# Patent

## Title
Tenant placement in multitenant cloud databases with data sharing

## Abstract

A method for tenant placement in a multi-tenant system is shown that includes creating a weighted graph of tenants and sharing relationships between tenants, where a weight for each tenant and each sharing relationship represents an associated degree of resource consumption; and adding one or more tenants to a database using a processor based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to the added tenants are within the database capacity. If a tenant cannot be added to the database without exceeding the database capacity, a new database is created and the one or more tenants are added to the new database, subject to a new database capacity. The adding and creating steps are repeated until all tenants have been added to a database.

## Background
RELATED APPLICATION INFORMATION
This application claims priority to provisional application Ser. No. 61/563,154 filed on Nov. 23, 2011, incorporated herein by reference.
The present application is related to U.S. application Ser. No. 13/684,405, entitled “Tenant Placement in Multitenant Cloud Databases with One-to-Many Data Sharing,” filed Nov. 23, 2012 and incorporated herein by reference.


BACKGROUND
1. Technical Field
The present invention relates to multitenant databases and, more particularly to tenant placement within multitenant databases when the tenants share data.
2. Description of the Related Art
In a cloud environment, to achieve economies of scale and elasticity, multiple tenants may be placed in the same computing node/machine. Given a set of tenants, such that each tenant consumes a certain amount of resource, the cloud service provider would like to use as few machines as possible to accommodate these tenants. When different tenants have no interactions, this can be considered as the Bin Packing problem.
However, as the cloud provides opportunities for the tenants to share data, it is likely the case that tenants would have interactions with one another. If one tenant shares its data with another tenant, it may be beneficial to place these two tenants on the same machine as, at the very least, it can save the cost of transferring the data between different machines. While the bin packing problem for multitenant databases without data sharing is relatively easy to solve, introducing data sharing causes the problem to become very difficult, not only to solve outright but even to approximate.
SUMMARY
A method for tenant placement in a multi-tenant system is shown that includes creating a weighted graph of tenants and sharing relationships between tenants, where a weight for each tenant and each sharing relationship represents an associated degree of resource consumption; and adding one or more tenants to a database using a processor based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to said added tenants are within the database capacity. If a tenant cannot be added to the database without exceeding the database capacity, a new database is created and the one or more tenants are added to the new database, subject to a new database capacity. The adding and creating steps are repeated until all tenants have been added to a database.
A method for tenant placement in a multi-tenant system is shown that includes creating a weighted graph of tenants and sharing relationships between tenants, where a weight for each tenant and each sharing relationship represents an associated degree of resource consumption; and adding one or more tenants to a database using a processor based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to said added tenants are within the database capacity, the weight of a sharing relationship between two tenants is zero when tenants are on the same database, and the weight of a sharing relationship is non-zero when the two tenants are on different databases, wherein each tenant is assigned to one and only one database. If a tenant cannot be added to the database without exceeding the database capacity, a new database is created and the one or more tenants are added to the new database, subject to a new database capacity. The steps of adding and creating are repeated until all tenants have been added to a database.
A method for generating a data fetching plan in a multi-tenant system includes placing tenants in consecutively allocated databases using a processor according to a weighted graph of tenants and sharing relationships between tenants, where at least one sharing relationship includes multiple accessing tenants accessing a given set of data from a provider tenant. For each sharing relationship, if a current database has one or more accessing tenants and does not have the provider tenant, the method includes fetching data from the latest-allocated database prior to the current database that has accessing tenants, if such a database exists; if a current database has the provider tenant, providing data to the earliest-allocated database after the current database that has accessing tenants if such a database exists. Said fetching and providing steps are repeated for each allocated database.
A method for generating a data fetching plan in a multi-tenant system is shown that includes placing tenants in consecutively allocated databases using a processor according to a weighted graph of tenants and sharing relationships between tenants, wherein the weighted graph of tenants and sharing relationships includes a weight for each tenant and for each sharing relationship that represents an associated degree of resource consumption and wherein at least one sharing relationship includes multiple accessing tenants accessing a given set of data from a provider tenant, where said placing includes adding one or more tenants to a database based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to said added tenants are within the database capacity; and allocating tenants to a new database when a previous database lacks available resources for new tenants. For each such sharing relationship: if a current database has one or more accessing tenants and does not have the provider tenant, the method includes fetching data from the latest-allocated database prior to the current database that has accessing tenants, if such a database exists; if a current database has the provider tenant, the method includes providing data to the earliest-allocated database after the current database that has accessing tenants if such a database exists. Said fetching and providing steps are repeated for each allocated database.
These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.



