Patent Grant
7809831
The present invention relates to a method for provisioning resources. More specifically, the present invention relates to a method, system and control program for provisioning resources in an IT environment.
In a present IT environment, in particular, in a place such as a data center in which resources are concentrated at one place, a mechanism (provisioning) for efficiently (in terms of processing time and resource utilization rate) allocating an excess resource (a server, a network apparatus, a storage, or the like) to each of services in response to load fluctuations of the services is required. In order to allocate this excess resource to the service, a hardware configuration, OS/application installation, and setting operations, which are suitable for the service, are required. However, setting processes (installing OS/application, switching VLAN (Virtual LAN), and the like) which occur in the process of the operation are relatively time-demanding work in general.
With this being the situation, as a method of carrying out prompt provisioning, a method (Patent Document 1) for uploading data by backing up provisioning data for a network apparatus has been already invented. Additionally, “clone installation” has been disclosed (for example, non-patent documents 1 and 2). In the “clone installation”, a disk image of a sever into which a predetermined OS or application is installed is stored in advance, and the disk image is copied at the time of provisioning.
[Patent Document 1] Japanese Patent Application Publication No. Hei 11-308302]
[Non-patent Document 1]
http://www.symantec.com/region/jp/products/ghost_enterprise/index.html
[Non-patent Document 2]
http://www.symantec.com/content/product.cfm?productid=9
In a present IT system of a data center and an intra-company, IT resources are secured separately for each of services (customers in the case of the data center, and businesses in the case of the intra-corporation IT system), and in each of the IT resources, a system is constructed and operated independently. Furthermore, for each of the services, the IT resources enough to withstand a load during a peak time are provided in advance. Therefore, a capacity utilization rate of the IT resources except during the peak time becomes low in general. According to a research by a US research company, it is reported that about 75% of CPU powers of servers are not being utilized all over the world. On the other hand, when peak times respectively of services operating by using IT resources are surveyed, it is found that rarely the peak times of the respective services completely coincide with one another, and that the peak times thereof are dispersed in most cases. Accordingly, even when a load more than expected occurs in one service, effective utilization of resources can be realized by utilizing resources allocated to other services. However, it is difficult to realize the above in present systems, since the systems have not been configured with assuming the above-mentioned utilization of the resources.
Against a background as described above, a demand for a system such as an on-demand computing system or a utility computing system has been emerging. This is a mechanism in which various services are integrally managed in a manner that: excess IT resources independently owned by the respective services are reserved in a resource pool shared by all of the services; and at the time of a sudden increase in a load of a resource used by a service, a resource is dynamically allocated to the service from this resource pool. This allocation of the resource to the service from the resource pool is called provisioning. At the time of provisioning, it is necessary to change the settings of the resource, which is to be allocated, to the settings required by the service before starting to utilize the resource.
However, in such a simple provisioning system as described above, only after a request for provisioning for a particular service arises, various setting operations are carried out for a resource in a resource pool. Then, after all of the setting operations are completed, the resource is deployed as a service. It takes a long time to carry out the setting operations themselves, and the service is kept suspended until the resource is deployed after the request is generated. In the case of such a simple system, it is difficult to respond to an abruptly increasing fluctuation in load in terms of time. Particularly in the case of provisioning of servers, an OS/application needed by a service have to be sequentially installed in the servers, and thereby it takes a vast amount of time to install the OS/application.
The method in Patent Document 1 where backup data are used, and the clone installation in Non-patent Document 1 are techniques for shortening such setting operations. However, in both of the methods, it is necessary to previously have information associated with H/W, such as a device driver, contained in backup data, or in an original disk image. For example, in the clone installation, a disk image can be installed only in a server of the completely same type as that of a server from which the disk image is extracted. Moreover, since pieces of information, such as a host name and an IP address, which are specific to each of the servers, are left in the disk image, it is necessary to appropriately change settings after the clone installation. These pieces of information are sometimes retained respectively by an OS and applications, and it is difficult to appropriately set up all of information items. Additionally, the number of necessary disk images corresponds to the number of kinds of services which are to be destinations of provisioning. Thus, there is a drawback that it is impossible to make a flexible configuration change, such as, newly applying a patch, or having plural levels of patches. That is, in the provisioning methods such as uploading of backup data and cloning of a disk image, it is necessary to previously prepare the backup data and the disk image respectively, and hence, it is difficult to perform provisioning of resources in a short time in response to plural different requests to the resources.
