Patent Grant
9286104
The present invention relates generally to cloud computing, and more particularly to selecting virtual machines to be relocated, such as to balance cloud computing resources, based on memory volatility.
In a cloud computing environment, computing is delivered as a service rather than a product, whereby shared resources, software and information are provided to computers and other devices as a metered service over a network, such as the Internet. In such an environment, computation, software, data access and storage services are provided to users that do not require knowledge of the physical location and configuration of the system that delivers the services.
In a virtualized computer environment, such as may be implemented in a physical cloud computing node of the cloud computing environment, the virtualized computer environment includes a virtual operating system. The virtual operating system includes a common base portion and separate user portions that all run on a physical computer. The physical computer is referred to as a host. The common base portion may be referred to as a hypervisor in which one or more virtual machines in the user portions are run by the hypervisor.
Virtual machines that are running on the physical computer (also referred to as “physical machine,” “compute machine” or “compute node”) of the physical cloud computing node may need to be migrated or relocated to another physical compute machine in the same or different cloud computing node for various reasons, such as to balance the cloud computing resources or to perform a maintenance operation. For example, a compute machine may experience an increase in workload demand thereby requiring additional virtual machines to handle the increase in workload demand. Such virtual machines may be migrated from compute machine(s) that are experiencing a low workload demand. In another example, virtual machines may need to be migrated to another compute machine if a maintenance operation needs to be performed on the compute machine that is hosting the virtual machines.
In the process of migrating or relocating virtual machines from one physical compute machine to another compute machine, the contents of the virtual machine's physical memory as well as the virtual machine's disk drive utilized by the migrating virtual machine needs to be copied. Furthermore, the network address assigned to the migrated virtual machine needs to be reassigned. As a result, in the process of migrating or relocating virtual machines from one physical compute machine to another compute machine, a “shell virtual machine” is created with the same attributes as the migrating virtual machine. Existing disks utilized by the migrating virtual machine are mapped to both the hypervisor in the virtual machine's current compute machine and the hypervisor in the target compute machine. The contents of the physical memory utilized by the migrating virtual machine are copied to the target hypervisor using an iterative approach to recopy memory contents that have changed during the copy operation. Such an iterative approach to recopy memory contents that have changed during the copy operation may result in a longer duration of time in migrating virtual machines. Additionally, the network address of the migrating virtual machine is switched to the target compute machine. The source migrating virtual machine is then stopped and deleted.
However, there is not currently a means for selecting the optimal virtual machine to be migrated from one physical compute machine to another compute machine to lessen the duration of time in migrating virtual machines that takes into consideration the iterative approach to recopy memory contents that have changed during the copy operation.
In one embodiment of the present invention, a method for selecting virtual machines to be migrated comprises monitoring page consumption by each virtual machine running on a physical machine as a function of time, where the page consumption comprises an amount of virtual memory accessed. The method further comprises recording the page consumption by each virtual machine running on the physical machine at sample points in time within an observation window of time. The method additionally comprises computing a gradient of page consumption for each virtual machine running on the physical machine at each sample interval within the observation window of time, where each sample interval is a time duration between two consecutive sample points in time within the observation window of time. Furthermore, the method comprises identifying those virtual machines with a positive computed gradient of page consumption that is less than a threshold to be placed in a list of virtual machines to be ranked. Additionally, the method comprises computing a relative page consumption value for each virtual machine in the list of virtual machines to be ranked for each sample interval within the observation window of time, where the relative page consumption value indicates a change in a rate of the page consumption. In addition, the method comprises ranking the virtual machines in the list of virtual machines based on an increasing order of the relative page consumption value at each sample interval within the observation window of time. The method further comprises computing, by a processor, a final rank for each virtual machine in the list of virtual machines based on averaging its ranking across each sample interval within the observation window of time. The method additionally comprises selecting one or more virtual machines to be migrated from the list of virtual machines that have a lowest final ranking.
Other forms of the embodiment of the method described above are in a system and in a computer program product.
The foregoing has outlined rather generally the features and technical advantages of one or more embodiments of the present invention in order that the detailed description of the present invention that follows may be better understood. Additional features and advantages of the present invention will be described hereinafter which may form the subject of the claims of the present invention.