BRIEF DESCRIPTION OF DRAWINGS
The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein:
FIG. 1 is a diagram of a multi-tenant cloud database system according to the present principles.
FIG. 2 is a block/flow diagram of tenant placement in a multi-tenant database system according to the present principles.
FIG. 3 is a block/flow diagram of online tenant placement in a multi-tenant database system according to the present principles.
FIG. 4 is a block/flow diagram of tenant placement in a multi-tenant database system with one-to-many sharing according to the present principles.



DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
The present principles formalize the problem of tenant placement with data sharing as a variation of Bin Packing named Bin Packing with Sharing (BPS). Unlike Bin Packing and some of its other variations, which are fairly easy to approximate, BPS is one of the most difficult problems to approximate. The problem of determining the feasibility of a BPS instance is strongly NP-complete. As a result, the present principles restrict the focus to those BPS instances where there is always a trivial feasible solution. Since it is not possible to approximate BPS unless P=NP, an alternative performance criterion for BPS is considered named Tightly Consolidated Packing (TCP). The present principles provide a method that satisfies TCP. Furthermore, the present principles may achieve TCP for online BPS as well.
Referring now to FIG. 1, an exemplary multitenant database system 100 is shown. The system 100 includes a set of multitenant databases 102 connected to one another through a cloud network 101. Each multitenant database 102 may include one or more tenants 104. The tenants 104 may operate separately or may share data.
A major expense of the multitenant database service providers is to purchase or rent a system 100 from an Infrastructure-as-a-Service (IaaS) provider. Hence, it is desirable to minimize the number of machines 102 used to host those tenants 104. As computing resources, such as CPU, memory and I/O, are generally considered to be additive (i.e., the total consumption of a set of tenants equals the sum of their individual consumption without the other tenants), this could be considered as the multi-capacity Bin Packing problem where each item represents a tenant 104 and a bin represents a machine 102.
Multitenant cloud databases 102 not only provide the opportunity for tenants 104 to share the infrastructure resources, databases or tables, but also enable tenants 104 to share data with one another. For cloud service provider 100, this means more utilization of the cloud resources and hence more revenue; for the data owner, data sharing means turning its data into profits; for the consumer, it means purchasing data for a reasonable price, which can then be used in its own applications to achieve various purposes and enhance user experiences. For example, if a hotel booking application gets the data of users' travel schedules from other applications such as an airline booking application or a calendar application, then it can automatically recommend the most relevant hotels to the user. In this way, different applications can work seamlessly to deliver an excellent user experience.
If data is shared among different tenants 104, then the Bin Packing problem becomes much harder. If tenant t1 shares data with t2, then it is beneficial to place t1 and t2 on the same machine, so that they can access the same copy of the shared data. On the other hand, if t1 and t2 are on different machines, it incurs a cost of propagating the shared data from t1 to t2, which costs additional resources on both machines 102, as it involves additional procedures such as checking the staleness of the data, determining when to propagate the update of the data to the destination machine 102, updating the data on the destination machine 102, updating the indexes, recovering from network failures, etc.
In the embodiments described herein, it is assumed that each machine 102 has a fixed capacity and that each tenant 104 consumes a certain amount of resources to query its own data. Furthermore, it is assumed that, if t1 and t2 share data, there will be no additional cost; otherwise it accessing another tenant's data will cost a certain amount of resources. These assumptions are set forth for the purpose of illustration and ease of description—they should not be considered to be limiting.
