Determining placement of distributed application onto distributed resource infrastructure

A method of determining a placement of services of a distributed application onto nodes of a distributed resource infrastructure comprises first, second, and third steps. The first step forms communication constraints between node pairs. The communication constraints ensure that a sum of transport demands between a particular node pair does not exceed a transport capacity between the particular node pair. Each term of the sum comprises a product of a first placement variable, a second placement variable, and the transport demand between the services associated with the first and second placement variables. The second step forms an objective. The communication constraints and the objective comprise an integer program. The third step employs a local search solution to solve the integer program, which determines the placement of the services onto the nodes.

RELATED APPLICATIONS

The following applications disclose related subject matter: U.S. application Ser. No. 10/627,274, filed Jul. 25, 2003 and entitled, “Determination of One or More Variables to Receive Value Changes in Local Search Solution of Integer Programming Problem”; and U.S. application Ser. No. 10/627,883, filed Jul. 25, 2003 and entitled, “Incorporating Constraints and Preferences for Determining Placement of Distributed Application onto Distributed Resource Infrastructure”; the contents of all of which are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of placing a distributed application onto a distributed resource infrastructure. More particularly, the present invention relates to the field of placing a distributed application onto a distributed resource infrastructure where the distributed application and the distributed resource infrastructure have arbitrary communication topologies.

BACKGROUND OF THE INVENTION

A distributed application includes a plurality of services. Each of the services performs a task or tasks as part of the distributed application. Often the distributed application is placed on a network of computers. The network of computers forms a distributed resource infrastructure where each of the computers forms a node. Performance of the distributed application depends on optimizing a placement of the services onto the nodes.

A first method of the prior art uses parameters for individual nodes to determine a placement of the services onto the nodes. Such parameters include processing and storage capabilities of the nodes.

The first method does not consider relationships among the nodes or among the services in the determination of the placement of the services onto the nodes. A second method of the prior art considers topologies between the services and between the nodes but requires that the topologies be fixed in certain configurations. The second method does not determine a placement of the services onto the nodes where an arbitrary topology exists between the nodes or between the services.

What is needed is a method of determining a placement of services of a distributed application onto nodes of a distributed resource infrastructure taking into account arbitrary topologies between the nodes and between the services.

SUMMARY OF THE INVENTION

The present invention is a method of determining a placement of services of a distributed application onto nodes of a distributed resource infrastructure. In an embodiment of the present invention, the method comprises first, second, and third steps. The first step forms communication constraints between node pairs. The communication constraints ensure that a sum of transport demands between a particular node pair does not exceed a transport capacity between the particular node pair. Each term of the sum comprises a product of a first placement variable, a second placement variable, and the transport demand between the services associated with the first and second placement variables. The second step forms an objective. The communication constraints and the objective comprise an integer program. The third step employs a local search solution to solve the integer program, which determines the placement of the services onto the nodes.

These and other aspects of the present invention are described in more detail herein.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

The present invention determines a placement of a distributed application onto a distributed resource infrastructure. The distributed application comprises a plurality of services. The distributed resource infrastructure comprises a plurality of nodes.

A distributed application embodiment is illustrated schematically inFIG. 1. The distributed application embodiment100comprises a firewall102, a local buffer104, first, second, and third web servers,106,108, and110, an application server112, and a database114, each of which forms a service. The firewall102is coupled to the local buffer104. Each of the first, second, and third web servers,106,108, and110, couples the local buffer104to the application server112. The application server112is coupled to the database114. In operation, the distributed application embodiment100provides web access to users who provide and obtain information from the database114.

A distributed resource infrastructure embodiment is illustrated schematically inFIG. 2. The distributed resource infrastructure embodiment200comprises first through eighth nodes,202. . .216, and a switch218. The switch218couples each of the first through eighth nodes,202. . .216, to remaining nodes of the first through eighth nodes,202. . .216. The determination of the placement of the distributed application embodiment100onto the distributed resource infrastructure embodiment200is accomplished according to an objective such as minimizing network traffic, minimizing latency in order to reduce response time, or balancing a load on the nodes.