A better understanding of the present invention can be obtained when the following detailed description is considered in conjunction with the following drawings, in which:
The present invention comprises a method, system and computer program product for selecting virtual machines to be migrated. In one embodiment of the present invention, the page consumption (referring to the amount of virtual memory accessed) is monitored and recorded for each virtual machine running on a physical machine (also referred to as a “compute node”) in a cloud computing node. The gradient of page consumption for each virtual machine running on the physical machine is computed at each sample interval within an observation window of time, where each sample interval is a time duration between two consecutive sample points in time within the observation window of time. The gradient of page consumption refers to a vector-valued function that indicates the change in virtual memory accessed over the sample interval. A positive gradient of page consumption indicates an increase in page consumption. That is, a positive gradient of page consumption indicates an increase in virtual memory access. It may be opportune to select those virtual machines with the lowest memory volatility to be migrated since the contents of the physical memory utilized by the migrating virtual machine will be copied fewer iterative times during the copy operation due to having fewer changes in the memory contents during the copy operation. As a result, those virtual machines with a positive gradient of page consumption that is less than a threshold (percentage of sample intervals within the observation window of time that have a positive gradient of page consumption) are placed in a list of virtual machines to be ranked. A relative page consumption value for each virtual machine in the list of virtual machines to be ranked is computed for each sample interval within the observation window of time. The relative page consumption value indicates a change in the rate of page consumption. A higher relative page consumption value indicates a higher rate of change in the page consumption (i.e., a higher rate of memory volatility). Such an indication is one way of quantifying memory volatility for the virtual machines. The virtual machines in the list of virtual machines are ranked based on an increasing order of the relative page consumption value at each sample interval within the observation window of time. A higher ranked virtual machine has a higher relative page consumption value than a lower ranked virtual machine. A final rank for each virtual machine in the list of virtual machines to be ranked is computed based on averaging its ranking across each sample interval within the observation window of time. One or more virtual machines are then selected to be migrated to another physical machine that have the lowest final ranking (i.e., with the lowest relative page consumption values). Since, as discussed above, it may be opportune to select those virtual machines with the lowest memory volatility to be migrated, those virtual machine(s) with the lowest relative page consumption values, which indicate a rate of change in the rate of page consumption, are selected to be migrated. In this manner, the virtual machine(s) with the lowest memory volatility are selected to be migrated since the contents of the physical memory utilized by such virtual machines will be copied fewer iterative times during the copy operation thereby reducing the time duration in migrating the virtual machine(s) to a different physical machine.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without such specific details. In other instances, well-known circuits have been shown in block diagram form in order not to obscure the present invention in unnecessary detail. For the most part, details considering timing considerations and the like have been omitted inasmuch as such details are not necessary to obtain a complete understanding of the present invention and are within the skills of persons of ordinary skill in the relevant art.
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, the embodiments of the present invention are capable of being implemented in conjunction with any type of clustered computing environment now known or later developed.
In any event, the following definitions have been derived from the “The NIST Definition of Cloud Computing” by Peter Mell and Timothy Grance, dated September 2011, which is cited on an Information Disclosure Statement filed herewith, and a copy of which is provided to the U.S. Patent and Trademark Office.
Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models.
Characteristics are as follows:
On-Demand Self-Service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed, automatically without requiring human interaction with each service's provider.
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, tablets, laptops and workstations).
Resource Pooling: The provider'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 consumer 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 data center). Examples of resources include storage, processing, memory and network bandwidth.
Rapid Elasticity: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
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.
Service Models are as follows:
Software as a Service (SaaS): The capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through either a thin client interface, such as a web browser (e.g., web-based e-mail) or a program interface. 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.
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, libraries, services 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 configuration settings for the application-hosting environment.
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 and deployed applications; and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private Cloud: The cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units). It may be owned, managed and operated by the organization, a third party or some combination of them, and it may exist on or off premises.
Community Cloud: The cloud infrastructure is provisioned for exclusive use by a specific community of consumers from organizations that have shared concerns (e.g., mission, security requirements, policy and compliance considerations). It may be owned, managed and operated by one or more of the organizations in the community, a third party, or some combination of them, and it may exist on or off premises.
Public Cloud: The cloud infrastructure is provisioned for open use by the general public. It may be owned, managed and operated by a business, academic or government organization, or some combination of them. It exists on the premises of the cloud provider.
Hybrid Cloud: The cloud infrastructure is a composition of two or more distinct cloud infrastructures (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).
Referring now to the Figures in detail,
Network 103 may be, for example, a local area network, a wide area network, a wireless wide area network, a circuit-switched telephone network, a Global System for Mobile Communications (GSM) network, Wireless Application Protocol (WAP) network, a WiFi network, an IEEE 802.11 standards network, various combinations thereof, etc. Other networks, whose descriptions are omitted here for brevity, may also be used in conjunction with system 100 of
Cloud computing environment 102 is used to deliver computing as a service to client device 101 implementing the model discussed above. An embodiment of cloud computing environment 102 is discussed below in connection with
Referring now to
As shown in
As further shown in
Referring now to
Virtual operating system 401 further includes one or more virtual machines 406A-406C (identified as “VM 1,” “VM 2” and “VM 3,” respectively, in
As discussed above, virtual operating system 401 and its components execute on physical or real computer 402. These software components may be loaded into memory 404 for execution by processor 403.
