Patent Abstract:
A resource management system for a virtual machine computing environment includes a software component that optimizes capacity between server clusters or groups by monitoring the capacity of server clusters or groups and automatically adding and removing host systems to and from server clusters or groups. The software component may be implemented at a server cluster management level to monitor and execute host system moves between server clusters and/or at a higher level in the resource management hierarchy. At the higher level, the software component is configured to monitor and execute host system moves between sets of server clusters being managed by different server cluster management agents.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This patent application is a continuation of U.S. application Ser. No. 12/699,631, filed on Feb. 3, 2010, which is incorporated herein by reference. 
     
    
     BACKGROUND 
       [0002]    Computer virtualization is a technique that involves encapsulating a physical computing machine platform into a virtual machine that is executed under the control of virtualization software on a hardware computing platform. Virtualization software enables multiple virtual machines to be run on a single hardware computing platform, and can manage the allocation of computing resources to each virtual machine. 
         [0003]    A set of hardware computing platforms can be organized as a server cluster to provide computing resources for example, for a data center. In addition, supporting technology can move running virtual machines between servers (also referred to herein as “host systems”) in the cluster; an example of this supporting technology is sold as VMware VMotion™ by VMware, Inc. of Palo Alto, Calif. In addition, server cluster virtualization management software that incorporates cluster resource management technology can determine initial and ongoing locations of virtual machines on hardware computing platforms in the server cluster, and can manage the allocation of cluster computing resources. An example of this server cluster virtualization management software is sold as VMware Distributed Resource Scheduler™ by VMware, Inc. of Palo Alto, Calif. (hereinafter referred to as “DRS”). In addition, the server cluster virtualization management software can request that a server in the cluster power itself down, and can use mechanisms available in the marketplace to remotely power-on a server that is powered down. An example of this power management software is sold as the VMware Distributed Power Management feature within DRS by VMware, Inc. of Palo Alto, Calif. (hereinafter referred to as “DPM”). 
         [0004]    Current implementations of DRS limit the cluster size to a certain number (N) of servers. As a consequence, resource management has to be carried out in groups of N servers or less. For data centers that operate considerably more than N servers and data centers that operate multiple groups of servers where each group is dedicated to a different customer or has a particular server configuration, DRS cannot ensure optimized resource management. Although resource usage within any single group of servers may be balanced using DRS, adding capacity to an overloaded group of servers cannot be easily done. 
       SUMMARY 
       [0005]    One or more embodiments of the present invention provide a system and a method for automatically optimizing capacity between server clusters or groups that support a virtual machine computing environment. Such a system and method enable the balancing of resources across server clusters or groups and provides inter-cluster or inter-group resource sharing without compromising the isolation aspect of a server cluster or a server group. 
         [0006]    According to this system and method, a software component monitors the capacity of server clusters or groups and automatically adds and removes host systems to and from server clusters or groups. The software component may be implemented at a server cluster management level to monitor and execute host system moves between server clusters and/or at a higher level in the resource management hierarchy. At the higher level, the software component is configured to monitor and execute host system moves between sets of server clusters being managed by different server cluster management agents. 
         [0007]    A method of allocating physical computing resources in a virtual machine computing environment, according to an embodiment of the present invention, includes the steps of computing a usage metric of a multiple groups of server computers, determining a load imbalance between the groups, evacuating a host system in an under-utilized group, and allocating the evacuated host system to an over-utilized group. The host system move from the under-utilized group to the over-utilized group is carried out when the overall utilization is high enough. In situations where overall utilization is low, the host system move is not carried out although load imbalance has been determined. 
         [0008]    A method of allocating physical computing resources in a virtual machine computing environment, according to another embodiment of the present invention, includes the steps of computing a usage metric of a group of server computers, determining a load imbalance for the group, and allocating an additional server computer to the group if the group is overloaded and deallocating one of the server computers of the group if the group is underloaded. 
         [0009]    A hierarchical resource management system according to an embodiment of the present invention includes a plurality of first level resource managers, each configured to monitor a load imbalance across two or more clusters of server computers, and a second level resource manager configured to monitor a load imbalance between groups of server computers, where each group is monitored by one of the first level resource managers. 