A preferred method of the present invention is illustrated as a block diagram inFIG. 3. The preferred method300comprises first through third steps,302. . .306. The first step302forms communication constraints, which ensure that transport demands between node pairs do not exceed transport capacities between the node pairs. Each of the communication constraints is made up of a sum of terms. Each of the terms comprises a product of a first placement variable, a second placement variable, and the transport demand between the services associated with the first and second placement variables. The second step304forms the objective. In the third step306, a local search solution is employed to solve an integer program comprising the communication constraints and the objective, which provides the placement of the service onto the nodes.

A first alternative method of the present invention is illustrated as a block diagram inFIG. 4. The first alternative method400adds fourth and fifth steps to the preferred method300. The fourth step402establishes an application model, which comprise the transport demands between the services. The fifth step404establishes an infrastructure model, which comprises the transport capacities between the nodes. The application model and the infrastructure model provide the transport demands and capacities to the preferred method300.

An alternative distributed application embodiment is illustrated schematically inFIG. 5. The alternative distributed application embodiment500comprises first through fourth services,501. . .504, and fifth through Sth services,505. Mathematically, the first through Sth services,501. . .505, are expressed as sε{1, 2, 3, . . . , S}. Each pair of the services has an associated transport demand. For example, a first transport demand dt12represents communication traffic between the first and second services,501and502. A transport demand matrix Dt lists the transport demands between the first through Sth services,501. . .505, as follows.

Since services do not communicate with themselves over a network, the transport demands along a matrix diagonal have no values. Further, depending upon a particular implementation it may be sufficient to characterize the transport demands without reference to direction in which case the transport demands below the matrix diagonal would also have no values.

Each of the first through Sth services,501. . .505, of the alternative distributed application500is also characterized with a processing demand and a storage demand. For example, the first service501has a first processing demand dp1and a first storage demand ds1. A processing demand vector Dp and a storage demand vector Dslist the processing demands and the storage demands of the first through Sth servers,501. . .505, as follows.

An alternative distributed resource infrastructure is illustrated schematically inFIG. 6. The alternative distributed resource infrastructure600comprises first through fourth nodes,601. . .604, and fifth through Nth nodes,605. Mathematically, the first through Nth nodes are expressed as nε{1, 2, 3, . . . , N}. Each pair of the nodes has an associated transport capacity. For example, a first transport capacity ct12represents communication bandwidth between the first and second nodes,601and602. A transport capacity matrix Ct lists the transport capacities between the first through Nth nodes,601. . .605, as follows:

Ct=(-ct12ct13…ct1⁢Nct21-ct23…ct2⁢Nct31ct32-…dt3⁢N………-…ctN⁢⁢1ctN⁢⁢3ctN⁢⁢3…-)
Since nodes do not communicate with themselves over a network, the transport capacities along a matrix diagonal have no values. Further, depending upon a particular implementation it may be sufficient to characterize the transport capacities without reference to direction in which case the transport capacities below the matrix diagonal would also have no values.

Each of the first through Nth nodes,601. . .605, of the alternative distributed resource infrastructure600is also characterized with a processing capacity and a storage capacity. For example, the first node601has a first processing capacity cp1and a first storage capacity cs1. A processing capacity vector Cp and a storage capacity vector Cs list the processing capacities and the storage capacities of the first through Nth nodes,601. . .605, as follows.

In some situations, the distributed application under consideration operates solely on the distributed resource infrastructure. In this situation, the transport, processing, and storage capacities represent absolute capacities for the nodes. In other situations, the distributed application is one of a plurality of distributed applications operating on the distributed resource infrastructure. In these other situations, the transport, processing, and storage capacities represent available capacities for the nodes.

