Abstract:
A method for optimizing query compilation by tuning memory across a database cluster is provided. The method may include receiving, by a global memory tuner, memory configuration information from a plurality of nodes within the database cluster. The method may also include determining a node within the plurality of nodes having a least available memory value relative to a remainder of nodes within the plurality of nodes based on the received memory configuration information. The method may then include calculating a globally tuned memory value based on a memory value associated with the determined node. The method may further include determining a memory distribution based on the calculated globally tuned memory value. The method may also include sending the determined memory distribution to the plurality of nodes.

Description:
BACKGROUND 
       [0001]    The present invention relates generally to the field of computing, and more particularly to distributed database memory tuning. 
         [0002]    Database memory may be tuned across database nodes in a cluster that may be used to execute distributed database queries. Memory tuning within databases may use cost-based query optimization. A cost-based query optimizer may generate query plans based on available resources, including available memory. In a shared-nothing architecture, a centralized query optimizer may be used to compile queries at a given node and then distribute the query to all other nodes in the cluster. 
       SUMMARY 
       [0003]    According to one exemplary embodiment, a method for tuning memory across a database cluster is provided. The method may include receiving, by a global memory tuner, memory configuration information from a plurality of nodes within the database cluster. The method may also include determining a node within the plurality of nodes having a least available memory value relative to a remainder of nodes within the plurality of nodes based on the received memory configuration information. The method may then include calculating a globally tuned memory value based on a memory value associated with the determined node. The method may further include determining a memory distribution based on the calculated globally tuned memory value. The method may also include sending the determined memory distribution to the plurality of nodes. 
         [0004]    According to another exemplary embodiment, a computer system for tuning memory across a database cluster is provided. The computer system may include one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, whereby the computer system is capable of performing a method. The method may include receiving, by a global memory tuner, memory configuration information from a plurality of nodes within the database cluster. The method may also include determining a node within the plurality of nodes having a least available memory value relative to a remainder of nodes within the plurality of nodes based on the received memory configuration information. The method may then include calculating a globally tuned memory value based on a memory value associated with the determined node. The method may further include determining a memory distribution based on the calculated globally tuned memory value. The method may also include sending the determined memory distribution to the plurality of nodes. 
         [0005]    According to yet another exemplary embodiment, a computer program product for tuning memory across a database cluster is provided. The computer program product may include one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor. The computer program product may include program instructions to receive, by a global memory tuner, memory configuration information from a plurality of nodes within the database cluster. The computer program product may also include program instructions to determine a node within the plurality of nodes having a least available memory value relative to a remainder of nodes within the plurality of nodes based on the received memory configuration information. The computer program product may then include program instructions to calculate a globally tuned memory value based on a memory value associated with the determined node. The computer program product may further include program instructions to determine a memory distribution based on the calculated globally tuned memory value. The computer program product may also include program instructions to send the determined memory distribution to the plurality of nodes. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0006]    These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding the invention in conjunction with the detailed description. In the drawings: 
           [0007]      FIG. 1  illustrates a networked computer environment according to at least one embodiment; 
           [0008]      FIG. 2  is a block diagram of a database cluster having memory tuned by a global memory tuner according to at least one embodiment; 
           [0009]      FIG. 3  is an operational flowchart illustrating a process for global database memory tuning according to at least one embodiment; 
           [0010]      FIG. 4  is a block diagram of internal and external components of computers and servers depicted in  FIG. 1  according to at least one embodiment; 
           [0011]      FIG. 5  is a block diagram of an illustrative cloud computing environment including the computer system depicted in  FIG. 1 , in accordance with an embodiment of the present disclosure; and 
           [0012]      FIG. 6  is a block diagram of functional layers of the illustrative cloud computing environment of  FIG. 5 , in accordance with an embodiment of the present disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this invention to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments. 
