Patent Publication Number: US-2023155912-A1

Title: Rate limiting of cloud account change events and state management

Description:
BACKGROUND 
     A data center is a facility that houses servers, data storage devices, and/or other associated components such as backup power supplies, redundant data communications connections, environmental controls such as air conditioning and/or fire suppression, and/or various security systems. A data center may be maintained by an information technology (IT) service provider. An enterprise may purchase data storage and/or data processing services from the provider in order to run applications that handle the enterprises&#39; core business and operational data. The applications may be proprietary and used exclusively by the enterprise or made available through a network for anyone to access and use. 
     A software defined data center (SDDC) can include objects, which may be referred to as virtual objects. Virtual objects, such as virtual computing instances (VCIs), for instance, have been introduced to lower data center capital investment in facilities and operational expenses and reduce energy consumption. A VCI is a software implementation of a computer that executes application software analogously to a physical computer. Virtual objects have the advantage of not being bound to physical resources, which allows virtual objects to be moved around and scaled to meet changing demands of an enterprise without affecting the use of the enterprise&#39;s applications. In a software defined data center, storage resources may be allocated to virtual objects in various ways, such as through network attached storage (NAS), a storage area network (SAN) such as fiber channel and/or Internet small computer system interface (iSCSI), a virtual SAN, and/or raw device mappings, among others. 
     A customer may have one or more cloud accounts with a cloud provider (e.g., a public cloud provider). Cloud accounts can be monitored and/or modeled by a monitoring platform to provide security, for instance. The monitoring and/or modeling may be enabled through “change events” received from the cloud provider. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram of a host and a system for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. 
         FIG.  2    is a flow chart associated with rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. 
         FIG.  3    illustrates a system for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. 
         FIG.  4    is a diagram of a machine for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The term “virtual computing instance” (VCI) refers generally to an isolated user space instance, which can be executed within a virtualized environment. Other technologies aside from hardware virtualization can provide isolated user space instances, also referred to as data compute nodes. Data compute nodes may include non-virtualized physical hosts, VCIs, containers that run on top of a host operating system without a hypervisor or separate operating system, and/or hypervisor kernel network interface modules, among others. Hypervisor kernel network interface modules are non-VCI data compute nodes that include a network stack with a hypervisor kernel network interface and receive/transmit threads. 
     VCIs, in some embodiments, operate with their own guest operating systems on a host using resources of the host virtualized by virtualization software (e.g., a hypervisor, virtual machine monitor, etc.). The tenant (i.e., the owner of the VCI) can choose which applications to operate on top of the guest operating system. Some containers, on the other hand, are constructs that run on top of a host operating system without the need for a hypervisor or separate guest operating system. The host operating system can use name spaces to isolate the containers from each other and therefore can provide operating-system level segregation of the different groups of applications that operate within different containers. This segregation is akin to the VCI segregation that may be offered in hypervisor-virtualized environments that virtualize system hardware, and thus can be viewed as a form of virtualization that isolates different groups of applications that operate in different containers. Such containers may be more lightweight than VCIs. 
     Where the present disclosure refers to VCIs, the examples given could be any type of virtual object, including data compute node, including physical hosts, VCIs, non-VCI containers, virtual disks, and hypervisor kernel network interface modules. Embodiments of the present disclosure can include combinations of different types of virtual objects (which may simply be referred to herein as “objects”). As used herein, a container encapsulates an application in a form that&#39;s portable and easy to deploy. Containers can run without changes on any compatible system—in any private cloud or public cloud—and they consume resources efficiently, enabling high density and resource utilization. Although containers can be used with almost any application, they may be frequently associated with microservices, in which multiple containers run separate application components or services. The containers that make up microservices are typically coordinated and managed using a container orchestration platform. 
     As used herein, a “disk” is a representation of storage policy resources that are used by an object. As used herein, “storage policy resource” includes secondary or other storage (e.g., mass storage such as hard drives, solid state drives, removable media, etc., which may include non-volatile memory). The term “disk” implies a single physical memory device being used by an object. 