Unlike the classic Bin Packing problem, which can be readily approximated, BPS is very difficult to approximate. Due to its inapproximability, the present principles provide a non-trivial local optimum criterion: in the solution of BPS, the present principles do not consolidate all of the tenants 104 on any subset of machines 102 into a single machine 102. Although this property is trivial to achieve in classic Bin Packing, that is not the case for BPS. The present principles provide a polynomial time method for achieving that local optimum property.
Tenants 104 may come and go within the cloud system 100, where a tenant 104 may launch of suspend a sharing relationship with another tenant 104 at any time. After a change has occurred to a tenant 104 or a sharing relationship between tenants 104, it is still desirable to maintain the local optimum property of tenant placement. However, the present principles avoid two things: computing the tenant placement from scratch and moving too many tenants 104 from one machine 102 to another machine 102. Recomputing placement from scratch may be very expensive, as there can be hundreds or thousands of tenants 104 in a single cloud system 100. Similarly, moving too many tenants 104 between machines is costly, as it creates a throughput bottleneck.
The input of the BPS problem is a directed graph G (V, A). Each vertex u∈V corresponds to a tenant 102 and has a non-negative weight wV(u). Each arc e=(u, v)∈A has two non-negative weights, associated with u and v respectively, denoted as wA(e, u) and wA(e, v). Each bin corresponds to a database 102 and has a capacity of B. Placing vertex u in a bin b consumes wV(u)+Σe∈A′, wA(e, u) of the capacity of bin b, where A′ is the set of arcs (u, v) and (v, u), where v is not placed in b. The goal is to place all vertices into the smallest number of bins. For conciseness, for an arc e=(u, v), let x=wA(e, u) and y=wA(e, v), the weights of e is denoted as (x, y).
Intuitively, wV(u) corresponds to the resources consumed individually by a tenant 104, u, when it is placed in a database 102, bin b. (x, y) corresponds to additional resources consumed to enable and maintain the data sharing related operations described above. For each vertex v adjacent to u (i.e., where u and v have a sharing relationship), if v is placed in the same bin as u, there is no additional cost. Otherwise, the corresponding arc weight wA(e, u) will be consumed out of b's capacity.
In practice, placing u and v in the same bin may incur a small additional cost, rather than no additional cost. However, since this cost (say (cu, cv)) is small compared to the cost of placing u and v in separate bins (i.e., (wA(e, u), wA(e, v)), one can remove this cost by considering it as part of u and v's vertex weights. In other words, one can add cu and cv to wV (u) and wV(v), and subtract them from wA(e, u) and wA(e, v), respectively.
The BPS problem is highly inapproximable. To address this problem, the present principles provide a performance criterion that provides locally optimal solutions. That criterion is described herein as Tightly Consolidated Packing (TCP), restricting the focus to those BPS instances where it is always possible to place each vertex in a distinct bin, a condition which is generally the case in real-world applications.
The TCP criterion is defined as solutions to a BPS instance, if and only if one cannot combine vertices in any set of multiple bins in the solution into a single bin. While the criterion seems straightforward, in the BPS problem, the interaction among vertices makes finding a solution significantly more difficult difficult.
Referring now to FIG. 2, a method for achieving TCP in polynomial time is shown. Block 202 creates a graph of the tenants 104, including a list of vertices V that represent the tenants 104 and arcs A that represent sharing connections between the tenants. Block 204 creates a dummy bin bd to hold all of the vertices at block 206.
Block 208 creates a new, empty bin, representing a database 102 having a fixed capacity. Block 210 moves nodes from the dummy bin into the new bin. A move function emoployed by block 210 moves vertices while ensuring that the capacity of the new bin is not exceeded. If vertices were successfully moved, block 212 returns processing to block 210 to determine whether additional vertices may be moved into the new bin. If not, block 214 determines whether any unplaced vertices remain in the dummy bin. If not, processing returns to block 208 and a new bin is created. If so, processing ends with all tenants 104 having been assigned to databases 102.
Embodiments described herein may be entirely hardware, entirely software or including both hardware and software elements. In a preferred embodiment, the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.