         [0014]    The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0015]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0016]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0017]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0018]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
         [0019]    These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0020]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0021]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
         [0022]    The following described exemplary embodiments provide a system, method and program product to tune database memory for query stability. As such, the present embodiment has the capacity to improve the technical field of distributed database memory tuning by configuring memory across nodes having differing available resources without negatively impacting query optimization. More specifically, memory configuration data may be collected from nodes within a cluster and then the node with the least amount of available physical memory may be determined. Thereafter, a memory distribution is determined based on the node with the least amount of available memory and then the determined distribution may be sent to the other nodes within the cluster. 
         [0023]    As described previously, database memory may be tuned across database nodes in a cluster that may be used to execute distributed database queries. Memory tuning within databases may use cost-based query optimization. A cost-based query optimizer may generate query plans based on available resources, including available memory. In a shared-nothing architecture, a centralized query optimizer may be used to compile queries at a given node and then distribute the query to all other nodes in the cluster. Therefore, if a query is optimized at one node within the cluster and if the query is then distributed to the other nodes in the cluster (with different memory configurations), the query may exhibit poor and unpredictable query performance. As such, it may be advantageous to, among other things, provide a way to uniformly tune memory across the nodes of a database cluster to enhance query stability. 
         [0024]    According to at least one embodiment, a local memory tuner (LMT) may be present on each database node within the cluster. Additionally, a global memory tuner (GMT) may be present on one node within the database cluster. The GMT may periodically collect memory configuration information from each node to determine how much memory may be available for tuning across the nodes of the cluster. Then, the GMT may determine which node has the least amount of available physical memory. The determined least amount of available physical memory may then become the globally tuned memory amount for each node within the cluster. Alternatively, the globally tuned memory may be determined to be less than the amount of physical memory on the smallest node in the cluster, thus leaving some locally tuned memory available at all nodes in the cluster. Next, the GMT determines a distribution for the globally tuned memory using aggregated cost/benefit data obtained from remote members (e.g., local benefit collectors (LBC)). After the distribution is determined, the distribution may be sent to each node for the nodes to apply and the node with the GMT may apply the distribution locally. Thus, each node may have similar starting memory configurations. 
         [0025]    While the GMT is tuning globally tuned memory, the LMTs may also tune the memory within nodes. The LMTs may be limited to tuning memory not partitioned for the globally tuned memory (i.e., the total physical memory on a node minus the globally tuned memory partition). This remaining physical memory (i.e., locally tuned memory) may differ by node depending on how much total physical memory the node has. If the globally tuned memory is calculated as the amount of memory on the node in the cluster with the least amount of physical memory, there may be at least one node that may not have an active LMT. The LMTs may determine how to distribute the locally tuned memory and apply configuration changes locally. Thus, each node within the cluster may determine how to best use any additional memory. 
         [0026]    According to at least one embodiment, a centralized query optimizer in each node may only consider the memory configuration specified by the GMT as input. Thus, the generated query plans may be based on the memory configuration that may be guaranteed to be configured for each node within the cluster. Resulting query plans may therefore be generated consistently in terms of memory inputs. 
         [0027]    Referring to  FIG. 1 , an exemplary networked computer environment  100  in accordance with one embodiment is depicted. The networked computer environment  100  may include a server  102   a  with a processor  104   a  and a data storage device  106   a  that is enabled to run a software program  108   a  and a global memory tuner program  110   a  that may interact with a database  114   a.  The networked computer environment  100  may also include a second server  102   b  with a processor  104   b  and a data storage device  106   b  that is enabled to run a software program  108   b  and a global memory tuner program  110   b  that may interact with a database  114   b  and a communication network  116 . The networked computer environment  100  may include a plurality of servers  102   a  and  102   b,  only two of which are shown. The communication network  116  may include various types of communication networks, such as a wide area network (WAN), local area network (LAN), a telecommunication network, a wireless network, a public switched network and/or a satellite network. It should be appreciated that  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. 