Accordingly, in order to solve the abovementioned problems, an object of the present invention is to a provide a method, a system and a controlling program thereof, which more flexibly shorten a time required for provisioning in response to plural different requests.
In a first aspect of the present invention, provided is a method for provisioning resources required by a plurality of services, the method for provisioning including the steps of: staging so as to provide at least one stage, as an intermediate state, in the process of provisioning, in a resource pool shared by the plurality of services; allocating a state of at least one resource to at least one stage, as an initial state provided in advance; and calculating a predetermined cost by using a predetermined algorithm based on a setting operation time required for causing a state of the allocated resources to transition to another stage, and optimally allocating all of the resources to the respective stages in order to minimize the predetermined cost.
In a second aspect of the present invention, provided is a staging engine for provisioning resources required by a plurality of services, the staging engine including: a graphing unit for providing at least one stage in a resource pool shared by the services, as an intermediate state in the process of provisioning, and for generating a graph having each of the stages as a node and a transition between the nodes as an edge; a transition planning unit for finding an optimum allocation of states of all of resources in the resource pool in order to minimize a predetermined cost found by a predetermined algorithm; a transition request issuing unit for issuing a resource transition process request based on a transition plan issued by the transition planning unit; an event processor for waiting for an event from an outside; and a deploying unit for finding a node nearest to a service generating a request, if the event is the request from the service.
In a third aspect of the present invention, provided is a computer program for causing a computer to execute provisioning for allocating resources required by a plurality of services, the computer program including the functions of: staging so as to provide at least one stage, as an intermediate state, in the process of provisioning, in a resource pool shared by the services; allocating a state of at least one resource to at least one stage as an initial state provided in advance; and calculating a predetermined cost by using a predetermined algorithm based on a setting operation time required for causing a state of the allocated resources to transition to another stage, and optimally allocating all of the resources to the respective stages in order to minimize the predetermined cost.
In the abovementioned invention, devising a resource pool in order to manage an excess resource shared by the certain number of services, a plurality of intermediate states (stages) are set up in a virtual manner in the process of provisioning to the respective services from the resource pool. It is possible to shorten a time required, from a reception of a resource request to a service from an outside, to a completion of the provisioning, by efficiently allocating in advance a plurality of resources to the intermediate stages in the process of the provisioning, instead of starting setting operations necessary for the provisioning from the beginning after the resource request to the service from the outside is generated. For example, assuming that servers are the resources, a time required until completion of provisioning can be shortened by having an OS previously installed in the servers, and then dynamically installing a plurality of applications operating on the OS in response to the request.
By using the method of the present invention, the number of resources allocated in a resource pool can be reduced relatively by sharing the resource pool among the plurality of services as compared with the conventional case where a resource pool is retained for each of clusters which are units in allocating resources. On the other hand, by using the method of the present invention, a resource can be provisioned from an optimum stage when a request for the provisioning of a resource is generated, whereby a time required for completing the provisioning can be shortened as compared with a conventional case where a resource pool is shared by clusters. Hereinbelow, an embodiment of the present invention will be described with reference to the drawings.
In a preferred embodiment of the present invention, procedures starting from initial states of each resource, and ending in deployment thereof for a service are identified, and are expressed in a directed graph having a state of each of the resources in each phase as a node (stage), and having a setting operation as an edge. In some cases, however, even in the case of deployments for different services, the initial state and some of intermediate states are common. In these cases, those common intermediate states are expressed as the same nodes.
