# Patent

## Title
Datacenter entity information system

## Abstract

A computerized datacenter entity information method includes obtaining datacenter entity information at an initial time, compressing and storing the datacenter entity information as a datacenter entity frame of reference, obtaining datacenter entity information changes, and compressing and storing the datacenter entity information changes with respect to the datacenter entity frame of reference. In another implementation, the computerized datacenter entity information method includes obtaining datacenter entity information at an initial time, compressing and storing the datacenter entity information as a datacenter entity frame of reference, obtaining datacenter entity information changes, and compressing and storing the datacenter entity information changes with respect to the datacenter entity frame of reference.

## Background
RELATED APPLICATIONS
This application claims the benefit of the filing of application Ser. No. 14/464,579, filed on Aug. 20, 2014.
BACKGROUND
Modern datacenters include large numbers of physical and virtual entities. Examples of physical entities may include application servers, storage servers, load balancers, network interface cards (NICs), firewalls, switches, routers, etc. Almost any of these physical entities can be virtualized as software entities, such as virtual machines (VM), virtual firewalls, virtual switches, virtual routers, virtual extensible local area networks (VXLANs), etc.
Virtual entities increase the complexities of monitoring, troubleshooting, searching characteristics of, and visualizing these dynamically configurable networks. Existing systems may only monitor and collect data for a subset of physical or virtual components within the datacenter. Existing systems also may not be able to understand these new architectures, entities, and the relationships between these entities. Thus, present systems may not effectively monitor or visualize the physical and virtual entity configurations within modern datacenters, and may not provide adequate capabilities to search characteristics of such datacenters.
Additional aspects and advantages of this invention will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts an example datacenter management system.
FIGS. 2A and 2B depict example information displayed by the management system.
FIG. 3 depicts example information displayed by the management system.
FIG. 4 depicts a topology displayed by the management system.
FIG. 5 depicts example collaboration interface provided by the management system.
FIG. 6 depicts an example datacenter network.
FIG. 7 depicts an example model/schema used by the management system.
FIG. 8 depicts example data models used by the management system.
FIG. 9 depicts example documents generated by the management system.
FIG. 10 depicts an example process for generating a layer 2 network model.
FIG. 11 depicts how documents and data models are dynamically updated.
FIG. 12 is a flow diagram of a datacenter entity information storage method by which datacenter entity information may be stored in a compressed data format.
FIG. 13 is a flow diagram of a datacenter entity information storage method.
FIG. 14 depicts an example computing device for implementing the datacenter management system.



DETAILED DESCRIPTION
A datacenter management system uses time-series based modeling of entities and properties (Objects) to effectively capture the evolving state of a datacenter. Models represent physical, virtual, and logical entities and entity relationships for different periodic and aperiodic events. The management system captures different performance data and configuration data associated with the entities. The performance data measures performance of different entities, such as central processing unit (CPU) utilization, memory utilization, packet drops, etc. The configuration data identifies configurations within entities, such as the number of CPUs assigned to a virtual machine or the rules used by a physical or virtual firewall.
A natural language based search engine operates within the context of the datacenter domain and identifies the intent of search queries. The search engine uses the models to more efficiently search and visualize current and historical events within the datacenter. The search engine also includes a collaboration feature that enables users to more effectively exchange datacenter information.
FIG. 1 depicts an example management system 100. A datacenter 102 may include a variety of different physical entities 104 and virtual entities 106. Physical entities104 may comprise any physical devices used for processing, storing, and/or exchanging data. A few examples of physical entities 104 include application servers, storage servers, load balancers, network interface cards (NICs), firewalls, switches, routers, client devices, etc. Virtual entities106 may include any entities virtualized in software, such as virtual machines (VM), virtual firewalls (VFW), virtual switches, virtual routers, virtual extensible local area networks (VXLANs), etc. Datacenter 102 also may include different logical entity relationships, such as layer 2 and layer 3 logical networks. These are just examples of an almost limitless number of different physical and virtual entities and relationships that may exist within datacenter 102.
Data collection proxies 108 are alternatively referred to as crawlers and collect and store data from physical entities 104 and virtual entities106 in a data storage layer 110. The data may include performance data 112, configuration or change data 114, and event and log data, such as alerts, problems, faults, etc. Management system 100 also may store search indexes and search histories 116 from search queries.
Performance data 104 may be associated with the performance of any entity or combination of entities in datacenter 102. Examples of performance data 112 include CPU utilization, packet processing, packet transmission and reception drops, memory utilization, etc. Examples of configuration data 114 include any information associated with the configuration of any entity or combination of entities in datacenter 102. Examples of configuration data 114 include a number of CPUs assigned to a VM, network and communication paths in a VXLAN, rules in a physical or virtual firewall, NICs connected to a VM, changes in system configurations, etc. These are just a few examples of almost limitless types of performance and configuration data.