         [0028]    The server  102   a  may communicate with the server  102   b  via the communications network  116 . The communications network  116  may include connections, such as wire, wireless communication links, or fiber optic cables. As will be discussed with reference to  FIG. 4 , server  102   a  may include internal components  902   a  and external components  904   a,  respectively, and server  102   b  may include internal components  902   b  and external components  904   b,  respectively. Servers  102   a  and  102   b  may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). Servers  102   a  and  102   b  may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud. Servers  102   a  and  102   b  may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database  114   a,    114   b.  According to various implementations of the present embodiment, the global memory tuner program  110   a,    110   b  may interact with a database  114   a,    114   b  that may be embedded in various storage devices, such as, but not limited to a computer/mobile device, a networked server  102   a,    102   b,  or a cloud storage service. 
         [0029]    According to the present embodiment, a user using a server  102   a,    102   b  may use the global memory tuner program  110   a,    110   b  (respectively) to tune the physical memory for each node in a cluster based on the node having the least amount of available physical memory. Thus, memory may be allocated consistently across the nodes of the cluster, thereby improving query stability. The global memory tuner process is explained in more detail below with respect to  FIGS. 2 and 3 . 
         [0030]    Referring now to  FIG. 2 , a block diagram of a database cluster  200  having memory tuned by the global memory tuner (GMT)  202  according to at least one embodiment is depicted. The cluster  200  may include one or more nodes  204   a - c  (e.g., server  102   a  ( FIG. 1 )) connectively coupled with the GMT  202  running on one of the nodes (e.g.,  204   b ). Each node  204   a - c  within the cluster  200  may be connected (i.e., connectively coupled) via a communication network  116  ( FIG. 1 ), whereby the nodes  204   a - c  within the cluster  200  may communicate with each other. 
         [0031]    Furthermore, each node  204   a - c  may include physical memory that may be partitioned as globally tuned memory  206   a - c  and may also include additional physical memory partitioned as locally tuned memory  208   a - b . Globally tuned memory  206   a - c  on all nodes  204   a - c  may be managed by the GMT  202 , and locally tuned memory  208   a - b , if present, may be managed by a local memory tuner (LMT)  210   a - c . Cost/benefit data from each node  204   a - c  may be collected by a local benefit collector (LBC)  212   a - c  and transmitted (e.g., via a communication network  116  ( FIG. 1 )) to the GMT  202  for use in the managing globally tuned memory  206   a - c  for the nodes  204   a - c  in the cluster  200 . Additionally, each node  204   a - c  may include a query optimizer  214   a - c  for determining the most efficient way to perform a query after considering multiple possible query plans. The query optimizer  214   a - c  may exclusively consider the globally tuned memory  206   a - c  when compiling a database query as the globally tuned memory  206   a - c  may be known to be available on each node  204   a - c.    
         [0032]    In the illustrated example database cluster  200 , the GMT  202  runs on node  204   b  and collects local benefit data from the LBCs  212   a - c  to use in determining a globally beneficial memory configuration as will be described in detail below with respect to  FIG. 3 . Once a globally beneficial memory configuration has been determined, the new memory configuration may be sent out and applied to remote nodes  204   a  and  204   c.  Additionally, the new memory configuration may be applied locally to node  204   b.    
         [0033]    As depicted in  FIG. 2 , node  204   c,  having the least amount of physical memory, has all available physical memory in the node  204   c  partitioned as globally tuned memory  206   c.  The additional physical memory in the other nodes  204   a - b  may then be partitioned as locally tuned memory  208   a - b  managed by each node&#39;s  204   a - b  LMT  210   a  and  210   b  (respectively). The LMTs  210   a - c  may also receive benefit information from the LBCs  212   a - c  to determine how to best allocate the locally tuned memory  208   a - b . Periodically, a new configuration may be applied to the locally tuned memory  208   a - b  using the LMTs  210   a - c  determination. 
