthropod Evolution

2005 ◽  
Author(s):  
C.A. Stewart ◽  
R. Keller ◽  
R. Repasky ◽  
M. Hess ◽  
D. Hart ◽  
...  
Keyword(s):  
Arthropod Evolution ◽  
Global Grid

Global Grid Architecture Concept

1999 ◽  
Author(s):  
Leonard J. Schiavone
Keyword(s):  
Grid Architecture ◽  
Global Grid

Combinatorial Double Auction Based Meta-scheduler for Medical Image Analysis Application in Grid Environment

2020 ◽  
Vol 13 (5) ◽  
pp. 999-1007
Author(s):  
Karthikeyan Periyasami ◽  
Arul Xavier Viswanathan Mariammal ◽  
Iwin Thanakumar Joseph ◽  
Velliangiri Sarveshwaran
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Grid Resource ◽  
Resource Broker ◽  
Analysis Application ◽  
Local Grid ◽  
Grid Resource Broker ◽  
Global Grid ◽  
Grid Site

Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

Is Globalization Killing Local Business?

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Technology Markets ◽  
The Past ◽  
Local Business ◽  
Use Of Technology ◽  
Local Store ◽  
Global Arena ◽  
Primary Mission ◽  
Global Grid

The fact that the influence of globalization has been driven by dramatic changes is not one of those “blinding flashes of the obvious” that seems to sneak up on us. It is very evident and even predictable. Advances in technology, markets, and environments were precursors to the big changes we are now talking about. Advances in technology have led to the current global grid driven by information. The primary mission of business is to provide solutions, and this technology explosion has provided opportunities and market applications for those solutions. Local businesses now have an opportunity to move beyond their restricted geography of the past into the global arena with the use of technology. A local store in a remote village in Kentucky has the same opportunity as a large store in London to access global customers. These could be exciting times for local businesses if they use technology to their advantage.

A global grid model for the correction of the vertical zenith total delay based on a sliding window algorithm

GPS Solutions ◽  
2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Liangke Huang ◽  
Ge Zhu ◽  
Lilong Liu ◽  
Hua Chen ◽  
Weiping Jiang
Keyword(s):  
Sliding Window ◽  
Total Delay ◽  
Grid Model ◽  
Zenith Total Delay ◽  
Global Grid

scholarly journals Arthropod Evolution: Brine shrimp add salt to the stew

1995 ◽  
Vol 5 (12) ◽  
pp. 1330-1333 ◽  
Author(s):  
Terri A. Williams ◽  
Lisa M. Nagy
Keyword(s):  
Brine Shrimp ◽  
Arthropod Evolution

Anomaly detection based on two global grid motion templates

2018 ◽  
Vol 60 ◽  
pp. 6-12 ◽  
Author(s):  
Shifeng Li ◽  
Yuqiang Yang ◽  
Chunxiao Liu
Keyword(s):  
Anomaly Detection ◽  
Global Grid

Arthropod Evolution

Entomology ◽  
1980 ◽  
pp. 3-21
Author(s):  
Cedric Gillott
Keyword(s):  
Arthropod Evolution

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

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