     A customer may have one or more cloud accounts with a cloud provider (e.g., Amazon Web Services (AWS), Azure, Google Cloud Platform (GCB), etc.). In accordance with the present disclosure, a monitoring platform, such as Cloudhealth Secure State (CHSS), can uncover configuration related security threats by maintaining a model (e.g., a graph model) of cloud accounts. By modeling the asset inventory of a cloud account in an interconnected, graph database, a monitoring platform makes it easier to inspect resources, metadata, relationships, and changes. For instance, different paths a criminal can take to access sensitive data or escalate privileges to hijack cloud accounts can be visualized. 
     A “change event” as referred to herein, is a message that is sent to the monitoring platform from a cloud provider when a read action, write action, update action, and/or delete action takes place in a cloud account monitored by the monitoring platform. Change events can enable real time updates to the model and uncover security vulnerabilities that the change may have introduced. In some embodiments, a change event received from a cloud provider can include a timestamp, a source, an event name, a subject and/or description, a timestamp of the event, and an identifier (e.g., a unique ID of the resource associated with the change event). 
     Traditionally, there are two ways in which a graph model in a monitoring platform is updated. In some instances, a change event triggers a collection for the particular resource that the change concerns. In other instances, a full account-wide collection results in querying the current state of all resources in a cloud account. 
     However, there may be issues associated with processing change events. For instance, there may be limits to the number of change events that can be processed by the underlying data pipeline in the monitoring platform. This bottleneck, if exceeded, can have negative results. For instance, a backlog of processing change events can be created, causing delays in discovering security vulnerabilities. Additionally, because monitoring platforms may have shared infrastructure for customers, if one customer&#39;s cloud account sends a high rate of change events, it could negatively impact another customer&#39;s latency in uncovering security vulnerabilities. 
     Embodiments of the present disclosure can enable a more stable platform and a more predictable latency for processing change events by rate limiting change events. A rate limiting solution in accordance with embodiments herein can utilize a version of a sliding window algorithm in order to set limits on the number of change events the monitoring platform processes per cloud account. In addition to rate limiting, embodiments of the present disclosure include a mechanism to update the state of a cloud accounts graph model in a monitoring platform. 
     As discussed further below, when an account exceeds a particular rate threshold, a flag indicating that the account has change events disabled can be created. This flag may be referred to herein as “eventswitch.” For any received future change event for the cloud account, if the eventswitch indicates that change events are disabled for the account, then the change event is dropped or discarded (e.g., not stored). In order to get the state of the model current, a collection can be performed for all assets (e.g., resources) in the cloud account. Once the collection is completed, the state of the cloud account in the monitoring platform database is up to date. After the completion of the collection for all the assets in the cloud account, the eventswitch can be set to “enabled” for the cloud account, which indicates that change events can be processed for the cloud account. 
     Additionally, though the present disclosure discusses limiting (e.g., not processing) change events that are associated with a particular cloud account, embodiments herein are not so limited and can be used more granularly. In some embodiments, for instance, change events can be limited based on the type of event rather than just by account. For example, change events related to reads by a particular account can be limited. In some embodiments, change events related to a type of asset, or to a specific asset, can be limited. 
     The figures herein follow a numbering convention in which the first digit or digits correspond to the drawing figure number and the remaining digits identify an element or component in the drawing. Similar elements or components between different figures may be identified by the use of similar digits. For example,  114  may reference element “ 14 ” in  FIG.  1   , and a similar element may be referenced as  414  in  FIG.  4   . As will be appreciated, elements shown in the various embodiments herein can be added, exchanged, and/or eliminated so as to provide a number of additional embodiments of the present disclosure. In addition, as will be appreciated, the proportion and the relative scale of the elements provided in the figures are intended to illustrate certain embodiments of the present invention, and should not be taken in a limiting sense. 