         [0010]    Other embodiments of the present invention include, without limitation, a computer-readable storage medium that includes instructions that enable a processing unit to implement one or more aspects of the disclosed methods as well as a system configured to implement one or more aspects of the disclosed methods. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a schematic diagram of a hierarchical resource management system according to an embodiment of the present invention; 
           [0012]      FIG. 2  illustrates the components of the hierarchical resource management system of  FIG. 1  in additional detail; 
           [0013]      FIG. 3  is a block diagram representing an example of a host system included in a cluster of servers shown in  FIG. 1 ; 
           [0014]      FIG. 4  illustrates the virtual cloud resource manager of  FIG. 1  in additional detail; 
           [0015]      FIGS. 5A and 5B  conceptually illustrate the process of moving a host system from one server cluster to another server cluster; 
           [0016]      FIG. 6  conceptually illustrates the process of evacuating a host system prior to moving the host system to a repository or another server cluster; 
           [0017]      FIG. 7  is a flow diagram that depicts the steps carried out to balance resource usage between server clusters or server groups; and 
           [0018]      FIG. 8  is a flow diagram that depicts the steps carried out to allocate or deallocate a host system. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]      FIG. 1  is a schematic diagram of a hierarchical resource management system  10  according to an embodiment of the present invention. Two levels of resource management are shown in  FIG. 1  for simplicity. An embodiment of the present invention may be practiced with additional (higher) levels, e.g., a third level. A third level resource manager would operate similarly to a second level resource manager except that the third level resource manager would collect statistics data from and recommend host system moves to the second level resource managers (one of which is shown in  FIG. 1  as cloud resource manager  40 ). At the first level, cluster managers  20 ,  30  are managing resources for their respective server clusters. At the second level, virtual cloud resource manager  40  is managing resources for server clusters managed by cluster managers  20 ,  30 . Resources being managed by system  10  are physical resources, namely server computers (or host systems) contained in server clusters  21 ,  22 ,  31 ,  32  and server repositories  23 ,  33 . Server clusters  21 ,  22  and server repository  23  are under the control of cluster manager  20  and server clusters  31 ,  32  and server repository  33  are under the control of cluster manager  30 . Cluster manager  20  and cluster manager  30  are server computers each programmed to manage its respective server clusters and server repository in the manner described herein. 
         [0020]    The components of cluster manager  20  are detailed in  FIG. 2 . Cluster manager  30  includes the same components except they are used to manage server clusters  31 ,  32  and server repository  33 . The components of cluster manager  20  include a server cluster virtualization management software  201  which comprises a user interface  202 , DRS module  203 , and a DPM module  204 . An inter-cluster capacity manager  205  is implemented as an extension to server cluster virtualization management software  201  and communicates with server cluster virtualization management software  201  using Application Programming Interface (API) calls. Inter-cluster capacity manager  205  has three modules. The first module is a capacity monitor which collects at periodic intervals resource usage statistics. In one embodiment, resource usage statistics that are collected include the total idle capacity of each cluster managed by cluster manager  20 . In another embodiment, resource usage statistics that are collected include entitlement data for each running virtual machine (VM). Entitlement data for a VM at a point in time signifies the amount of resources the VM is entitled to at that point in time. The resource usage statistics may be supplied by a software component inside server cluster virtualization management software  201  that is separate from DRS module  203  or they may be supplied by DRS module  203 . The second module is a policy engine which stores information (policy) on how resources are to be allocated to each server cluster. The third module is a capacity balancer which receives inputs from the capacity monitor and the policy engine and makes decisions on host system moves accordingly. In one embodiment, the capacity balancer makes host system move decisions less frequently than entitlement data collection, because it may be desirable to analyze capacity trends prior to making a move decision. In either case, the frequency of data collection and the frequency of host system move decisions are configurable parameters. In another embodiment, as will be further described below, the capacity balancer makes host system move decisions by comparing a statistical measure of variance between normalized entitlements of server clusters. 
