Abstract:
Methods and apparatus, including computer program products, for matching software requirements against target system landscape descriptions and for applying rating metrics to intermediate results during the matchmaking process. Data are received as inputs describing the requirements and dependencies of a particular software application and the resources available in the target system that may be able to meet these requirements. These data are searched for combinations of system resources that will satisfy the requirements of the application as well as optimization metrics and other requirements supplied by the user. Once a match is found, it is given a rating and ranked against any other matches.

Description:
BACKGROUND 
     This invention relates to data processing by digital computer, and more particularly to determining software deployment parameters. 
     An interconnected collection of computing resources may be referred to as a system landscape. Such a system landscape includes the computers, networks, and other connected devices available within a given scope. The system landscapes of large enterprises are generally complex and heterogeneous. They include various types of computers (such as mainframes, servers or workstations), different storage systems, and multiple network topologies, with various levels of interconnection. Further, the operators of such a system may have a variety of policies and rules governing the use of these resources, designed to optimize such factors as availability, quality of service, and cost. 
     Modern software applications exhibit a similar degree of complexity, i.e., they have complex interdependencies, different sizing variations, and specific system requirements. This complexity is compounded, generating significant challenges, when such software is to be installed into a target system landscape. Since many dependencies have to be taken into account, and multiple combinations of resources may satisfy the software&#39;s requirements to varying degrees of efficiency, it is difficult to manually find an appropriate assembly of all the required resources needed to run the software in a target landscape that ensures optimal operation while complying with the policies of the operators of the system. 
     SUMMARY 
     The description describes methods and apparatus, including computer program products, for matching software requirements against target system landscape descriptions and for applying rating metrics to intermediate results during the matchmaking process. 
     In general, a process of matching requirements to resources involves receiving as inputs a description of the requirements or dependencies of a particular software application to be deployed on a computer system, and a description of the resources available in the system that may be able to meet these requirements, referred to as the system landscape, together with any further conditions governing such a deployment. The process verifies that these inputs refer to resources in a common manner and may filter out of its representation of the system landscape resources that are not relevant to the requirements of the software application being deployed. The process then searches through the system landscape description for combinations of resources that will satisfy the requirements of the application, as well as any optimization metrics and other requirements that may be supplied. Such other requirements may come from the user, for example specifying that he must have physical access to the resources used, or from the owner of the computer network, for example specifying a preference that certain types of applications be run on certain systems. Optimization metrics define relationships between system landscape resources and program requirements and are used to assure that an optimal combination of resources is determined. Once a match is found, it is given a rating based on the optimization metrics and ranked against any other matches. In one embodiment, the first match with a rating above a threshold value is returned. In other embodiments, the process repeats until another condition is met, such as finding a given number of matches above the threshold, or fining all possible matches. 
     To implement this process a system is described in which a user makes a request that a combination of resources be provided that will meet the requirements or dependencies of a particular software application. An optimizer, having access to a description of the system landscape and any metrics to be used in evaluating potential matches of requirements to resources, uses the process to find and evaluate such matches, and then outputs to the user a list of which resources may be used to deploy the software application. Such output may include simply the best match, or alternatively may include a number of matches above some threshold measure of quality, or may include all the matches that the process was able to discover. In another variation, the metrics used in evaluating matches are supplied by the application, as part of the requirements and dependencies that it communicates to the optimizer. The optimizer may, as part of the process, evaluate the inputs to assure that they use a common method of describing requirements and resources. It may also simplify the contents of the inputs to assure more efficient operation of the matching process. 
     One advantage of the described process is that it address challenges presented by the generally complex and heterogeneous system landscape of a modern computing environment and similarly complex requirements of modern software. By automating the process of finding an appropriate assembly of all the resources available to run a particular software application in a target landscape, this process makes it easier to deploy software in such a complex computing environment. It can be configured to find the best combination of resources available, or to provide a number of configurations, allowing the user from to choose from among a manageable number of configurations rather than having to manually evaluate each possible arrangement of resources. 
     Other features and advantages of the invention are apparent from the following description, and from the claims. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram. 
         FIG. 2  is a flow diagram. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     As shown in  FIG. 1 , a process  200  can be implemented in a computing system  100 . Application  110  provides a deployment descriptor  112 . Deployment optimizer  120  has access to a system model description  102  and a system landscape descriptor  122 . The system model description  102  defines the grammar and semantics of the deployment descriptor  112  and the landscape descriptor  122 . The deployment descriptor  112  specifies the requirements and the dependencies of the application  110 , such as operating system constraints, hardware dependencies like central processing unit (CPU) speed or storage capacity, and other software components that are required by the software. System landscape descriptor  122  describes components  126  of the system landscape including available operating systems, hardware capabilities, and software components, and represents a snapshot of an organization&#39;s computing and networking environment, a data structure representing that environment according to the system model description  102 . Optimizer  120  also has access to optimization metrics  124 , which represent organization specific optimization policies expressed through functions, for example, if a policy were to run memory-intensive programs on one particular set of machines and computation-intensive tasks on a different set, an optimization metric may assign a value to a resource allotment that complied with this policy that is higher than the value assigned to a resource allotment that did not comply. System model description  102 , system landscape descriptor  122 , and optimization metrics  124  may be in deployment optimizer  120 &#39;s internal memory or may be available to deployment optimizer  120  from an outside source as needed. 