In the alternative distributed application embodiment500and the alternative distributed resource infrastructure embodiment600, the transport and storage demands, Dt and Ds, as well as the transport and storage capacity, Ct and Cs, are normalized according to standard parameters of data per unit time and data, respectively. In an embodiment of the present invention, the processing demand Dp and the processing capacity Cp are normalized according to a processing criterion. Preferably, the processing criterion is a transaction speed especially when the distributed application forms a database application. Alternatively, the processing criterion is an algorithm speed. In another embodiment of the present invention, various processors are listed in a look-up table with associated processing capacities that have been normalized by the processing criterion. In this embodiment, a system implementing the present invention would go to the look-up table to find the processing capacity for a particular node when needed.

Applying the first alternative method400(FIG. 4) to the alternative distributed application model500and the alternative distributed resource infrastructure600begins with the fourth and fifth steps,402and404, in which the transport demand and capacity matrices, Dt and Ct, are established. The first step302then forms the communication constraints in first and second tasks. The first task forms placement variables, which according to an embodiment of the present invention are Boolean variables. In a matrix notation, the placement variables are given by a placement variable matrix X where rows represent the services and columns represent the nodes. The placement variable matrix X is as follows.

X=(x11x12…x1⁢Nx21x22…x2⁢N…………xS⁢⁢1xS⁢⁢2…xSN)
The second task forms the communication constraints according to a communication constraint equation, which is given as follows.

The second step304forms the objective, which according to an embodiment of the present invention minimizes communication traffic between the nodes and balances processing loads on the nodes. The latter is accomplished by minimizing the mathematical variance of the processing loads. The objective is given as follows.

Since the communication constraints account for a distributed application topology according to the transport demands and for a distributed resource infrastructure topology according to the transport capacities, the present invention allows arbitrary topologies for both the distributed application and the distributed resource infrastructure. This allows the present invention to be used for determining placement of applications onto infrastructures in wide variety of situations. Examples of such situations include placing an application onto nodes in a data center and placing a different application onto nodes in geographically distributed data centers.

A second alternative method of the present invention adds processing constraints to the integer program. The processing constraints ensure that a sum of the processing demands for a specific node does not exceed the processing capacity of the specific node. In an embodiment of the present invention, the processing constraints are formed according to a processing constraint equation, which is given as follows.

A third alternative method of the present invention adds storage constraints to the integer program. The storage constraints ensure that a sum of the storage demands for a specific node does not exceed the storage capacity of the specific node. In an embodiment of the present invention, the storage constraints are formed according to a storage constraint equation, which is given as follows.

A fourth alternative method of the present invention adds placement constraints to the integer program. The placement constraints ensure that each of the services is placed on one and only one of the nodes. In an embodiment of the present invention, the placement constraints are formed according to a placement constraint equation, which is given as follows.

A fifth alternative method of the present invention recognizes that, once the services have been placed onto the nodes, a rearrangement of the services onto the nodes comes at a cost. In the fifth alternative method, reassignment penalties are assessed when a service placement differs from an existing assignment of the service. According to an embodiment of the fifth alternative method, a second objective is added to the integer program. The second objective seeks to minimize the reassignment penalties.

In the present invention, the communication constraints include terms which comprise products of two placement variables. Depending on the embodiment of the present invention, the objective also includes products of two placement variables. Thus, the communication constraints and possibly the objective are quadratic equations, i.e., equations having polynomial terms of second order. Solving integer programs that include polynomial terms of second or higher order is particularly difficult. In an embodiment of the present invention, a local search solution is employed according to a local search solution method disclosed in U.S. patent application Ser. No. 10/627,274 filed on Jul. 25, 2003, which is incorporated by reference in its entirety.

An embodiment of the local search solution method is illustrated as a flow chart inFIG. 7. The local search solution method700comprises first through tenth solution steps,702. . .720, in which a gradient following approach iteratively improves an initial assignment of values to the variables until a near optimum solution is reached. Each iteration of the local search solution produces a new assignment of values for the variables. The new assignment of values differs from a previous assignment of values by one value for a particular variable.