         [0021]    In the embodiment described above, inter-cluster capacity manager  205  is shown as an extension of server cluster virtualization management software  201 . In alternative embodiments, inter-cluster capacity manager  205  may be a stand-alone software component that periodically polls each of the clusters for resource usage statistics or a software component inside server cluster virtualization management software  201  that periodically polls each of the clusters for resource usage statistics. 
         [0022]      FIG. 2  further illustrates a representative structure for a server cluster. In the illustration, the components of server cluster  21  are shown but it should be understood that server clusters  22 ,  31 ,  32  have substantially the same structure, although the number of host systems can differ. Server cluster  21  includes a plurality of host systems  211 - 218  that are grouped or clustered together (physically or logically). Eight host systems  211 - 218  are shown here; however, in practice, server cluster  21  may include an arbitrary number of host systems. 
         [0023]    A server repository  23  or  33  is a logical group of host systems that are made available for any of the server clusters to utilize. Some are powered off to preserve power consumption. Others are left powered on and booted for quick deployment. 
         [0024]      FIG. 3  is a block diagram of a host system  300  in which one or more VMs are running and is representative of a host system in any of the server clusters. Host system  300  is the physical platform for one or more VMs (e.g., VM  321 , VM  322 , and VM  323 ) and has conventional hardware resources of a computing device, such as one or more CPUs  351 , system memory  352 , disk interface  353 , and network interface  354 . Examples of disk interface  353  are a host bus adapter and a network file system interface. An example of network interface  354  is a network adapter. The VMs run on top of a hypervisor (or virtual machine monitor)  340 , which is a software interface layer that enables sharing of the hardware resources of host system  300 . Persistent data storage is served by a storage device (not shown) connected via disk interface  353 . 
         [0025]      FIG. 4  illustrates the virtual cloud resource manager of  FIG. 1  in additional detail. The components of virtual cloud resource manager  40  include a software component referred to herein as a cloud capacity manager  405 . Cloud capacity manager  405  has three modules. The first module is a capacity monitor which collects resource usage statistics from cluster managers  20 ,  30 . The second module is a policy engine which stores information (policy) on how resources are to be allocated to each set of clusters managed by cluster managers  20 ,  30 . The third module is a capacity balancer which receives inputs from the capacity monitor and the policy engine, and makes decisions on host system moves accordingly. 
         [0026]    A server repository  43  is a logical group of host systems that are made available by virtual cloud resource manager  40  for either cluster manager  20 ,  30  to allocate. Some are powered off to preserve power consumption. Others are left powered on and booted for quick deployment. 
         [0027]      FIG. 5A  conceptually illustrates the process of moving a host system directly from one server group to another server group. The host system that is moved is initially contained in server cluster  22 . When cluster manager  20  determines through inter-cluster capacity manager  205  that server cluster  22  is underutilized and that server cluster  21  is overutilized, it deallocates a host system within server cluster  22  by evacuating the VMs running therein and making the host system available for server cluster  21 . In the example shown, host system  227  is selected for deallocation and is made available for allocation by server cluster  21 . The selection of host system  227  among all host systems running in server cluster  22  may be determined through any heuristic, e.g., host system with the smallest total entitlement. More complicated heuristics, e.g., the heuristic implemented in DPM  204  to select the host system to power down, may also be used. 
         [0028]      FIG. 5B  conceptually illustrates the process of moving a host system from one server group to another server group via a server repository  23 . The host system that is moved into server repository  23  is initially contained in server cluster  22 . When cluster manager  20  determines through inter-cluster capacity manager  205  that server cluster  22  is underutilized, it deallocates a host system within server cluster  22  by evacuating the VMs running therein and making the host system available for server cluster  21 . In the example shown, host system  227  is selected for deallocation and is made available for allocation by another server cluster by logically placing host system  227  in server repository  23 . The selection of host system  227  is made in the manner previously described. 