For example, servers are assumed as the resources, and installing tasks of an OS or an application are assumed as setting operations. A state of being a bare-metal server (H/W), in which an OS is not yet installed, corresponds to an initial node; a state where only an OS is installed in a server corresponds to an intermediate node 1; a state where middleware and the like are installed in a server corresponds to an intermediate node 2; and a state where an application necessary for a service is installed in a server corresponds to a final node. Additionally, there are other various intermediate states such as: a state where no middleware exists; a state where a large number of applications are installed; and a state where the same application are installed with different settings.
Here, since it is possible to apparently cause transitions between the initial node and the intermediate node 1, between the intermediate node 1 and the intermediate node 2, and between the intermediate node 2 and the final node, directed edges are drawn therebetween. Additionally, in the case where another service utilizes the same OS, the intermediate node 1 is to be shared by the service and the forgoing services. Furthermore, since it is possible to go back to and restart the procedure for installation of the OS, from any one of the nodes, an edge is drawn from each of the nodes to the initial node. Additionally, when a setting operation can be cancelled (for example, when an application can be uninstalled), an edge is drawn in a reverse direction therebetween.
Each of resources in a resource pool is allocated to any one of the nodes, and can be in a state corresponding to the node (stage) to which the each of the resources is allocated. Additionally, by carrying out a process or a setting operation associated with an edge (processing operation), each of the resources is caused to transition between two nodes along the edge. When a large number of resources are allocated to a node (for example, the nodes 4, 6 and 7 in
When a request is generated by a certain service, a resource is provisioned to the service, the resource allocated to the nearest node to a state where the service is provided. After the provisioning, one resource is reduced in the pool. In response to this, an optimum allocation is calculated again, whereby allocation of the resources is changed.
Hereinbelow, an example of a system configuration according to the present invention will be described.
An entire configuration thereof as an embodiment of the present invention is shown in
An entire flow of processes in the staging engine 10 is shown in
After the graph is generated, Step S200 is a step of finding which node (stage) in the graph each of the resources such as a server and the like in the resource pool is caused to transition to (a transition planning unit). A specific algorithm of the transition plan will be described later.
Step S300 is a step of issuing an instruction for a transition process (a setting operation associated with an edge along a transition) of a resource from a current resource allocation, in accordance with the transition plan calculated in Step S200 (a transition request issuing unit). The issued instruction is carried out by the provisioning manager 20 which actually controls setting operations. In Step S300, only issuing the process instruction is carried out, and immediately after the issuing the instruction, the process goes to Step S400. If there is no need to cause any resource to transition, nothing is processed.
Step S400 is a step of waiting for an event from an external unit (the provisioning manager, a service request system, or the like) (an event processor). Here, as kinds of the events, the following three kinds are assumed:
1) Resource request event, which indicates that a request for a resource is generated by a service;
2) Transition process completion event, which indicates that the transition instruction (setting operation) issued to the provisioning manager is completed; and
3) Event of adding or deleting a resource to and from the resource pool, the event indicating a change in the number of resources in the resource pool.
If any one of the above events occurs, the process goes to Step S500.
In Step S500, a kind of the event which occurs is judged to be one of the above three. The process goes to Step S600 in the case of the resource request event, or the process goes back to Step S200 in the other cases.
In Step S600, an instruction is issued to the provisioning manager 20 and the like in order to deploy a resource for the service generating the request, the resource located at a stage which is the nearest (requires the shortest processing time until the completion of deployment) to the service (a deploying unit). The provisioning manager 20 may not actually deploy the resource, but another system may do. In the staging engine 10, the process goes back to Step S200 immediately after issuing the deployment instruction (in Step S600).
First, in Step S110, with respect to the resource pool and the services A, B and C, which utilize the resource pool, a procedure of provisioning for each of the services is divided into intermediate states (stages). A result of the division is shown in
Next, in Step S120, the intermediate states which are identical to each other among the divided intermediate states are combined as one to generate a graph as shown in
Furthermore, in Step S130, an edge in a reverse direction in a case where a setting operation is reversible (a case where cancellation of the setting operation is possible, or a case where there is an operation for returning to one stage before), and an edge to an initial state (it is possible by a deleting operation) in OS/application installation are added.