Each compute node 302 may include any number of virtual machines 406, hypervisors 407, etc. Furthermore, the virtualization environment for compute node 302 is not to be limited in scope to the elements depicted in
Referring now to
As illustrated in
Furthermore, the software components of management software 304 include a candidate pool selector 502 configured to compute a gradient of page consumption for each virtual machine 406 running on compute node 302 and identify those virtual machines 406 with a positive gradient of page consumption that is less than a threshold (e.g., percentage of sample intervals within an observation window of time that have a positive gradient of page consumption) to be placed in a list of virtual machines to be ranked. The gradient of page consumption refers to a vector-valued function that indicates the change in virtual memory accessed over a sample interval. A positive gradient of page consumption indicates an increase in page consumption. That is, a positive gradient of page consumption indicates an increase in virtual memory access. As will be discussed further below, it may be opportune to select those virtual machines 406 with the lowest memory volatility to be migrated since the contents of the physical memory (e.g., memory 404) utilized by the migrating virtual machine 406 will be copied fewer iterative times during the copy operation due to having fewer changes in the memory contents during the copy operation. As a result, those virtual machines 406 with a positive gradient of page consumption that exceeds a threshold period of time (e.g., 80% of the sample intervals) will be excluded from being in the list of possible virtual machines 406 to be migrated. Additional details regarding the functionality of candidate pool selector 502 is provided below in connection with
Additionally, the software components of management software 304 include a ranking engine 503 configured to compute a relative page consumption value for each virtual machine 406 in the list of virtual machines to be ranked for each sample interval within the observation window of time, where the relative page consumption value indicates a change in a rate of the page consumption. A higher relative page consumption value indicates a higher rate of change in the page consumption (i.e., a higher rate of memory volatility). Such an indication is one way of quantifying memory volatility for virtual machines 406. Ranking engine 503 ranks virtual machines 406 in the list of virtual machines at each sample interval within the observation window of time and then computes a final rank for each virtual machine 406 in the list of virtual machines based on averaging its ranking across each sample interval within the observation window of time. One or more virtual machines 406 may then be selected from the list of virtual machines with the lowest final ranking Since, as discussed above, it may be opportune to select those virtual machines 406 with the lowest memory volatility to be migrated, those virtual machine(s) 406 with the lowest relative page consumption values, which indicate a rate of change in the rate of page consumption, are selected to be migrated. Additional details regarding the functionality of ranking engine 503 is provided below in connection with
Referring now to
Referring again to
Administrative server 303 may further include a communications adapter 609 coupled to bus 602. Communications adapter 609 interconnects bus 602 with an outside network (e.g., network 103 of
The present invention may be a system, a method, and/or a computer program product. 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.
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.
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.
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, 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 conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user'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'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.
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.
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.
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.
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 block 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.
As stated in the Background section, virtual machines that are running on the physical computer (also referred to as “physical machine,” “compute machine” or “compute node”) of the physical cloud computing node may need to be migrated or relocated to another physical compute machine in the same or different cloud computing node for various reasons, such as to balance the cloud computing resources or to perform a maintenance operation. In the process of migrating or relocating virtual machines from one physical compute machine to another compute machine, the contents of the virtual machine's physical memory as well as the virtual machine's disk drive utilized by the migrating virtual machine needs to be copied. Furthermore, the network address assigned to the migrated virtual machine needs to be reassigned. As a result, in the process of migrating or relocating virtual machines from one physical compute machine to another compute machine, a “shell virtual machine” is created with the same attributes as the migrating virtual machine. Existing disks utilized by the migrating virtual machine are mapped to both the hypervisor in the virtual machine's current compute machine and the hypervisor in the target compute machine. The contents of the physical memory utilized by the migrating virtual machine are copied to the target hypervisor using an iterative approach to recopy memory contents that have changed during the copy operation. Such an iterative approach to recopy memory contents that have changed during the copy operation may result in a longer duration of time in migrating virtual machines. Additionally, the network address of the migrating virtual machine is switched to the target compute machine. The source migrating virtual machine is then stopped and deleted. However, there is not currently a means for selecting the optimal virtual machine to be migrated from one physical compute machine to another compute machine to lessen the duration of time in migrating virtual machines that takes into consideration the iterative approach to recopy memory contents that have changed during the copy operation.