         [0034]    Thus, the globally tuned memory  206   a - c  may be tuned according to an aggregated global benefit and the locally tuned memory  208   a - b  may be tuned according to a local benefit. By separating the globally tuned memory  206   a - c  from the locally tuned memory  208   a - b , the query optimizer  214   a - c  may be restricted to seeing only configuration changes made to the globally tuned memory  206   a - c . This may ensure that no memory-related plan changes if the same query is compiled on different nodes  204   a - c . Furthermore, partitioned globally tuned memory  206   a - c  may ensure that the physical memory assumed to be available at query compilation (i.e., globally tuned memory  206   a - c ) will be available at runtime (unless a memory reconfiguration occurs between query compilation and query execution). 
         [0035]    Referring now to  FIG. 3 , an operational flowchart illustrating the exemplary global database memory tuner process  300  by the global memory tuner program  110   a  and  110   b  ( FIG. 1 ) according to at least one embodiment is depicted. 
         [0036]    At  302 , memory configuration information is collected from the nodes  204   a - c  ( FIG. 2 ) of the cluster  200  ( FIG. 2 ). According to at least one embodiment, the GMT  202  ( FIG. 2 ) may periodically collect memory configuration information from each node  204   a - c  ( FIG. 2 ) to determine how much memory is available for tuning across the cluster  200  ( FIG. 2 ). The memory configuration information may include a variety of memory-related information such as a memory value (i.e., physical memory value) indicating the amount of available physical memory (e.g., 32 gigabytes of available random access memory (RAM)). 
         [0037]    Next, at  304 , the node  204   a - c  ( FIG. 2 ) with the least amount of available physical memory is determined. According to at least one embodiment, the GMT  202  ( FIG. 2 ) may determine which node (e.g.,  204   c  ( FIG. 2 )) within the cluster  200  ( FIG. 2 ) has the least amount of available physical memory based on the collected memory configuration information. According to at least one implementation, the GMT  202  ( FIG. 2 ) may compare the available physical memory values for each node  204   a - c  ( FIG. 2 ) within the cluster  200  ( FIG. 2 ) and generate a list of nodes  204   a - c  ( FIG. 2 ) ordered from least available physical memory (i.e., the head of the list) to most available physical memory (i.e., tail of the list). After generating a complete ordered list, the GMT  202  ( FIG. 2 ) may identify the node (e.g.,  204   c  ( FIG. 2 )) having the least amount of available physical memory from the head of the list. 
         [0038]    For example, if node  204   a  ( FIG. 2 ) has 24 gigabytes of available physical memory, node  204   b  ( FIG. 2 ) has 32 gigabytes of available memory, and node  204   c  ( FIG. 2 ) has 16 gigabytes of available physical memory, then the resulting ordered list would have node  204   c  ( FIG. 2 ) at the head of the list, then node  204   a  ( FIG. 2 ), and then node  204   b  ( FIG. 2 ) at the tail of the list. Thus, the GMT  202  ( FIG. 2 ) would determine that node  204   c  ( FIG. 2 ) has the least amount of available physical memory since node  204   c  ( FIG. 2 ) is at the head of the ordered list. 
         [0039]    According to at least one embodiment, the value of the globally tuned memory  206   a - c  ( FIG. 2 ) for the nodes  204   a - c  ( FIG. 2 ) within the cluster  200  ( FIG. 2 ) may be set to equal the amount of available physical memory in the node (e.g.,  204   c  ( FIG. 2 )) having the least amount of available physical memory. Any nodes (e.g.,  204   a - b  ( FIG. 2 )) having additional memory beyond the least amount of available physical memory may partition the additional memory as locally tuned memory  208   a - b  ( FIG. 2 ). 