Hereinafter, a specific scenario is shown in
All of the three servers are assumed to be at the node 1 in the initial state. At this point, first of all, a graph such as the above is generated, and an optimum server allocation calculation is performed (
Subsequently, the servers are caused to transition (move between stages) based on a result of the allocation calculation (
In the resource transition plan (Step S200, or the transition planning unit) in
Inputs:
A weight of each edge is defined as a time required for a transition between nodes (a time required for a setting operation)
A deployment request distribution for n resources is estimated based on track record data of past deployment requests. As a precondition to this estimation, it is assumed that the track record data of the past requests are applicable (on the precondition that the deployment request distribution for n resources are the same as, or coincide within few errors with, the past deployment request distribution, just like, for example, changes from Sunday to Saturday of one week, or changes from 0:00 to 23:00 of a day). Alternatively, the deployment request distribution for n resources may be dynamically estimated in each case by using a prediction technique or the like. (In general, it is assumed that a generation probability can have a different generation probability for each unit time. Additionally, the unit time is set to be at the same level as compared to times for transitions between nodes.)
The cost is determined by a product of a time from generation of a deployment request to completion of the deployment, and a penalty for a service for which the deployment is performed.
Hereinbelow, specific three kinds of algorithm, i.e., Algorithms 1 to 3 will be described. Hereinafter, the following parameters or expressions will be used.
d(I, j) denotes a time required for a transition from node i to node j (a weight of an edge).
S(n1, n2, n3, . . . , nm) denotes a state where n1 resources, n2 resources, n3 resources, and nm resources are allocated to a node 1, to a node 2, to a node 3, and to a node m, respectively.
X(n1, n2, n3, . . . , nx) indicates that requests for n1 resources, for n2 resources, and for nx resources are generated by a service 1, by a service 2, and by a service X, respectively.
ωx denotes a penalty for the service X.
For example, in an example shown in
<Algorithm 1>
In this part, a first algorithm will be described.
Algorithm 1 finds an allocation pattern which is one move or two moves ahead of a current allocation pattern, and which minimizes a cost, by running simulations based on resource request distributions of the respective services.
All possible combinations of resource transitions are considered from a view point of whether or not each of the resources transitions to an adjacent node from the current resource allocation. For example, taking the example in
S(2, 0, 0, 0) for a case where none of the two resources transition;
S(1, 1, 0, 0) for a case where only one resource transitions; and
S(0, 2, 0, 0) for a case where both of the two resources transition.
For each of the allocation candidates, resource requests from the services in a certain time period (0≦t≦T) are generated based on random numbers, or based on particular probability distributions. Then, moves at a time of the deployments are simulated, and costs required for the deployments are calculated. If any number of times for running simulations is denoted as N, this calculation is repeated N times. Thereby, a candidate which minimizes a mean of costs among the candidates is set as a next allocation destination, and in order to realize the state thereof, a transition process is carried out.
Specifically, in accordance with an OptimalAllocation function as follows, an optimal allocation is found.
Method:
From the current resource allocation (s_cur), a group (S) consisting of resource allocation candidates which are one move or two moves ahead. “S” includes “s_cur” in which no transitions are caused.
The minimum cost c(s_next) and the resource allocation s_next are set and outputted as c, and s_opt.
Flowcharts in
First of all, in an initial state S1, two servers are allocated. As states possible to be realized next to the state S1 (the state which is one move ahead), states S1-1 and S1-2a are considered. In the state S1-1, no transitions are caused (that is, S1-1=S1), and in the state S1-2a, one server is on the way of a transition to a node 2. Here, as a cost C(S1) in S1, the smaller of cost between those of S1-1 and S1-2a is adopted. That is, if written in a mathematical expression, this is expressed as C(S1)=Min(C(S1-1), C(S1-2)), where C(S1-2)=C(S1-2a).