Embodiments may include a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer readable medium may include any apparatus that stores, communicates, propagates, or transports the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. The medium may include a computer-readable storage medium such as a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk, etc.
A data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code to reduce the number of times code is retrieved from bulk storage during execution. Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
Referring now to FIG. 3, online placement of a new tenant 104 is shown. After initial TCP placement, online placement can be implemented in such a way as to preserve TCP. If a new tenant/vertex 104 is added to cloud 100, it is first determined at block 302 whether any existing database/bin 102 can accommodate it. If so, the vertex is placed into an existing bin at block 304. If not, block 306 creates a new bin and block 308 places the tenant into the new bin. If the original bin packing plan is TCP, then this bin packing plan is also TCP, since adding a new vertex always reduces the remaining capacity of a bin.
If two vertices in different bins start a new sharing, then one or both of the bis may exceed their capacity. If a bin exceeds capacity, it may move some vertices to another bin or move vertices from another bin to itself. Because sharing between bins incurs an overhead, bringing additional vertices into the bin may actually reduce the overall capacity consumed.
The above method for placement can be adjusted to accommodate the removal of vertices and arcs. Due to the high overhead of moving tenants, the present principles determine whether some bins in the plan can be consolidated. Bins are either left unchanged or are emptied entirely, such that all vertices in each bin may be considered as a single logical vertex for the purpose of consolidation.
The above assumes that a sharing is a binary relationship: one tenant 104 shares some data to another tenant 104 in the cloud 100. If there are multiple tenants 104 that share data from a data owner, then they are modeled as multiple binary relationships. However, if multiple tenants 104 share the same data from a data owner, then there one can do more to optimize the tenant placement.
First, suppose a tenant t1 shares the same data to two tenants t2 and t3. Then, if t2 and t3 cannot be placed on the same machine 102 as t1, it is beneficial to put t2 and t3 on the same machine 102, rather than separate t2 and t3. In this way, one only transfers one copy of the data from t1's machine to t2 and t3's machine, and t2, t3 can share this data on their machine. Second, if t1 shares the same data to t2 and t3 and they are all placed on different machines, then t2 and t3 does not necessarily have to both fetch data from t1's machine. t2 can fetch data from t1's machine, and t3 can fetch data from t2's machine.
Based on these observations, the BPS problem may be extended to Bin Packing with one-to-Many Sharings (BPMS). The input of the BPMS problem is a set of vertices V, a set of sharings S among the vertices, and a bin capacity B. Each vertex u∈V is associated with a non-negative weight wV(u). Each sharing s∈S is defined as s=(u, U) where u∈V, U⊂V and u∉U. Each sharing s is associated with two non-negative weights: win(s) and wout(s)
A sharing plan has two parts: a placement plan which decides which vertex to place into which bin, and a data fetching plan, which decides which bin provides data to which bin(s) for each sharing. For each sharing s=(u, U), if a subset U′⊂U of vertices are placed in the same bin b, and u∉b, then the data fetching plan specifies another bin from where b fetches the data of s. The data originates from u.
Placing a vertex u in bin b consumes wV(u) capacity of b. Furthermore, if bin b fetches the data of sharing s from another bin b′, then it consumes win(s) capacity in b and consumes wout(s) capacity in b′. The goal is to find a sharing plan that uses the smallest number of bins.
One cannot use the same TCP definition from the BPS problem is used for BPMS, because that TCP formulation does not say anything about the fetching plan. It is NP-hard to determine whether there is a fetching plan, which, together with the given placement plan, forms a valid sharing plan. Therefore, allowing arbitrary changes on the fetching plan makes it difficult to achieve TCP. The BPMS definition of TCP is therefore as follows. A solution to a BPMS instance is a TCP if and only if one cannot combine the vertices in any set of multiple bins B in the solution into a single bin b without changing the data fetching plan, except for the following cases: (1) if b1∈B provides data to b2∈B, this is no longer needed since both b1 and b2 are consolidated into b; (2) if b1∈B provides/fetches data to/from b2∉B, then let b, instead of b1, provides/fetches this data to/from b2.