       FIG.  1    is a diagram of a host and a system for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. The system can include a host  102  with processing resources  108  (e.g., a number of processors), memory resources  110 , and/or a network interface  112 . The host  102  can be included in a software defined data center. A software defined data center can extend virtualization concepts such as abstraction, pooling, and automation to data center resources and services to provide information technology as a service (ITaaS). In a software defined data center, infrastructure, such as networking, processing, and security, can be virtualized and delivered as a service. A software defined data center can include software defined networking and/or software defined storage. In some embodiments, components of a software defined data center can be provisioned, operated, and/or managed through an application programming interface (API). 
     The host  102  can incorporate a hypervisor  104  that can execute a number of virtual computing instances  106 - 1 ,  106 - 2 ,. . . ,  106 -N (referred to generally herein as “VCIs  106 ”). The VCIs can be provisioned with processing resources  108  and/or memory resources  110  and can communicate via the network interface  112 . The processing resources  108  and the memory resources  110  provisioned to the VCIs can be local and/or remote to the host  102 . For example, in a software defined data center, the VCIs  106  can be provisioned with resources that are generally available to the software defined data center and not tied to any particular hardware device. By way of example, the memory resources  110  can include volatile and/or non-volatile memory available to the VCIs  106 . The VCIs  106  can be moved to different hosts (not specifically illustrated), such that a different hypervisor manages the VCIs  106 . The host  102  can be in communication with a monitoring platform system  114 . An example of the monitoring platform system is illustrated and described in more detail below. In some embodiments, the monitoring platform system  114  can be a server, such as a web server. 
       FIG.  2    is a flow chart associated with rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. At  216 , a change event for a cloud account is received. The change event can be received from a cloud provider (e.g., AWS, Azure, GCB, etc.). In some embodiments, the change event triggers collection for the particular resource associated with the change. At  218 , a determination is made regarding whether eventswitch is enabled or disabled for the cloud account. If eventswitch is disabled, the change event is dropped and/or discarded at  222 . If eventswitch is enabled, a determination is made at  224  whether the change event causes the cloud account to exceed a rate threshold. In some embodiments, a sliding window (e.g., Leaky Bucket, Token Bucket, Fixed Window, etc.) can be used. A summary of a sliding window algorithm may be: if the number of requests served on configuration key “key” in the last “time_window_sec” seconds is more than “number_of_requests” configured for it then discard, else the request goes through while the counter is updated. The rate threshold is configurable. In some embodiments, the cloud account exceeds the rate threshold at  50  events per second. In some embodiments, the cloud account exceeds the rate threshold at  100  events per second. 
     If the cloud account does not exceed the threshold, the change event is processed at  228 . If the cloud account does exceed the threshold, eventswitch is marked as disabled for the cloud account at  226 . At  230 , a collection is performed for all resources in the cloud account. The collection can be performed after a period of time. The period of time can be selected based on an expected reduction in the rate of change events. The period of time is configurable. In some embodiments, the period of time can be 15 minutes. In some embodiments, the period of time is 30 minutes. In some embodiments, the period of time is one hour. Once the collection is performed, events are again received (no longer discarded) and the process can return to  216 . 
       FIG.  3    illustrates a system  332  for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. The system  332  can include a database  334 , a subsystem  336 , and/or a number of engines, for example first stream engine  338 , threshold engine  340 , discard engine  342 , collection engine  344 , and/or third stream engine  346 , and can be in communication with the database  334  via a communication link. The system  332  can include additional or fewer engines than illustrated to perform the various functions described herein. The system can represent program instructions and/or hardware of a machine (e.g., machine  448  as referenced in  FIG.  4   , etc.). As used herein, an “engine” can include program instructions and/or hardware, but at least includes hardware. Hardware is a physical component of a machine that enables it to perform a function. Examples of hardware can include a processing resource, a memory resource, a logic gate, etc. 
     The number of engines can include a combination of hardware and program instructions that is configured to perform a number of functions described herein. The program instructions (e.g., software, firmware, etc.) can be stored in a memory resource (e.g., machine-readable medium) as well as hard-wired program (e.g., logic). Hard-wired program instructions (e.g., logic) can be considered as both program instructions and hardware. 