         [0029]    Then, at a later time, when cluster manager  20  determines through inter-cluster capacity manager  205  that server cluster  21  is overutilized, it allocates a host system from server repository  23  (e.g., host system  227 ) to server cluster  21 .  FIG. 5B  also shows host systems  231 ,  232  within server repository  23 . Cluster manager  20  may also allocate host system  231  or  232  to server cluster  21 , as needed. In addition, if server cluster  22  becomes overutilized, cluster manager  20  may also allocate host system  231  or  232  to server cluster  22 , as needed. For quicker deployment, any of host systems  231 ,  232 ,  227  in server repository  23  may be kept in a powered-on state. If power conservation is of higher priority, one or more of host systems in server repository  23  may be powered off. In certain instances, e.g., in situations where no cluster seems to be close to needing additional resources, host systems in server repository  23  may be powered off in a more aggressive manner. There also may be situations where a set number of host systems in server repository  23 , the set number being configurable, are kept powered on and the rest are powered off. In all of these different scenarios, the powered-off host systems need to be remotely powered on and booted for deployment. 
         [0030]      FIG. 6  conceptually illustrates the process of evacuating a host system prior to moving the host system to a repository or to another server group. In this example, host system  227  has been selected for deallocation. Two VMs are shown running in host system  227  and thus they need to be moved to other host systems within server cluster  22 . As shown, one VM is moved to host system  224  and the other VM is moved to host system  226 . In one embodiment, the selection of destination host systems is made by DRS and the VMs are moved using VMware&#39;s VMotion™ technology. After evacuation, host system  227  is allocated to server cluster  21  in the embodiment of  FIG. 5A  or logically placed into server repository  23  for subsequent allocation in the embodiment of  FIG. 5B . 
         [0031]      FIG. 7  is a flow diagram that depicts the steps carried out to balance resource usage between server clusters or server groups. This method may be carried out by a cluster manager through its inter-cluster capacity manager or by a virtual cloud resource manager through its cloud capacity manager. In step  710 , resource usage statistics (in particular, entitlement data) are collected at regular intervals of time for each running VM. The entitlement data can be obtained using an API call into DRS. Entitlement data for a VM at a point in time signify the amount of resources the VM is entitled to at that point in time. Therefore, the total entitlement for all VMs running in a server cluster or in any server group signifies the amount of resources that are entitled to the VMs running in that server cluster or server group. In step  712 , a normalized entitlement for each server cluster or server group is computed by dividing the total entitlement by a number representing the total processing power and memory capacity of the server cluster or server group. 
         [0032]    The equations for computing the normalized entitlement for a group of server clusters managed by a cluster manager (also referred to as Virtual Center or VC, for short), and at the cloud level are provided below. In the equations below, E VM  is the entitlement value for a VM, E C   DRS  entitlement for a cluster C as calculated by DRS, Ë C   DRS  entitlement for a cluster C as adjusted for statistical smoothing, E VC   VC  is the total entitlement for a group of clusters managed by one cluster manager VC as calculated at the VC layer and Ë VC   VC  its statistically adjusted value. NE C  is the normalized entitlement for a server cluster C. NE VC  is the normalized entitlement for a group of server clusters managed by a cluster manager VC. 
         [0000]      E C   DRS =ΣE VM  
 
         [0000]    (summation is done over all of the VMs in the cluster C) 
         [0000]        Ë   C   DRS =mean (recent values of  E   C   DRS )+two times the standard deviation from this mean 
         [0000]      E VC   VC =ΣË C   DRS  
 
         [0000]    (summation is done over all of the clusters C managed as a group by a VC) 
         [0000]        Ë   VC   VC =mean (recent values of  E   VC   VC )+two times the standard deviation from this mean 
         [0000]        NE   C   =Ë   C   DRS /total resource capacity of server cluster  C    
         [0000]        NE   VC   =Ë   VC   VC /total resource capacity of a group of server clusters managed by  VC    
         [0033]    In the equations above, the entitlement value represents either processing power or memory capacity, and the normalized entitlement is calculated separately for each resource. 