         [0040]    Continuing the previous example, node  204   c  ( FIG. 2 ), having the least amount of available physical memory, would be used to set the globally tuned memory  206   a - c  ( FIG. 2 ) to 16 gigabytes (i.e., the amount of available physical memory that node  204   c  ( FIG. 2 ) has) for all nodes  204   a - c . As such, all of the physical memory of node  204   c  ( FIG. 2 ) would be partitioned as globally tuned memory  206   c  ( FIG. 2 ). Node  204   a  ( FIG. 2 ) would have any remaining physical memory (i.e., 8 gigabytes) partitioned for locally tuned memory  208   a  ( FIG. 2 ) and node  204   b  ( FIG. 2 ) would have any remaining physical memory (i.e., 16 gigabytes) partitioned for locally tuned memory  208   b  ( FIG. 2 ). 
         [0041]    According to at least one other embodiment, the value of the globally tuned memory  206   a - c  ( FIG. 2 ) for the nodes  204   a - c  ( FIG. 2 ) within the cluster  200  ( FIG. 2 ) may be set to be less than the amount of available physical memory in the node (e.g.,  204   c  ( FIG. 2 )) having the least amount of available physical memory. Thus, all nodes (e.g.,  204   a - c  ( FIG. 2 )) may have additional memory that may be partitioned as locally tuned memory (e.g.,  208   a - b  ( FIG. 2 )). 
         [0042]    Then, at  306 , the memory distribution for the nodes  204   a - c  ( FIG. 2 ) in the cluster  200  ( FIG. 2 ) is determined by the GMT  202  ( FIG. 2 ). According to at least one embodiment, the LBCs  212   a - c  ( FIG. 2 ) may collect cost/benefit information for the node  204   a - c  ( FIG. 2 ) associated with the LBC  212   a - c  ( FIG. 2 ). Thereafter, the collected cost/benefit information may be transmitted to the GMT  202  ( FIG. 2 ). Based on the aggregated cost/benefit information from the LBCs  212   a - c  ( FIG. 2 ) and the least amount of available physical memory determined previously (i.e., at  304 ), the GMT  202  ( FIG. 2 ) may determine a memory distribution for use by the nodes  204   a - c  ( FIG. 2 ). 
         [0043]    Next, at  308 , the determined memory distribution is sent to the nodes  204   a - c  ( FIG. 2 ). According to at least one embodiment, the GMT  202  ( FIG. 2 ) may send the memory distribution data to the other nodes (e.g.,  204   a  and  204   c  ( FIG. 2 )). Thereafter, the other nodes (e.g.,  204   a  and  204   c  ( FIG. 2 )) may apply the memory distribution to the node&#39;s (e.g.,  204   a  and  204   c  ( FIG. 2 )) physical memory. Furthermore, the node (e.g.,  204   b  ( FIG. 2 )) hosting the GMT  202  ( FIG. 2 ) may apply the memory configuration locally. Thus, the globally tuned memory  206   a - c  ( FIG. 2 ) may be the same size (e.g., 16 gigabytes) across all nodes  204   a - c  ( FIG. 2 ) in the cluster  200  ( FIG. 2 ). 
         [0044]    It may be appreciated that  FIGS. 2 and 3  provide only an illustration of one embodiment and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted embodiment(s) may be made based on design and implementation requirements. 
         [0045]      FIG. 4  is a block diagram  900  of internal and external components of computers depicted in  FIG. 1  in accordance with an illustrative embodiment of the present invention. It should be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. 
         [0046]    Data processing system  902 ,  904  is representative of any electronic device capable of executing machine-readable program instructions. Data processing system  902 ,  904  may be representative of a smart phone, a computer system, PDA, or other electronic devices. Examples of computing systems, environments, and/or configurations that may represented by data processing system  902 ,  904  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, and distributed cloud computing environments that include any of the above systems or devices. 