     In some embodiments, the first stream engine  338  can include a combination of hardware and program instructions that is configured to process each of a first stream of change events received from a cloud provider and associated with any assets of a particular public cloud account. The first stream engine  338  can process a change event of the first stream of change events responsive to a determination that the change event does not cause the first stream of change events to exceed the rate threshold. 
     In some embodiments, the threshold engine  340  can include a combination of hardware and program instructions that is configured to determine that the first stream of change events exceeds a rate threshold. In some embodiments, the threshold engine  340  can perform a check for a flag associated with the particular public cloud account indicating that the first stream of change events exceeds the rate threshold. The threshold engine  340  can determine, for each change event of the first stream of change events, whether that change event causes the first stream of change events to exceed the rate threshold responsive to determining a lack of the flag. The threshold engine  340  can associate the flag with the particular public cloud account indicating that the first stream of change events exceeds the rate threshold responsive to a determination that a change event of the first stream of change events causes the first stream of change events to exceed the rate threshold. 
     In some embodiments, the discard engine  342  can include a combination of hardware and program instructions that is configured to discard each of a second stream of change events received from the public cloud provider and associated with any assets of the particular public cloud account. In some embodiments, the discard engine  342  can discard each of the second stream of change events received from the public cloud provider and associated with any assets of the particular public cloud account responsive to determining a presence of the flag. 
     In some embodiments, the collection engine  344  can include a combination of hardware and program instructions that is configured to query the cloud provider to perform a collection on all the assets of the particular public cloud account after a particular delay period. In some embodiments, the third stream engine  346  can include a combination of hardware and program instructions that is configured to process each of a third stream of change events received from the cloud provider and associated with any assets of the particular public cloud account responsive to a completion of the collection. 
       FIG.  4    is a diagram of a machine  448  for rate limiting cloud account change events and state management according to one or more embodiments of the present disclosure. The machine  448  can utilize software, hardware, firmware, and/or logic to perform a number of functions. The machine  448  can be a combination of hardware and program instructions configured to perform a number of functions (e.g., actions). The hardware, for example, can include a number of processing resources  108  and a number of memory resources  410 , such as a machine-readable medium (MRM) or other memory resources  410 . The memory resources  410  can be internal and/or external to the machine  448  (e.g., the machine  448  can include internal memory resources and have access to external memory resources). In some embodiments, the machine  448  can be a virtual computing instance (VCI) or other computing device. The term “VCI” covers a range of computing functionality. The term “virtual machine” (VM) refers generally to an isolated user space instance, which can be executed within a virtualized environment. Other technologies aside from hardware virtualization can provide isolated user space instances, also referred to as data compute nodes. Data compute nodes may include non-virtualized physical hosts, VMs, containers that run on top of a host operating system without a hypervisor or separate operating system, and/or hypervisor kernel network interface modules, among others. Hypervisor kernel network interface modules are non-VM data compute nodes that include a network stack with a hypervisor kernel network interface and receive/transmit threads. The term “VCI” covers these examples and combinations of different types of data compute nodes, among others. 
     The program instructions (e.g., machine-readable instructions (MM)) can include instructions stored on the MRM to implement a particular function (e.g., an action such as processing streams of change events). The set of MRI can be executable by one or more of the processing resources  408 . The memory resources  410  can be coupled to the machine  448  in a wired and/or wireless manner. For example, the memory resources  410  can be an internal memory, a portable memory, a portable disk, and/or a memory associated with another resource, e.g., enabling MRI to be transferred and/or executed across a network such as the Internet. As used herein, a “module” can include program instructions and/or hardware, but at least includes program instructions. 
     Memory resources  410  can be non-transitory and can include volatile and/or non-volatile memory. Volatile memory can include memory that depends upon power to store information, such as various types of dynamic random access memory (DRAM) among others. Non-volatile memory can include memory that does not depend upon power to store information. Examples of non-volatile memory can include solid state media such as flash memory, electrically erasable programmable read-only memory (EEPROM), phase change random access memory (PCRAM), magnetic memory, optical memory, and/or a solid state drive (SSD), etc., as well as other types of machine-readable media. 