Data collection proxies 108 may periodically collect performance data 112 and/or configuration data 114. For example, proxies 108 may monitor CPU utilization for a VM every ten minutes and save the utilization values as part of performance data 112. Data collection proxies 108 may aperiodically collect other performance data 112 and/or configuration data 114. For example, collection proxies 108 may identify the number of CPUs assigned to a VM as part of configuration data 114.
Data collection proxies 108 may include any combination of existing and customized programs for monitoring and extracting data from entities 104 and 106. For example, physical entities104, such as routers and switches, may include application program interfaces (APIs) for extracting CPU utilization, memory utilization, packet drops, routing tables, logged data, address resolution protocol (ARP) tables, etc.
A computation layer 118 uses the data in storage layer 110 to provide information to a user interface layer 126. A model schema 119 identifies the general relationships and properties associated with entities in datacenter 102. Data models 120 represent the particular performance data 112 and configuration data 114 associated with the entities in datacenter 102. For example, CPU and memory utilization, logical and physical connections, end-to-end communication paths, logical layer 2 and layer 3 network connections, application topologies, VXLAN topologies, port groups, etc. Some data models 120 are manually created and other data models 120 may be dynamically generated.
Analytics engine 122 automatically monitors and identifies data and other events. Analytics engine 122 may include event detectors that identify significant events in datacenter 102. For example, the event detector may identify configuration changes and performance data representing the performance status of datacenter 102. Analytics engine 122 may also operate as outlier detector that identifies events that are outside normal operating levels. For example, an outlier detector may identify CPU utilization above a particular threshold level. Analytics engine 122 may also operate as a problem detector that identifies problems in datacenter 102. For example, the problem detector may identify large packet losses or configuration mismatches between entities.
Search engine 124 conducts natural language searches within datacenter 102 and identifies a search query intent based on model schema 119 and a datacenter dictionary. Instead of operating just on keywords, search engine 124 also understands search query phrases that may be part natural language and part expression. This provides richer intent expression, greater ease of use, and applies well to the datacenter problem domain. For example, a search term such as TROUBLESHOOT may cause search engine 124 to search problem data generated by analytics engine 122.
Search engine 124 may operate as a time machine executing queries for specified time intervals. For example, a user may enter a search term requesting search engine 124 to show all configuration changes for a specified network over the past two days. In another example, the user may enter a search term requesting CPU usage for a host device over the last two days. Data models 120 may be configured in a unique time series format that enables search engine 124 to quickly identify events for any selectable time period.
Data models 120 may include identifiers associated with different physical and virtual entities, networks, performance data, and/or configuration data. Search engine 124 may search for data or provide search suggestions based on data models 120. For example, one of data models 120 for a virtual firewall may contain firewall rules. A user may enter the search term RULES. Search engine 124 may identify the firewall rules in the model or provide a suggestion for displaying the rules identified in the virtual firewall model.
User interface layer 126 includes a search interface 128 for receiving search queries and displaying search results. Search interface 128 may receive natural language based expressions for search engine 124 and display the results from search engine 124 in a textual and/or graphical format. A visualization manager 130 generates topology diagrams representing different entities and network configurations within the datacenter.
A collaboration manager 132 captures particular states of the datacenter and enables different users to view, share, and manipulate the captured states. For example, a first user may identify a network state where a large number of packets are being dropped. The first user may direct collaboration manager 132 to capture that state of the network and identify a second user for sharing the captured state. The second user can access the captured network state and perform additional analysis.
A newsfeed manager 133 identifies and displays events of particular interest to the user. For example, newsfeed manager 133 may identify a search index and/or search history 116 for a user currently logged on to the system. For example, a user may have previously performed search queries for a particular logical network. When the user logs in, newsfeed manager 133 may automatically display events associated with the previously queried logical network.
FIG. 2A depicts example screen sections 150A and 150B displayed within a computer screen by search interface 128. Referring to FIGS. 1 and 2A, analytics engine 122 monitors and identifies events associated with the datacenter models. The search engine operates in conjunction with the models and associated data to provide search suggestions and search results corresponding with the datacenter information.
For example, the user may enter the search term SHOW into search field 152. Search interface 128 may submit the search term to the search engine and display received back suggestions in drop down menu 154. For example, search interface 128 may display suggestions for showing a topology for VXLAN, showing all VMs, showing changes in a network since yesterday, or showing a topology for a VM. In another example, the user may enter the search term V into search field 152. User interface may display suggestions in drop down menu 154, such as virtual, VXLAN, VM, etc. The suggestions may be based on the models and the names of entities identified in the models or may be based on a datacenter library that may include names of datacenter entities.