     User  130  sends a request  132  to deployment optimizer  120 . The request  132  inquires which of components  126  may be used to deploy application  110  in the system landscape represented by system landscape descriptor  122 . Request  132  contains user constraints  134  and an indication  136  of which optimization metrics  124  should be used. The request is accompanied by deployment descriptor  112  from application  110 . When this request is received, deployment optimizer  120  uses process  200  to generate a result set  138  which is returned to user  130 . 
     As shown in  FIG. 2 , a process  200  receives ( 210 ) as inputs a system model description  102 , a deployment descriptor  112 , and a target system landscape descriptor  122 . These inputs  102 ,  112 , and  122  are validated ( 220 ) to verify that the deployment descriptor  112  and the landscape descriptor  122  both refer to a common system model and use a common grammar and semantics as defined in the system model description  102 . Preparation ( 221 ) of the input data may include conversion to an internal representation, which allows faster and more efficient processing, and normalization, in which the internal form of the data is enhanced with implicit information to allow easier processing. For example, this may include replacing diverse descriptions of equivalent hardware resources with a common identifier. 
     After the inputs have been validated ( 220 ) and prepared ( 221 ), they may be filtered ( 222 ) to constrain the search space for the rest of process  200 . Constraints  134  are received ( 213 ) from the user  130 . Optimization metrics  124  are received ( 214 ) from internal memory or an external source. These are used together with deployment descriptor  112  to remove irrelevant components  126  of the landscape descriptor  122  which need not be processed. For example, the user may decide that computing resources having a CPU speed lower than 500 MHz should not be taken into account, in which case the filtering ( 222 ) will remove such computing resources from the system landscape descriptor  122 . Likewise, the deployment descriptor  112  or optimization metrics  124  could define such constraints. Optimization metrics  124  may be used at several stages of process  200  to influence the way in which results are generated and evaluated. The user may specify which optimization metrics  124  to use in a particular instance of the process by including a choice of metrics  136  as part of their request  132  that the process be used. The filtering ( 222 ) produces a reduced landscape description and ensures that only relevant parts of the system landscape description  122  are searched for a match. 
     Matchmaking ( 224 ) includes searching for combinations of resources  126  from the landscape descriptor  122  that match the requirements of the software to be deployed as defined in the deployment descriptor  112 . The combinations found by this process are referred to as matches. Matchmaking ( 224 ) may be a search technique combined with a pattern matching algorithm. The pattern matching can be implemented in various ways such as constraint satisfaction techniques or graph pattern matching algorithms. Constraints restrict which matches are generated by, for example, specifying that certain combinations of resources  126  not be assembled. These constraints may be input ( 213 ,  214 ) via user constraints  134  or optimization metrics  124 , or read from the deployment descriptor  112 . 
     After a match has been generated, it is rated and ranked ( 226 ) using the appropriate optimization metrics  124 . The functions in the optimization metrics  124  are used to determine a level of quality for the match. The level of quality of the match is compared ( 228 ) to a required level of quality defined in the deployment descriptor  112 , and if the level of quality of the match is at least equal to the required level of quality, the match is considered sufficient and recorded ( 232 ) in the result set  138 . If the match is not sufficient and more matches are possible, the process enters a loop  230 , finding ( 224 ) and rating ( 226 ) additional matches, the loop being terminated ( 236 ) when a sufficient match is found or no more matches are possible. Once the loop  230  has been terminated ( 236 ), the result set  138  to be returned is displayed ( 238 ) to the user  130 . Variations may include continuing execution until all possible matches, or some intermediate number of matches, are found, and then returning the highest ranked of those matches. Further variations may include returning more than one match, such as the five highest-ranked matches, or all matches above a particular level of quality. Care must be taken to avoid defining a termination condition that may never be reached. 
     The functions represented by the optimization metrics  124  may be combined in various ways, including composition of the functions through a formula. Optimization metrics may also be combined through prioritization, where matches which have the same ranking under the highest priority metric are further compared based on a metric with the next-lower priority. 
     In another variation, normalization of the input data may be done at the end of the filtering, which may additionally accelerate the optimization. 
     In the basic form, the deployment descriptor  112  does not allow structure variances, i.e. the software to be deployed has a static structure and does not allow alternative combinations of parts of it. However, there are software systems that require landscape specific adaptation. For instance, before installing a cluster-based application server the user has to decide how many servers need to be installed. The number of servers, however, is dependent on the load and the power of the computing resources used as servers. One solution for this is to search for solutions for all possible structure variances of the software in the target landscape. This, however, would multiply the number of loops  230  with the number of possible variations of the structure the software can accommodate. Heuristics like genetic algorithms can be used to constrain the search space and significantly cut down processing time. 
     The above-described techniques can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The implementation can be as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     Method steps can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). Modules can refer to portions of the computer program and/or the processor/special circuitry that implements that functionality. 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in special purpose logic circuitry. 
     To provide for interaction with a user, the above described techniques can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     The above described techniques can be implemented in a distributed computing system that includes a back-end component, e.g., as a data server, and/or a middleware component, e.g., an application server, and/or a front-end component, e.g., a client computer having a graphical user interface and/or a Web browser through which a user can interact with an example implementation, or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet, and include both wired and wireless networks. 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     The invention has been described in terms of particular embodiments. Other embodiments are within the scope of the following claims. The above examples are for illustration only and not to limit the alternatives in any way. The steps of the invention can be performed in a different order and still achieve desirable results.