Method of instantiating data placement heuristic

A method of instantiating a data placement heuristic for a distributed storage system comprising the steps of:

• receiving heuristic parameters which comprise a cost function, a placement constraint, a metric scope, an approximation technique, and an evaluation interval;
• and
• for each evaluation interval within an execution of the data placement heuristic, performing the steps of:
• for data objects and nodes within the metric scope, determining a cost array which comprises cost elements determined from the cost function;
• if the approximation technique comprises a ranking technique, placing the data objects according to the placement constraint in order of rank within a sorted cost array;
• if the approximation technique comprises a greedy ranking technique, placing the data objects according to the placement constraint in order of the rank within the sorted cost array, and recomputing and resorting the cost array for remaining cost elements after each placement decision;
• and
• if the approximation technique comprises a threshold technique, eliminating the cost elements of the cost array which violate a threshold, thereby forming a threshold limited cost array, and placing the data objects according to the placement constraint in order of rank within a sorted version of the threshold limited cost array.

A computer readable memory comprising computer code for implementing a method of instantiating a data placement heuristic for a distributed storage system, the method of instantiating the data placement heuristic comprising the steps of:

• receiving heuristic parameters which comprise a cost function, a placement constraint, a metric scope, an approximation technique, and an evaluation interval;
• and
• for each evaluation interval within an execution of the data placement heuristic, performing the steps of:
• for data objects and nodes within the metric scope, determining a cost array which comprises cost elements determined from the cost function;
• if the approximation technique comprises a ranking technique, placing the data objects according to the placement constraint in order of rank within a sorted cost array;
• if the approximation technique comprises a greedy ranking technique, placing the data objects according to the placement constraint in order of the rank within the sorted cost array, and recomputing and resorting the cost array for remaining cost elements after each placement decision;
• and
• if the approximation technique comprises a threshold technique, eliminating the cost elements of the cost array which violate a threshold, thereby forming a threshold limited cost array, and placing the data objects according to the placement constraint in order of rank within a sorted version of the threshold limited cost array.