To generate a TCP sharing plan for BPMS, the data fetching plan is designed in such a way that, when the current bin is being filled, without knowledge about the bins to the right of the current bin, one can still decide whether the current bin needs to fetch data from another bin, or provide data to other bins, for each sharing s.
To do so, the following data fetching plan is used. Given a placement plan that consists of m bins b1, . . . , bm, let Vi denote the set of vertices placed in bi. For each sharing s=(u, U):
If u∈Vi, and U∩Vi≠U, then bi provides the data of s to either one bin or two bins. Let bj be the rightmost bin to the left of bi which contains vertices in U, and let bk be the leftmost bin to the right of bi which contains vertices in U. Then bi provides the data to bj and bk, if exist (note that at least one of them exists).
If u∉Vi and U∩Vi≠Ø, then bi fetches the data of s from another bin bj. Let bk be the bin that contains u. If k>i, bj is the leftmost bin to the right of bi such that either j=k, or U∩Vj≠Ø. Otherwise, bj is the rightmost bin to the left of bi such that either j=k, or U∩Vj≠Ø.
If u∉Vi and U∩Vi≠Ø, let bk be the bin that contains u·bi provides the data of s to exactly one other bin bj if and only if one of the following two cases happens: (1) k>i, and there exist bins to the left of bi that contain vertices in U. In this case, bj is the rightmost bin to the left of bi that contains vertices in U. (2) k<i, and there exists bins to the right of bi that contain vertices in U. In this case, bj is the leftmost bin to the right of bi that contains vertices in U.
Using this data fetching plan, a bin bi is being filled, it is still possible to know whether bi receives or provides the data of a sharing from/to other bins. For example, for a sharing s=(u, U), if u is placed in a bin before bi, then bi needs to provide the data to another bin if and only if not all remaining vertices in U are placed in bi.
Referring now to FIG. 4, a method for generating a data fetching plan is shown. Block 402 places vertices in bins using, e.g., the bin placement method described above in FIG. 2. Block 404 determines whether all of the sharings between vertices have been visited. If there are no more unvisted sharings, processing ends. If there are, block 406 determines whether all bins have been checked. If not, processing returns to block 404 for the next sharing. If so, block 408 collects the vertices in a bin bi into set Vi. Block 410 determines whether the intersection of U and Vi is an empty set and whether u is not in Vi. If both conditions are true, block 412 finds the largest j, such that j<i and the intersection of U and Vi is an empty set. Block 414 determines whether such a j exists. If so, block 416 fetches the sharing data s from bin bj for bin bi. Processing then proceeds to block 426 and a new bin is selected.
If the condition in block 410 was false, block 418 determines whether u is in Vi. If so, block 420 finds the smallest j, such that j is different from i and the intersection of U and Vi is an empty set. If such a j exists at block 422, then block 424 fetches the sharing data s from bin bi for bin bj. Processing then proceeds to block 426 and a next bin is selected.
Processing continues in this manner until all bins have been checked for all sharings. This produces a locally optimum placement for any BPMS instance if the instance occupies a valid sharing plan. Even using a similar assumption to the above, where there is a valid sharing plan such that each vertex is placed in a distinct bin, finding a sharing plan is computationally expensive.
Creating the sharing plan may be understood as spreading sharing relationships out along a line of databases. The databases may be ordered in any way, though it is specifically contemplated that they will be ordered in terms of their time of creation. Rather than having each tenant access shared data from a single provider tenant u, the burden of sharing data is spread out across the databases. The method of FIG. 4 establishes data fetching between nearby databases that have access to the data in question.
Having described preferred embodiments of a system and method for tenant placement in multitenant cloud databases with data sharing (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.