The suggestions and search results also may be based on search history 116. For example, search engine 124 may store previous search queries and provide at least some suggestions that are weighted based on the most frequently requested networks, entities, and data.
Search interface 128 may display newsfeed data from newsfeed manager 133 within screen section 150B. The newsfeed data in screen section 150B is searchable since the events, anomalies, and/or changes are first class objects. In this example, the newsfeed data identifies a layer 2 network and identifies a maximum transmission unit (MTU) mismatch within a VXLAN. Newsfeed manager 133 may automatically generate the data in screen section 150B based on user configuration setting.
FIG. 2B depicts an example screen section 150C displayed within a computer screen by the search interface 128. In this example, the user enters the search term: SHOW VMS WITH NUMCPUS>1. Search engine 124 searches VMs models for any configuration data indicating more than one CPU. Search interface 128 displays the names of any identified VMs, number of CPUs, the host device IP address, memory capacity, and number of virtual NICs connected to the VM. Search interface 128 also may display a graph showing the percentage of CPU bandwidth used by the different virtual machines.
FIG. 3 depicts examples of other information displayed by search interface 128. Referring to FIGS. 1 and 3, in this example a user enters the search term TROUBLESHOOT LOGICAL NETWORK 1234 into search field 152. Logical network 1234 was previously configured by a user and may include a variety of physical and virtual entities. Search interface 128 sends the search term to search engine 124.
Search engine 124 uses the model schema 119 in FIG. 1 to identify the relationships and properties of a logical network and then searches data models 120 for problem data identified by analytics engine 122 and associated with any of the entities of the logical network 1234. For example, search engine 124 may identify a firewall XYZ in logical network 1234 dropping 27% of the packets transmitted between VM563 and VM 8902. Search engine 124 may also identify a configuration mismatch for a host device HOST 3 in logical network 1234 that blocks packets to VM 563.
A screen section 150E shows performance data associated with logical network 1234. In this example, the performance data includes CPU usage and memory usage for two entities VM 563 and Host 3 within logical network 1234. Visualization manager 130 may display different information and visualize the information differently based on the search query intent and on the search results. For example, the intent of the search query in search field 152 is to identify problems in logical network 1234. Visualization manager 130 may display problem data discovered in the search results in a larger screen section 150 than other data. If no problem data is discovered, visualization manager 130 may display performance metrics, or other data, that could reflect potential network problems.
FIG. 4 depicts an example topology displayed by search interface 128. Referring to FIGS. 1 and 4, a user may enter the search term TROUBLESHOOT LOGICAL NETWORK 1234 into search field 152. Search interface 128 submits the search term to search engine 124. Search engine 124 uses models 199 and 120 to identify the entities and relationships associated with logical network 1234. For example, data models 120 may include a graph model that identifies all of the entities and relationships within logical network 1234. Visualization manager 130 uses the entity and relationship information to display topology 162.
In one example, topology 162 comprises multiple circular layers 170 that represent different physical and/or logical entities within logical network 1234. Underlay layers 170 refer to the physical network serving an overlay layer 170. For example, underlay layer 170A is the network that delivers the packets from multiple overlay networks between hosts and may include physical switches and routers. Overlay layer 170B includes circles displayed over layer 170A representing the VMs configured on the hosts in layer 170A.
A logical switch layer 170C is displayed over host layer 170A and shows the logical switches connecting the VMs in layer 170B to other network entities. A layer 170D shows tunnel endpoints and a layer 170E identifies virtual switches (VS) within logical network 1234. A central circular layer 170F displays physical network entities connecting the hosts in layer 170A. Visualization manager 130 displays boxes in layer 170F that represent different physical switches and routers that form logical network 1234. Visualization manager 130 also displays lines 172 that show the logical and physical connections between the different virtual and physical entities in logical network 1234.
Search interface 128 may detect selections of different entities within topology 162 and display associated information. For example, search interface 128 may detect a mouse click on VM 563 and display the associated properties, such as number of CPUs, memory allocation, logical network relationships, NICs, etc. In another example, the user may click on two different VMs and search interface 128 may automatically display the logical and physical network topology containing the two selected VMs.
As explained above, search engine 124 may identify anomalies or problems within logical network 1234. Visualization manager 130 may display the identified anomalies or problems within topology 162. For example, visualization manager 130 may highlight or flash the circle in layer 170B representing a VM that is using a large amount of CPU resources or may highlight or flash one of the boxes in layer 170E representing a firewall that is dropping a large percentage of packets.
Search interface 128 may display a screen section 164 that allows the user to select the different layers 170 displayed in topology 162. For example, a first set of user selectable layers may include VMs, virtual firewalls, logical switches, virtual switches, and VXLAN tunnel end points (VTEPs). A second set of user selectable layers 170 may include physical hosts, switches, routers, load balancers, and firewalls.
