Self-contained placement of data objects in a data storage system
First Claim
1. A method implemented for execution on one more processors for optimal data storage, the method comprising:
- storing data objects that are most closely related in a storage container by modeling a constraint satisfaction problem for placement of said data objects in one or more storage containers,wherein the constraint satisfaction problem defines a self-contained placement degree for placement of the data objects, and accounts for penalties for placing multiple copies of the same data object in the one or more storage containers,wherein a weight is assigned to a path connecting two data objects u and v, and a self-contained placement degree M is defined for the data objects u and v where there is a storage container that includes u, v and M−
1 data objects w1, w2, . . . wM-1 that create a directed path from u to v,wherein output of a solver that solves the constraint satisfaction problem provides information about the data objects that are to be placed in the one or more storage containers,wherein the constraint satisfaction problem is defined as follows;
Given;
a set of data objects with known sizes that have weight-directed links or weight-directed paths;
number of storage containers K and the size of each container (SCi);
maximum number of required copies uL for each object u;
desired self-containment placement degree M;
Find a placement V1, . . . , VK of V for K storage containers, where;
sum of the object sizes in each container i doesn'"'"'t exceed the container size SCi;
number of copies for each object u doesn'"'"'t exceed uL;
such that placement weight is optimized to the highest weight of a self-contained placement degree M.
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Accused Products
Abstract
Systems and methods for optimal data storage are provided. The method comprises storing data objects that are most closely related in a storage container by modeling a constraint satisfaction problem for placement of said data objects in one or more storage containers, wherein a weight is assigned to an edge connecting two data objects based on an association defining relationships between the two data objects connected by said edge, taking into account certain penalties for placing multiple copies of the same object in the one or more storage containers, and wherein a storage container comprises a logical or physical storage area as a unit of storage.
19 Citations
21 Claims
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1. A method implemented for execution on one more processors for optimal data storage, the method comprising:
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storing data objects that are most closely related in a storage container by modeling a constraint satisfaction problem for placement of said data objects in one or more storage containers, wherein the constraint satisfaction problem defines a self-contained placement degree for placement of the data objects, and accounts for penalties for placing multiple copies of the same data object in the one or more storage containers, wherein a weight is assigned to a path connecting two data objects u and v, and a self-contained placement degree M is defined for the data objects u and v where there is a storage container that includes u, v and M−
1 data objects w1, w2, . . . wM-1 that create a directed path from u to v,wherein output of a solver that solves the constraint satisfaction problem provides information about the data objects that are to be placed in the one or more storage containers, wherein the constraint satisfaction problem is defined as follows; Given; a set of data objects with known sizes that have weight-directed links or weight-directed paths; number of storage containers K and the size of each container (SCi); maximum number of required copies uL for each object u; desired self-containment placement degree M; Find a placement V1, . . . , VK of V for K storage containers, where; sum of the object sizes in each container i doesn'"'"'t exceed the container size SCi; number of copies for each object u doesn'"'"'t exceed uL; such that placement weight is optimized to the highest weight of a self-contained placement degree M. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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9. The method of claim 8, where in a general degree M scenario, path between objects “
- u” and
“
v”
of length “
j”
in container “
i”
may be defined as the variable;
- u” and
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10. The method of claim 9, wherein the weight of the path of length “
- j”
from “
u”
to “
v”
in storage container “
i”
may be defined as coefficient;wj(uvi)=a function of the weights on the edges in the path plus a factor that decreases the weight as the path gets longer.
- j”
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11. The method of claim 10 wherein the objective function is defined as:
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12. The method of claim 11 wherein the constraints in the degree M scenario include:
one directed path between nodes u and v is selected, so there is no attempt to add profit by adding already used directed paths instead of unused directed paths, the used directed paths are unique
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13. The method of claim 12 wherein the constraints in the degree M scenario further include:
if an edge is selected in storage container i, then so are the nodes connected to that edge (incident vertices)
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14. The method of claim 13 wherein the constraints in the degree M scenario further include:
if a path of length 2 is selected in storage container i, then so are all the nodes in this path
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15. A computing system for optimal data storage, the system comprising:
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one or more processors; a logic unit for storing data objects that are most closely related in a storage container by modeling a constraint satisfaction problem for placement of said data objects in one or more storage containers, wherein the constraint satisfaction problem defines a self-contained placement degree for placement of the data objects, and accounts for penalties for placing multiple copies of the same data object in the one or more storage containers, wherein a weight is assigned to a path connecting two data objects u and v, and a self-contained placement degree M is defined for the objects u and v where there is a storage container that includes u, v and M−
1 objects w1, w2, . . . , wM-1 that create a directed path from u to v,wherein output of a solver that solves the constraint satisfaction problem provides information about the data objects that are to be placed in the one or more storage containers, wherein the constraint satisfaction problem is defined as follows; Given; a set of data objects with known sizes that have weight-directed links or weight-directed paths; number of storage containers K and the size of each container (SCi); maximum number of required copies uf for each object u; desired self-containment placement degree M; Find a placement V1, . . . , VK of V for K storage containers, where; sum of the object sizes in each container i doesn'"'"'t exceed the container size SCi; number of copies for each object u doesn'"'"'t exceed uL; such that placement weight is optimized to the highest weight of a self-contained placement degree M. - View Dependent Claims (16, 17)
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18. A computer program product comprising a non-transient computer readable storage medium having a computer readable program, wherein the computer readable program when executed on a computer causes the computer to:
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store data objects that are most closely related in a storage container by modeling a constraint satisfaction problem for placement of said data objects in one or more storage containers, wherein the constraint satisfaction problem defines a self-contained placement degree for placement of the data objects, and accounts for penalties for placing multiple copies of the same data object in the one or more storage containers, wherein a weight is assigned to a path connecting two data objects u and v, and a self-contained placement degree M is defined for the objects u and v where there is a storage container that includes u, v and M−
1 objects w1, w2, . . . , wM-1 that create a directed path from u to v,wherein output of a solver that solves the constraint satisfaction problem provides information about the data objects that are to be placed in the one or more storage containers, wherein the constraint satisfaction problem is defined as follows; Given; a set of data objects with known sizes that have weight-directed links or weight-directed paths; number of storage containers K and the size of each container (SCi); maximum number of required copies uL for each object u; desired self-containment placement degree M; Find a placement V1, . . . , VK of V for K storage containers, where; sum of the object sizes in each container i doesn'"'"'t exceed the container size SCi; number of copies for each object u doesn'"'"'t exceed uL; such that placement weight is optimized to the highest weight of a self-contained placement degree M. - View Dependent Claims (19, 20, 21)
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Specification