         [0034]    In step  714 , the normalized entitlements of two server clusters or server groups are compared to determine imbalance. In one example, the normalized entitlement of server cluster  21  is compared with the normalized entitlement of server cluster  22  to determine if there is any imbalance between these two server clusters. In another example, the normalized entitlement of a first server group containing host systems in server clusters  21 ,  22  is compared with the normalized entitlement of a second server group containing host systems in server clusters  31 ,  32 . If there is no imbalance, i.e., the difference between the two normalized entitlements is less than a predetermined threshold, the flow returns to step  710 . If there is an imbalance, i.e., the difference between the two normalized entitlements is greater than a predetermined threshold, step  716  is executed. In step  716 , a host system from the server cluster or server group with the lower normalized entitlement is evacuated and allocated to the server cluster or server group with the higher normalized entitlement. The movement of the host system can be carried out by making API calls into server cluster virtualization management software  201  to move the host system out of one server cluster and into another server cluster. 
         [0035]    When determining imbalance, processing power imbalance may be evaluated, or memory capacity imbalance may be evaluated, or an overall imbalance may be evaluated. The overall imbalance is a weighted combination of the imbalance on each resource. The weight value for each is configurable and defaults to 0.25 for processing power and 0.75 for memory capacity. 
         [0036]    In one embodiment, the decision block in step  714  is carried out with less frequency than steps  710  and  712 . Consequently, the decision on whether there is an imbalance is made by comparing the running averages of the normalized entitlements. 
         [0037]    In one embodiment, the decision block in step  714  is carried out by comparing a statistical measure of variance between the normalized entitlements of server clusters or server groups. In one example, the variance (e.g., standard deviation) of normalized entitlements of server cluster  21  and server cluster  22  is calculated. If the variance (e.g., standard deviation) is above a user specified threshold, a host system is evacuated from the server cluster with the lower normalized entitlement and allocated to the server repository or a server cluster with the higher normalized entitlement. After such a move, the variance (e.g., standard deviation) is computed again and the process is repeated until no further moves are possible or the variance is below the threshold. In a similar manner, variance can be used to determine the imbalance between server groups. 
         [0038]    A systematic search can be carried out to find a spare host system for one or more overloaded clusters. First, the server repository is examined and the spare host system is allocated from the server repository, if one is available. If not, the underloaded clusters and clusters in equilibrium are sorted in ascending order of normalized entitlement, and beginning from the top, look for host systems that have been powered down, and if none, select a host system from the most underloaded cluster. 
         [0039]    In some embodiments of the present invention, the host system move from the under-utilized group to the over-utilized group may not be always carried out although the load imbalance is sufficiently high. In situations where overall utilization is low, e.g., the maximum normalized entitlement of the server clusters or groups is less than a predefined threshold, the host system move is not carried out although load imbalance is sufficiently high. 
         [0040]      FIG. 8  is a flow diagram that depicts the steps carried out to allocate or deallocate a host system. This method may be carried out by a cluster manager through its inter-cluster capacity manager or by a virtual cloud resource manager through its cloud capacity manager. In this example, the host system is allocated from a server repository and configured with a default host profile, and, after deallocation, the host system is logically placed into a server repository. In step  810 , resource usage statistics (in particular, entitlement data) are collected at regular intervals of time for each running VM. The entitlement data can be obtained using an API call into DRS. Entitlement data for a VM at a point in time signify the amount of resources the VM is entitled to at that point in time. Therefore, the total entitlement for all VMs running in a server cluster or in any server group signifies the amount of resources that are entitled to the VMs running in that server cluster or server group. In step  812 , a normalized entitlement for the server cluster or server group is computed by dividing the total entitlement by a number representing the total processing power and memory capacity of the server cluster or server group. In step  814 , the normalized entitlement is compared to a predetermined upper threshold value. If the normalized entitlement is not greater than the upper threshold value, the normalized entitlement is compared to a predetermined lower threshold value. If the normalized entitlement is not less than the lower threshold value, the flow returns to step  810 . 
         [0041]    On the other hand, if the normalized entitlement is greater than the upper threshold value, steps  816  and  818  are carried out, or if the normalized entitlement is less than the lower threshold value, steps  822  and  824  are carried out. In step  816 , a host system is allocated to the server cluster or server group from the server repository. Then, in step  818 , the host system is configured according to a default host profile of the server cluster to which it was added and DRS performs balancing of the workloads within that server cluster. In step  822 , a host system is selected from the server cluster or server group according to heuristics previously discussed and evacuated. Then, in step  824 , the evacuated host system is logically placed in the server repository. The movement of the host system can be carried out by making API calls into server cluster virtualization management software  201  to move the host system out of a server cluster or server repository and into a server cluster or server repository. 
