Abstract: A method for resource management in grid computing systems includes defining user's demands on execution of a task as SLA (Service Level Agreements) information; monitoring states of resources in a grid to store the states as resource state information; calculating for each resource in the grid, based on the resource state information, an expected completion time of the task and an expected profit to be obtained by completing the task; creating an available resource cluster by using the expected execution time and the expected profit; and determining, if the SLA information is satisfied by the available resource cluster, a task processing policy for executing the task by using at least one resource in the available resource cluster. The available resource cluster is a set of resources having the expected completion time within a deadline of the task and the expected profit being positive.

Abstract: A method for resource management in grid computing systems includes defining user's demands on execution of a task as SLA (Service Level Agreements) information; monitoring states of resources in a grid to store the states as resource state information; calculating for each resource in the grid, based on the resource state information, an expected completion time of the task and an expected profit to be obtained by completing the task; creating an available resource cluster by using the expected execution time and the expected profit; and determining, if the SLA information is satisfied by the available resource cluster, a task processing policy for executing the task by using at least one resource in the available resource cluster. The available resource cluster is a set of resources having the expected completion time within a deadline of the task and the expected profit being positive.

Abstract: System and method provide an on-line high-performance diagnosis service for cardiovascular disorders. A client requests a high-performance diagnosis by transmitting real electrocardiographic treatment data and magnetocardiographic treatment data of a human body being a treatment object and virtual heart simulation parameters to a medical service server. The medical service server, in response to the diagnosis request, analyzes the real electrocardiographic treatment data to generate an electrocardiographic analysis result, and performs a virtual heart simulation using the simulation parameters to generate a pseudo electrocardiogram and magnetocardiogram.