Here, suppose that a request for one server is generated by a service in S1-1, one server is deployed for the service in the first place, and subsequently, a consideration is again given to transition candidates for the other server remaining after excluding the deployed server. In this case, as states possible to be realized next to S1-1, there are two kinds which are: a state S1-1-1 (=S1-1) where the remaining server is not caused to transition; and a state S1-1-2 where the remaining server is on the way of a transition from a node 1 to the node 2. Here, when the cost required for the foregoing deployment is denoted as C1, a value obtained by adding, to the cost C1, the smaller of cost between those of S1-1-1 and S1-1-2 is adopted as a cost C(S1-1) in the state S1-1. That is, this is expressed as C(S1-1)=C1+Min(C(S1-1-1), C(S1-2-2)).
On the other hand, upon completion of a transition from S1-2a to S1-2, a consideration is given to succeeding transition candidates. As the succeeding transition candidates, there are possible three kinds which are: S1-2-1 (a state where no transitions are caused, that is, =S1-2); S1-2-2 (a state where one server is on the way of a transition from the node 2 to a node 3); and S1-2-3 (a state where one server is on the way of a transition from the node 2 to a node 4). At this time, as a cost C(S1-2) in S1-2, the smallest of cost among those of S1-2-1, S1-2-2, and S1-1-2 is adopted. That is, this is expressed as C(S1-2)=Min(C(S1-2-1), C(S1-2-2), C(S1-2-3)).
Requests are generated N times by simulation, and the calculations as the above are performed for each of the requests to find a minimum cost and a resource allocation minimizing the cost. This resource allocation is set to be an allocation in the optimal staging.
<Algorithm 2>
In this part, a second algorithm will be described. In Algorithm 2, all of combinations of resource allocations are considered in the following manner. An expected value of each of the combinations is calculated in consideration to all possible provisioning requests, so as to find out a combination of resource allocations minimizing a expected value.
1. for each allocation S={all allocation patterns of resources}
2. Find an allocation S minimizing an E(S).
3. In order to realize a resource allocation found in 2., determine which and how resources should be caused to transition from a current resource allocation. This determination is made by using an existing solving method of a graph matching problem, the solving method using a method for minimizing a transition cost, and a method for prioritizing a transition for a service of high priority.
Calculations of Algorithm 2 are shown in
An illustration in the upper part of
A table in the lower part of
A table in the lower part of
Because Algorithm 2 analytically finds a result, it involves a smaller amount of calculation than Algorithm 3. However, in a case where another resource request is generated during execution of a transition for the allocation calculated in 2., a cost can be outrageous sometimes, and a resulting allocation can be slightly different from an optimal one sometimes. Here, when the number of resource in a pool, the number of nodes, and the number of clusters are denoted as n, m, and r, respectively, the number of elements in each of matrices, which respectively compose
rHn×mHn [Equation 2]
<Algorithm 3>
In this part, a third algorithm will be described. In Algorithm 3, in the following manner, a provisioning request generated by each service is treated independently from one generated by another service, whereby an amount of calculation is reduced.
1. for each allocation S={all allocation patterns of resources}
where if
ωx denotes a penalty for a delay in deployment for a service X; Px(i) denotes a generation probability of a request for i resources in the service X; nj denotes the number of resources allocated to the j-th nearest node from the service X, and ck denotes a weight of a transition to the service X from the k-th nearest node from the service X.
2. An allocation S minimizing E(S) is found. This is the effective allocation S.
3. In order to realize the resource allocation found in 2., it is determined which and how resources should be caused to transition from a current resource allocation. This determination is made by using an existing solving method for graph matching problem, such as a method for minimizing a transition cost, and a method for prioritizing a transition for a service with high priority.
