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.
A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis
