Smart optimization of tracks for cloud computing

A method for optimization of tracks on a hard disk, comprising:

• determining, by a processor, I/O characteristics for a plurality of data clusters to be stored on the hard disk;
• determining, by the processor, a plurality of costs of operation of the hard disk for a plurality of possible placement configurations of the plurality of data clusters, wherein the determining uses the I/O characteristics for the plurality of data clusters;
• and
• determining, by the processor, an optimal placement map for the plurality of data clusters from the plurality of costs of operation, wherein the determining the plurality of costs of operation of the hard disk for the plurality of possible placement configurations of the plurality of data clusters comprises:
• determining an objective function with a plurality of cost function values as variables;
• determining one or more constraints in terms of the variables;
• graphing the constraints;
• and
• shading a feasibility region on the graph.

A computer program product for optimization of tracks on a hard disk, the computer program product comprising:

• a computer readable storage device having computer readable program code embodied therewith, the computer readable program code configured to:
• determine I/O characteristics for a plurality of data clusters to be stored on the hard disk;
• determine a plurality of costs of operation of the hard disk for a plurality of possible placement configurations of the plurality of data clusters, wherein the determining uses the I/O characteristics for the plurality of data clusters;
• and
• determine an optimal placement map for the plurality of data clusters from the plurality of costs of operation,
• wherein the computer readable program code configured to determine the plurality of costs of the operation of the hard disk for the plurality of possible placement configurations of the plurality of data clusters is further configured to:
• generate a set of solutions for a possible placement configuration;
• and
• for each solution, calculate a cost function value for a plurality of cost functions,
• wherein the computer readable program code configured to determine the optimal placement map for the plurality of data clusters from the plurality of costs of operation is further configured to:
• for each solution, calculate a membership value using the cost function values, wherein the membership value represents how good the solution is as part of the optimal placement map;
• for each solution, calculate a fitness value using the membership value, wherein the fitness value serves as an indication of how fit the solution is as part of the optimal placement map;
• retain the solutions with the fitness value greater than a predetermined threshold;
• determine whether at least one stopping condition has been met;
• in response to determining that none of the stopping conditions have been met, add new solutions to the retained solutions to generate the next set of solutions;
• and
• repeat the calculating of the membership value using the cost function values, the calculating of the fitness value using the membership value, and the retaining of the solutions with the fitness value greater than the predetermined threshold until at least one stopping condition has been met.

A computer program product for optimization of tracks on a hard disk, the computer program product comprising:

• a computer readable storage device having computer readable program code embodied therewith, the computer readable program code configured to:
• determine I/O characteristics for a plurality of data clusters to be stored on the hard disk;
• determine a plurality of costs of operation of the hard disk for a plurality of possible placement configurations of the plurality of data clusters, wherein the determining uses the I/O characteristics for the plurality of data clusters;
• and
• determine an optimal placement map for the plurality of data clusters from the plurality of costs of operation,
• wherein the computer readable program code configured to determine the plurality of costs of operation of the hard disk for the plurality of possible placement configurations of the plurality of data clusters is further configured to:
• determine an objective function with a plurality of cost function values as variables;
• determine one or more constraints in terms of the variables;
• graph the constraints;
• and
• shade a feasibility region on the graph.

A system, comprising:

• at least one hard disk comprising a plurality of tracks;
• a processor;
• and
• a computer readable storage medium operationally coupled to the processor, the computer readable storage medium having computer readable program code embodied therewith to be executed by the processor, the computer readable program code configured to:
• determine I/O characteristics for a plurality of data clusters to be stored on the hard disk;
• determine a plurality of costs of operation of the hard disk for a plurality of possible placement configurations of the plurality of data clusters, wherein the determining uses the I/O characteristics for the plurality of data clusters;
• and
• determine an optimal placement map for the plurality of data clusters from the plurality of costs of operation,
• wherein the computer readable program code configured to determine the plurality of costs of the operation of the hard disk for the plurality of possible placement configurations of the plurality of data clusters is further configured to:
• generate a set of solutions for a possible placement configuration;
• and
• for each solution, calculate a cost function value for a plurality of cost functions,
• wherein the computer readable program code configured to determine the optimal placement map for the plurality of data clusters from the plurality of costs of operation is further configured to:
• for each solution, calculate a membership value using the cost function values, wherein the membership value represents how good the solution is as part of the optimal placement map;
• for each solution, calculate a fitness value using the membership value, wherein the fitness value serves as an indication of how fit the solution is as part of the optimal placement map;
• retain the solutions with the fitness value greater than a predetermined threshold;
• determine whether at least one stopping condition has been met;
• in response to determining that none of the stopping conditions have been met, add new solutions to the retained solutions to generate the next set of solutions;
• and
• repeat the calculating of the membership value using the cost function values, the calculating of the fitness value using the membership value, and the retaining of the solutions with the fitness value greater than the predetermined threshold until at least one stopping condition has been met.

A system, comprising:

• at least one hard disk comprising a plurality of tracks;
• a processor;
• and
• a computer readable storage medium operationally coupled to the processor, the computer readable storage medium having computer readable program code embodied therewith to be executed by the processor, the computer readable program code configured to:
• determine I/O characteristics for a plurality of data clusters to be stored on the hard disk;
• determine a plurality of costs of operation of the hard disk for a plurality of possible placement configurations of the plurality of data clusters, wherein the determining uses the I/O characteristics for the plurality of data clusters;
• and
• determine an optimal placement map for the plurality of data clusters from the plurality of costs of operation,
• wherein the computer readable program code configured to determine the plurality of costs of operation of the hard disk for the plurality of possible placement configurations of the plurality of data clusters is further configured to:
• determine an objective function with a plurality of cost function values as variables;
• determine one or more constraints in terms of the variables;
• graph the constraints;
• and
• shade a feasibility region on the graph.