Web-based access to the Grid using the Grid Resource Broker portal

2002 ◽  
Vol 14 (13-15) ◽  
pp. 1145-1160 ◽  
Author(s):  
Giovanni Aloisio ◽  
Massimo Cafaro
Keyword(s):  
Grid Resource ◽  
Web Based ◽  
Resource Broker ◽  
Grid Resource Broker

scholarly journals The Grid Resource Broker, A Ubiquitous Grid Computing Framework

2002 ◽  
Vol 10 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Giovanni Aloisio ◽  
Massimo Cafaro ◽  
Euro Blasi ◽  
Italo Epicoco
Keyword(s):  
Large Scale ◽  
Data Grids ◽  
Grid Resource ◽  
Web Based ◽  
Grid Portal ◽  
Secure Access ◽  
Computational Data ◽  
Resource Broker ◽  
Computing Framework ◽  
Grid Resource Broker

Portals to computational/data grids provide the scientific community with a friendly environment in order to solve large-scale computational problems. The Grid Resource Broker (GRB) is a grid portal that allows trusted users to create and handle computational/data grids on the fly exploiting a simple and friendly web-based GUI. GRB provides location-transparent secure access to Globus services, automatic discovery of resources matching the user's criteria, selection and scheduling on behalf of the user. Moreover, users are not required to learn Globus and they do not need to write specialized code or to rewrite their existing legacy codes. We describe GRB architecture, its components and current GRB features addressing the main differences between our approach and related work in the area.

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.

eNANOS Grid Resource Broker

2005 ◽  
pp. 111-121 ◽  
Author(s):  
Ivan Rodero ◽  
Julita Corbalán ◽  
Rosa M. Badia ◽  
Jesús Labarta
Keyword(s):  
Grid Resource ◽  
Resource Broker ◽  
Grid Resource Broker

Data Model for Describing Grid Resource Broker Capabilities

2008 ◽  
pp. 39-52 ◽  
Author(s):  
Attila Kertész ◽  
Ivan Rodero ◽  
Francesc Guim
Keyword(s):  
Data Model ◽  
Grid Resource ◽  
Resource Broker ◽  
Grid Resource Broker
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