Since an amount of calculation in this Algorithm 3 only becomes (combinations of resource allocation)
mHn [Equation 4],
the amount of calculation is reduced as compared to Algorithm 2. However, depending on topologies of a graph, there can be a case where extremely inefficient allocation is outputted.
In
In
Next, the service A is remarked as shown in
In
Furthermore, allocation of resources is changed in
Hereinbelow, a description will be provided for a system example in the field of on-demand computing by use of a software product named Tivoli (registered trademark) Intelligent Orchestrator (TIO) of IBM Corporation. This example employs a system which can execute provisioning by using TIO as a provisioning manager.
It is assumed that a data center utilizing a plurality of cluster systems for services is managed by TIO. The respective services are used for different purposes and different clients, and therefore have different peaks. If the peaks can be perfectly predicted, it is possible to previously make the system prepared for the peaks by carrying out provisioning in advance. However, it is difficult to perfectly predict the peaks in advance, and additionally, it is impossible to response to a load beyond expectation.
First of all, a directed graph is generated based on an installation procedure (setting operations) which is carried out at the time of provisioning servers to the respective clusters. In generating the directed graph, a node indicates a state where a certain OS or application is installed, and an edge indicates an operational step for installing the OS or application.
A weight of each of the edges represents a processing time of the operational step (for example, a time for an installation and a change of components), and the weight is automatically acquired as an initial value through a test-installation operation. Alternatively, this initial value can be provided by a manger manually. A probability of generation of requests to each of the clusters is determined based on past requests for servers, which have been generated in the each cluster, and is constantly updated as time passes. Additionally, a penalty for a delay in a time of provisioning to a cluster is determined by an SLA of each of the services, whereby a larger penalty is given to a cluster with a higher SLA, and a lower penalty is given to a cluster without the higher SLA.
Based on these data, determination is made on to which nodes currently pooled servers should be allocated, and actual installation operations are executed in order to realize the determined state. When there is a request for a resource (server) from any one of the clusters at this point of time, a server located at a stage whose time required for provisioning is shortest is provisioned.
If the system is provided with a forecasting function for peak times of the respective clusters, more efficient provisioning becomes possible by changing generation probabilities of requests from the respective services.
Although the present invention has been described hereinabove with reference to the embodiment and the example, a technical scope of the present invention is not limited to the scope described in the abovementioned embodiment. Various alterations or improvements can be added to the abovementioned embodiment. It is apparent from the scope of claims that embodiments where such alterations or modifications are incorporated can be included in the technical scope of the present invention.
The method for provisioning explained as the one embodiment of the present invention can be realized by a program which enables a system on a computer, or a computer, to execute functions for the method. Additionally, a storage medium for storing the above program can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or, device or apparatus), or a propagation medium. Examples of computer-readable media include a semiconductor, a solid state storage device or and a magnetic tape. Examples of removable computer-readable media include a semiconductor or solid state storage device, a magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Examples of currently existing optical disks include a compact disc read-only memory (CD-ROM), a compact disc read/write (CD-R/W), and a DVD.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2005-326047 | Nov 2005 | JP | national |
This application is a Continuation of U.S. application Ser. No. 11/557,577 filed Nov. 8, 2006, the complete disclosure of which, in its entirety, is herein incorporated by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 7581008 | Zhang et al. | Aug 2009 | B2 |
| 20030126202 | Watt | Jul 2003 | A1 |
| 20050102398 | Zhang et al. | May 2005 | A1 |
| 20050268063 | Diao et al. | Dec 2005 | A1 |
| 20050289499 | Ogawa et al. | Dec 2005 | A1 |
| 20060159014 | Breiter et al. | Jul 2006 | A1 |
| 20060179383 | Blass et al. | Aug 2006 | A1 |
| Number | Date | Country |
|---|---|---|
| 11-308302 | Nov 1999 | JP |
| Number | Date | Country | |
|---|---|---|---|
| 20080183544 A1 | Jul 2008 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11557577 | Nov 2006 | US |
| Child | 12058920 | US |