         [0042]    In one embodiment, the decision blocks in steps  814  and  820  are carried out with less frequency than steps  810  and  812 . Consequently, the decision on whether there is an imbalance is made by comparing the running averages of the normalized entitlements against the thresholds. In alternative embodiments, if a sudden rise in normalized entitlement is detected, the decision block in step  814  may be executed earlier than its scheduled time so that the sudden rise in processing and/or memory demands can be met in a timely manner. 
         [0043]    In addition, various policies for resource management may be specified. For example, a default low limit and a default high limit may be defined for all server clusters. For some server clusters, these limits may be overridden with custom values. When the percentage of spare capacity is below the low limit, then a host system is added to the server cluster. When the percentage of spare capacity is above the high limit, then a host system is removed from the server cluster. If the low limit is 0% and the high limit is 100% for a server cluster, that server cluster will not be monitored. 
         [0044]    The various embodiments described herein may employ various computer-implemented operations involving data stored in computer systems. For example, these operations may require physical manipulation of physical quantities usually, though not necessarily, these quantities may take the form of electrical or magnetic signals where they, or representations of them, are capable of being stored, transferred, combined, compared, or otherwise manipulated. Further, such manipulations are often referred to in terms, such as producing, identifying, determining, or comparing. Any operations described herein that form part of one or more embodiments of the invention may be useful machine operations. In addition, one or more embodiments of the invention also relate to a device or an apparatus for performing these operations. The apparatus may be specially constructed for specific required purposes, or it may be a general purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general purpose machines may be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations. 
         [0045]    The various embodiments described herein may be practiced with other computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. 
         [0046]    One or more embodiments of the present invention may be implemented as one or more computer programs or as one or more computer program modules embodied in one or more computer readable media. The term computer readable medium refers to any data storage device that can store data which can thereafter be input to a computer system. Computer readable media may be based on any existing or subsequently developed technology for embodying computer programs in a manner that enables them to be read by a computer. Examples of a computer readable medium include a hard drive, network attached storage (NAS), read-only memory, random-access memory (e.g., a flash memory device), a CD (Compact Discs), such as CD-ROM, a CD-R, or a CD-RW, a DVD (Digital Versatile Disc), a magnetic tape, and other optical and non-optical data storage devices. The computer readable medium can also be distributed over a network coupled computer system so that the computer readable code is stored and executed in a distributed fashion. 
         [0047]    Although one or more embodiments of the present invention have been described in some detail for clarity of understanding, it will be apparent that certain changes and modifications may be made within the scope of the claims. Accordingly, the described embodiments are to be considered as illustrative and not restrictive, and the scope of the claims is not to be limited to details given herein, but may be modified within the scope and equivalents of the claims. In the claims, elements and/or steps do not imply any particular order of operation, unless explicitly stated in the claims. 
         [0048]    In addition, while described virtualization methods have generally assumed that virtual machines present interfaces consistent with a particular hardware system, persons of ordinary skill in the art will recognize that the methods described may be used in conjunction with virtualizations that do not correspond directly to any particular hardware system. Virtualization systems in accordance with the various embodiments, implemented as hosted embodiments, non-hosted embodiments, or as embodiments that tend to blur distinctions between the two, are all envisioned. Furthermore, various virtualization operations may be wholly or partially implemented in hardware. For example, a hardware implementation may employ a look-up table for modification of storage access requests to secure non-disk data. 
         [0049]    Many variations, modifications, additions, and improvements are possible, regardless the degree of virtualization. The virtualization software can therefore include components of a host, console, or guest operating system that performs virtualization functions. Plural instances may be provided for components, operations or structures described herein as a single instance. Finally, boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of the invention(s). In general, structures and functionality presented as separate components in exemplary configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements may fall within the scope of the appended claims(s).

Technology Classification (CPC): 7