     The processing resources  408  can be coupled to the memory resources  410  via a communication path  460 . The communication path  460  can be local or remote to the machine  448 . Examples of a local communication path  460  can include an electronic bus internal to a machine, where the memory resources  410  are in communication with the processing resources  448  via the electronic bus. Examples of such electronic buses can include Industry Standard Architecture (ISA), Peripheral Component Interconnect (PCI), Advanced Technology Attachment (ATA), Small Computer System Interface (SCSI), Universal Serial Bus (USB), among other types of electronic buses and variants thereof. The communication path  460  can be such that the memory resources  410  are remote from the processing resources  408 , such as in a network connection between the memory resources  410  and the processing resources  408 . That is, the communication path  460  can be a network connection. Examples of such a network connection can include a local area network (LAN), wide area network (WAN), personal area network (PAN), and the Internet, among others. 
     As shown in  FIG.  4   , the MRI stored in the memory resources  408  can be segmented into a number of modules  438 ,  440 ,  442 ,  444 ,  446  that when executed by the processing resources  408  can perform a number of functions. As used herein a module includes a set of instructions included to perform a particular task or action. The number of modules  438 ,  440 ,  442 ,  444 ,  446  can be sub-modules of other modules. For example, the third stream module  446  can be a sub-module of the first stream module  438  and/or can be contained within a single module. Furthermore, the number of modules  438 ,  440 ,  442 ,  444 ,  446  can comprise individual modules separate and distinct from one another. Examples are not limited to the specific modules  438 ,  440 ,  442 ,  444 ,  446  illustrated in  FIG.  4   . 
     One or more of the number of modules  438 ,  440 ,  442 ,  444 ,  446  can include program instructions and/or a combination of hardware and program instructions that, when executed by a processing resource  408 , can function as a corresponding engine as described with respect to  FIG.  3   . For example, the threshold module  440  can include program instructions and/or a combination of hardware and program instructions that, when executed by a processing resource  408 , can function as the threshold engine  340 . 
     For example, the machine  446  can include a first stream module  438 , which can include instructions to process each of a first stream of change events received from a cloud provider and associated with any assets of a particular public cloud account. 
     For example, the machine  446  can include a threshold module  440 , which can include instructions to determine that the first stream of change events exceeds a rate threshold. 
     For example, the machine  446  can include a discard module  442 , which can include instructions to discard each of a second stream of change events received from the public cloud provider and associated with any assets of the particular public cloud account. 
     For example, the machine  446  can include a collection module  444 , which can include instructions to query the cloud provider to perform a collection on all the assets of the particular public cloud account after a particular delay period. 
     For example, the machine  446  can include a third stream module  446 , which can include instructions to process each of a third stream of change events received from the cloud provider and associated with any assets of the particular public cloud account responsive to a completion of the collection. 
     The present disclosure is not limited to particular devices or methods, which may vary. The terminology used herein is for the purpose of describing particular embodiments, and is not intended to be limiting. As used herein, the singular forms “a”, “an”, and “the” include singular and plural referents unless the content clearly dictates otherwise. Furthermore, the words “can” and “may” are used throughout this application in a permissive sense (i.e., having the potential to, being able to), not in a mandatory sense (i.e., must). The term “include,” and derivations thereof, mean “including, but not limited to.” 
     Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even where only a single embodiment is described with respect to a particular feature. Examples of features provided in the disclosure are intended to be illustrative rather than restrictive unless stated otherwise. The above description is intended to cover such alternatives, modifications, and equivalents as would be apparent to a person skilled in the art having the benefit of this disclosure. 
     The scope of the present disclosure includes any feature or combination of features disclosed herein (either explicitly or implicitly), or any generalization thereof, whether or not it mitigates any or all of the problems addressed herein. Various advantages of the present disclosure have been described herein, but embodiments may provide some, all, or none of such advantages, or may provide other advantages. 
     In the foregoing Detailed Description, some features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the disclosed embodiments of the present disclosure have to use more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.