## Claims

1. A method for tenant placement in a multi-tenant system, comprising:
creating a weighted graph of tenants and sharing relationships between tenants, where a weight for each tenant and each sharing relationship represents an associated degree of resource consumption;
assigning each graph vertex u∈V to a tenant with a non-negative weight wv(u), each arc e=(u, v)∈A has two non-negative weights, associated with u and v respectively, denoted as wA(e,u) and wA(e,v);
creating bins, each bin b corresponds to a database with a capacity of B.
placing vertex u in a bin b consumes wv(u)+Σe∈A′wA (e, u) of the capacity of bin b, where A′ is a set of arcs (u, v) and (v, u), where v is not placed in b;
placing all vertices into the smallest number of bins where an arc e=(u, v), x=wA(e,u) and y=wA(e, v), the weights of e is denoted as (x, y);
determining a cost of placing u and v in the same bin and the cost of placing u and v in separate bins (wA(e,u), wA(e,v)) by considering the cost as part of u and v's vertex weights by adding cu and cv to wv(u) and wv(v), and subtracting from wA (e, u) and wA (e, v), respectively;
adding one or more tenants to a database using a processor based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to said added tenants are within the database capacity;
if a tenant cannot be added to the database without exceeding the database capacity, creating a new database and adding the one or more tenants to the new database, subject to a new database capacity; and
repeating said adding and creating until all tenants have been added to a database.

2. The method of claim 1, wherein the weight of a sharing relationship between two tenants is zero when tenants are on the same database and non-zero when the two tenants are on different databases.

3. The method of claim 1, wherein each sharing relationship is binary.

4. The method of claim 1, wherein at least one sharing relationship exists between one provider tenant and a plurality of accessing tenants.

5. The method of claim 1, wherein each tenant is assigned to one and only one database.

6. The method of claim 1, further comprising adding one or more new tenants during runtime to an existing database if the existing database has sufficient free resources to accommodate the new tenant.

7. The method of claim 6, further comprising creating a new database during runtime and adding the one or more tenants to the new database if no existing database has sufficient free resources.

8. The method of claim 1, wherein database capacity is represented as a single value and wherein the weight of the tenants and the sharing relationships are directly added and compared to said single value.

9. A method for tenant placement in a multi-tenant system, comprising:
creating a weighted graph of tenants and sharing relationships between tenants, where a weight for each tenant and each sharing relationship represents an associated degree of resource consumption;
assigning each graph vertex u∈V to a tenant with a non-negative weight wv(u), each arc e=(u, v)∈A has two non-negative weights, associated with u and v respectively, denoted as wA (e, u) and wA (e, v);
creating bins, each bin b corresponds to a database with a capacity of B.
placing vertex u in a bin b consumes wv(u)+Σe∈A′wA (e, u) of the capacity of bin b, where A′ is a set of arcs (u, v) and (v, u), where v is not placed in b;
placing all vertices into the smallest number of bins where an arc e=(u, v), x=wA (e, u) and y=wA (e, v), the weights of e is denoted as (x, y);
determining a cost of placing u and v in the same bin and the cost of placing u and v in separate bins (wA (e, u), wA (e, v)) by considering the cost as part of u and v's vertex weights by adding cu and cv to wv(u) and wv(v), and subtracting from wA (e, u) and wA (e, v), respectively;
adding one or more tenants to a database using a processor based on said weighted graph and a database capacity, such that the combined weight of the added tenants and the sharing relationships belonging to said added tenants are within the database capacity, the weight of a sharing relationship between two tenants is zero when tenants are on the same database, and the weight of a sharing relationship is non-zero when the two tenants are on different databases, wherein each tenant is assigned to one and only one database;
if a tenant cannot be added to the database without exceeding the database capacity, creating a new database and adding the one or more tenants to the new database, subject to a new database capacity; and
repeating said adding and creating until all tenants have been added to a database.

10. The method of claim 9, wherein database capacity is represented as a single value and wherein the weight of the tenants and the sharing relationships are directly added and compared to said single value.