         [0047]    Network server  102   a  ( FIG. 1 ) and network server  102   b  ( FIG. 1 ) may include respective sets of internal components  902   a, b  and external components  904   a, b  illustrated in  FIG. 4 . Each of the sets of internal components  902   a, b  includes one or more processors  906 , one or more computer-readable RAMs  908 , and one or more computer-readable ROMs  910  on one or more buses  912 , and one or more operating systems  914  and one or more computer-readable tangible storage devices  916 . The one or more operating systems  914 , the software program  108   a  ( FIG. 1 ) and the global memory tuner program  110   a  ( FIG. 1 ) in server  102   a  ( FIG. 1 ), and the software program  108   b  ( FIG. 1 ) and the global memory tuner program  110   b  ( FIG. 1 ) in server  102   b  ( FIG. 1 ) may be stored on one or more computer-readable tangible storage devices  916  for execution by one or more processors  906  via one or more RAMs  908  (which typically include cache memory). In the embodiment illustrated in  FIG. 4 , each of the computer-readable tangible storage devices  916  is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices  916  is a semiconductor storage device such as ROM  910 , EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information. 
         [0048]    Each set of internal components  902   a, b  also includes a R/W drive or interface  918  to read from and write to one or more portable computer-readable tangible storage devices  920  such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the software program  108   a  and  108   b  ( FIG. 1 ) and the global memory tuner program  110   a  and  110   b  ( FIG. 1 ) can be stored on one or more of the respective portable computer-readable tangible storage devices  920 , read via the respective R/W drive or interface  918  and loaded into the respective hard drive  916 . 
         [0049]    Each set of internal components  902   a, b  may also include network adapters (or switch port cards) or interfaces  922  such as a TCP/IP adapter cards, wireless wi-fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The software program  108   a  ( FIG. 1 ) and the global memory tuner program  110   a  ( FIG. 1 ) in server  102   a  ( FIG. 1 ), and the software program  108   b  ( FIG. 1 ) and the global memory tuner program  110   b  ( FIG. 1 ) in network server  102   b  ( FIG. 1 ) can be downloaded from an external computer (e.g., server) via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces  922 . From the network adapters (or switch port adaptors) or interfaces  922 , the software program  108   a  ( FIG. 1 ) and the global memory tuner program  110   a  ( FIG. 1 ) in server  102   a  ( FIG. 1 ), and the software program  108   b  ( FIG. 1 ) and the global memory tuner program  110   b  ( FIG. 1 ) in network server  102   b  ( FIG. 1 ) are loaded into the respective hard drive  916 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
         [0050]    Each of the sets of external components  904   a, b  can include a computer display monitor  924 , a keyboard  926 , and a computer mouse  928 . External components  904   a, b  can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components  902   a, b  also includes device drivers  930  to interface to computer display monitor  924 , keyboard  926 , and computer mouse  928 . The device drivers  930 , R/W drive or interface  918 , and network adapter or interface  922  comprise hardware and software (stored in storage device  916  and/or ROM  910 ). 
         [0051]    It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
         [0052]    Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
         [0053]    Characteristics are as follows: 
         [0054]    On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
         [0055]    Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
         [0056]    Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
         [0057]    Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
         [0058]    Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
         [0059]    Service Models are as follows: 
         [0060]    Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
         [0061]    Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
         [0062]    Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
         [0063]    Deployment Models are as follows: 
         [0064]    Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
         [0065]    Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
         [0066]    Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
         [0067]    Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
         [0068]    A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
         [0069]    Referring now to  FIG. 5 , illustrative cloud computing environment  1000  is depicted. As shown, cloud computing environment  1000  comprises one or more cloud computing nodes  100  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  1000 A, desktop computer  1000 B, laptop computer  1000 C, and/or automobile computer system  1000 N may communicate. Nodes  100  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  1000  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  1000 A-N shown in  FIG. 5  are intended to be illustrative only and that computing nodes  100  and cloud computing environment  1000  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
         [0070]    Referring now to  FIG. 6 , a set of functional abstraction layers  1100  provided by cloud computing environment  1000  ( FIG. 5 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 6  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
         [0071]    Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
         [0072]    Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
         [0073]    In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
         [0074]    Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and global memory tuner  96 . A global memory tuner  96  provides a way to tune the physical memory for each node in a cluster based on the node having the least amount of available physical memory. Thus, memory may be allocated consistently across the nodes of the cluster, thereby improving query stability. 
         [0075]    The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.