The first solution step702defines a problem model as an overconstrained integer programming problem. In an embodiment of the present invention, the problem model comprises data, variables, and constraints. The data comprises the processing demands and capacities, the storage demands and capacities, and the transport demands and capacities. The variables comprise the placement variables, which are Boolean variables where a zero value indicates that a particular service is not located on a particular node and where a one value indicates that the particular service is located on the particular node. In the overconstrained integer programming problem, the constraints comprise hard constraints and at least one soft constraint. The hard constraints comprise the processing constraints, the storage constraints, the placement constraints, and the storage constraints. The soft constraint is the objective, which comprises minimizing the communication traffic between the nodes and balancing the processing loads on the nodes.

In the second solution step704, the placement variables are randomly initialized. The third solution step706selects an unsatisfied constraint. The fourth solution step708creates stores in memory for each of the placement variables in the unsatisfied constraint. The fifth solution step710parses the unsatisfied constraint by term. For each of the placement variables in the term, an associated store is updated with a change in the unsatisfied constraint due to flipping the value of the placement variable while holding other placement variables constant. In the sixth solution step712, the placement variable that is to have its value flipped is chosen according to an improvement criterion, such as the placement variable which most improves the unsatisfied constraint or the placement variable which most improves an overall solution while also improving the unsatisfied constraint.

In the seventh solution step714, assigned values are compared to optimality criteria to determine whether a solution has been found. The optimality criteria for the overconstrained integer programming problem are no violation of the hard constraints and a near optimum solution for the soft constraint. If the optimality criteria are not met, the local search solution method700continues in the eighth solution step716with a determination of whether an additional iteration is to be performed. If so, the local search solution method700returns to the third solution step706of selecting another unsatisfied constraint to determine another placement variable which is to have its value flipped. If not, a ninth solution step718determines whether to restart the local search solution method700by reinitializing the variables. If the optimality criteria are met in the seventh solution step714, a final value assignment for the placement variables is output as a result in the tenth solution step720. If the ninth step210determines to not restart the first alternative method200, a “no solution found” message is output in the tenth solution step720.

Preferably, the local search solution method is implemented using AMPL, a modeling language for optimization problems. Alternatively, the local search solution method700is implemented using an alternative modeling language.

An embodiment of a system for solving an integer program of the present invention is illustrated schematically inFIG. 8. The system800includes a display802, input/output devices804, a processing unit806, a storage device808, and a memory810. The processing unit806couples to the display802, the input/output devices804, the storage device808, and the memory810.

Employing the system800to solve the integer program according to the local search solution method700begins with the processing unit806reading the problem model into the memory810. The processing unit806then initializes the variables by randomly assigning values to the variables. Next, the processing unit806randomly selects the unsatisfied constraint. Following this, the processing unit806creates stores in the memory810for the allowable changes of the variables in the unsatisfied constraint. The processing unit806then parses the unsatisfied constraint by term updating individual stores associated with the term while maintaining other variables constant. Following this, the processing unit806chooses the placement variable to have its value flipped according to the improvement criterion. The processing unit806then determines whether the optimality condition has been met and, if not, determines whether to perform more iterations or whether the local search solution method700should be restarted.

In an embodiment of the present invention, computer code resides on a computer readable memory, which is read into the system800by one of the input/output devices804. Alternatively, the computer readable memory comprises the storage device808or the memory810. The computer code provides instructions for the processing unit806to form the problem model and to solve it according to an embodiment of the present invention. The computer readable memory is selected from a group including a disk, a tape, a memory chip, or other computer readable memory.

The foregoing detailed description of the present invention is provided for the purposes of illustration and is not intended to be exhaustive or to limit the invention to the embodiments disclosed. Accordingly, the scope of the present invention is defined by the appended claims.