The principles of the present invention provide a means for selecting those virtual machine(s) to be migrated based on memory volatility thereby taking into consideration the duration of time spent in iteratively recopying the memory contents during the copy operation. As will be discussed further below, it may be opportune to select those virtual machines with the lowest memory volatility to be migrated since the contents of the physical memory utilized by the migrating virtual machine will be copied fewer iterative times during the copy operation due to having fewer changes in the memory contents during the copy operation. A discussion of selecting those virtual machines based on memory volatility is provided below in connection with
Referring now to
In step 702, page consumption monitor 501 records the amount of virtual memory accessed (i.e., page consumption) by each virtual machine 406 running on compute node 302 at sample points in time within an observation window of time as illustrated in
Referring to
As illustrated in
Returning to
where P represents a page consumption value, i represents a sample point within observation window of time 803, and t represents a time of a sample point (e.g., t1 of
The gradient of page consumption refers to a vector-valued function that indicates the change in virtual memory accessed over a sample interval. A positive gradient of page consumption indicates an increase in page consumption. That is, a positive gradient of page consumption indicates an increase in virtual memory access. As previously discussed, it may be opportune to select those virtual machines 406 with the lowest memory volatility to be migrated since the contents of the physical memory (e.g., memory 404) utilized by the migrating virtual machine 406 will be copied fewer iterative times during the copy operation due to having fewer changes in the memory contents during the copy operation. As a result, those virtual machines 406 with a positive gradient of page consumption that exceeds a threshold period of time (e.g., 80% of the sample intervals) will be excluded from being in the list of possible virtual machines 406 to be migrated as discussed further below.
In step 704, candidate pool selector 502 identifies those virtual machines 406 running on compute node 302 with a gradient of page consumption that is less than a threshold to be placed in a list of virtual machines to be ranked. Such a threshold may correspond to a percentage (e.g., 80%) of sample intervals within observation window of time 803 that have a positive gradient of page consumption. Since a high percentage of sample intervals within observation window of time 803 that have a positive gradient of page consumption indicates a high number of increases in virtual memory accesses, it may be opportune to select those virtual machines 406 with a low percentage of sample intervals within observation window of time 803 that have a positive gradient of page consumption. As a result, those virtual machines 406 running on compute node 302 with a gradient of page consumption that is less than a threshold are placed in a list of virtual machines to be ranked.
In step 705, ranking engine 503 computes a relative page consumption value for each virtual machine 406 in the list of virtual machines to be ranked for each sample interval within observation window of time 803. The relative page consumption value indicates a change in the rate of page consumption. In one embodiment, the relative page consumption value for each virtual machine 406 running on compute node 302 is calculated using the following equation:
where RPC represents the relative page consumption value, P represents a page consumption value, and i represents a sample point (e.g., t1 of
A higher relative page consumption value indicates a higher rate of change in the page consumption (i.e., a higher rate of memory volatility). Such an indication is one way of quantifying memory volatility for virtual machines 406.
In step 706, ranking engine 503 ranks virtual machines 406 in the list of virtual machines based on an increasing order of the relative page consumption value at each sample interval (e.g., time between t1 and t2 of
In step 707, ranking engine 503 computes a final rank for each virtual machine 406 in the list of virtual machines to be ranked based on averaging its ranking across each sample interval (e.g., time between t1 and t2 of
In step 708, ranking engine 503 selects one or more virtual machines 406 from compute node 302 (e.g., compute node 302A) to be migrated to another compute node 302 (e.g., compute node 302F) that have the lowest final ranking (i.e., with the lowest relative page consumption values). Since, as discussed above, it may be opportune to select those virtual machines 406 with the lowest memory volatility to be migrated, those virtual machine(s) 406 with the lowest relative page consumption values, which indicate a rate of change in the rate of page consumption, are selected to be migrated. In this manner, virtual machine(s) 406 with the lowest memory volatility are selected to be migrated since the contents of the physical memory utilized by such virtual machines 406 will be copied fewer iterative times during the copy operation thereby reducing the time duration in migrating virtual machine(s) 406 to a different compute node 302. In one embodiment, the target compute node 302 (i.e., the compute node 302 receiving the migrated virtual machine 406) resides on the same cloud computing node 201 as the source compute node 302 (i.e., the compute node 302 that is the source of the migrating virtual machine 406). In another embodiment, the target compute node 302 (i.e., the compute node 302 receiving the migrated virtual machine 406) resides on a different cloud computing node 201 as the source compute node 302 (i.e., the compute node 302 that is the source of the migrating virtual machine 406).
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 and spirit 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.
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