A screen section 166 allows the user to select which properties are displayed within topology 162. For example, search interface 128 may selectively display MTU values, packet drops, IP addresses, physical switch port numbers, etc. for entities in logical network 1234 based on associated properties selected in screen section 166.
A slider 174 controls time periods associated with topology 162. For example, a slider knob 176 may currently be positioned underneath the date 3/20. Search interface 128 displays topology 162 for logical network 1234 on March 20th. The user may drag slider knob 176 from underneath the date 3/20 back in time to a position underneath date 3/17. Search interface 128 accesses previously captured data for logical network 1234 and displays the topology of logical network 123 for March 17th. For example, a VM 8902 may have been configured on a different host on March, 17th. Search interface 128 would display a different topology for logical network 1234 with VM 8902 located on the different host.
Moving slider knob 170 further to the left shows even earlier states of logical network 1234 and moving slider knob 170 further to the right shows later states for logical network 1234. Moving slider knob to the farthest right position shows the current state of logical network 1234. The user can also change the time period for logical network 1234 via search field 152. For example, the user can enter the search term TROUBLESHOOT LOGICAL NETWORK 1234 ON 3/17.
Search engine 124 identifies network states based on user intent. For example, based on the search term TROUBLESHOOT LOGICAL NETWORK 1234, search engine 124 predicts the user intent as wanting to view the entities in logical network 1234 with the latest problems or anomalies. If the user enters the search term TROUBLESHOOT VM 563, visualization manager 130 predicts the intent of the user as viewing text and graphs associated with the performance of VM 563.
The search engine identifies any time periods associated with the search query. For example, the search query may include a specific time period, such as a time of day, day, year, etc. In another example, the user may select the time slider previously shown in FIG. 4. If no time period is explicitly selected, the search engine may assume a time period, such as the current hour and day. The search engine may also assume the time period based on the search query intent. For example, if a user enters the search term TROUBLESHOOT LOGICAL NETWORK ABC, the search engine may assume the search should progress from a current day back in time to identify any problem data associated with the logical network ABC.
FIG. 5 depicts an example pinboard displayed in search interface 128. Referring to FIGS. 1 and 5, a user may select any of the screen sections described above and select an icon 184. Collaboration manager 132 attaches the information associated with the selected screen section to a pinboard 180. Collaboration manager 132 can then distribute the contents in pinboard 180 to different selected users.
For example, a first user may initiate a search query in search field 152 to troubleshoot logical network 1234. The first user may identify an interesting data related to logical network 1234 for March 20th at 09:21. The user selects topology 162 by clicking on icon 184. Once selected, collaboration manager 132 inserts the data associated with topology 162 into section 182A of pinboard 180 and saves the associated data in a collaboration database.
The first user may select any combination of visualizations and/or data from any combination of screen sections. For example, the user also may select pin icons associated with screen sections 150D and 150E in FIG. 3. Collaboration manager 132 moves the data for the selected screen sections 150D into section 182B of pinboard 180 and moves the data for screen section 152E into section 182C of pinboard 180 and stores the associated data into the collaboration database. The first use may also generate and pin comments 182D to pinboard 180. For example, the first user may generate comment 182D that points out a potential problem with Host 1. Collaboration manager 132 stores comment 182D with the other data in the collaboration database.
The first user can share pinboard 180 with other users. For example, the first user may identify one or more names or Internet addresses of other datacenter users. Collaboration manager 132 sends the identified users links to the collaboration data containing pinboard 180. Collaboration manager 132 displays pinboard 180 in response the other users selecting the link. The other users can then start troubleshooting the datacenter from the same state pinned by the first user.
Search interface 128 and collaboration manager 132 provide more effective troubleshooting and data mining by allowing users to visually search through datacenter topologies for different selectable time periods and share identified states with other users.
FIG. 6 shows one example network 200 configured within datacenter 102. In the following example, network 200 refers to any combination of topologies, configurations, communication paths, virtual networks, virtual extensible local area networks (VXLANs), logical networks, or the like, or any other combination of physical and/or virtual entities that may be configured within datacenter 102.
In this example, network 200 includes a first host (HOST1) configured for operating a first virtual machine (VM1), a virtual firewall (VFW), a virtual load balancer (VLB), and a logical switch and router (VSR). A second host (HOST2) is configured for operating a second virtual machine (VM2), a virtual firewall (VFW), a virtual load balancer (VLB), and a logical switch and router (VSR). A first physical switch (SW1) connects HOST1 to a physical router (RTR) and a second physical switch (SW2) connects HOST2 to RTR. The RTR is connected to other networks and devices via Internet 190.
In one example, HOST1, HOST2, SW1, SW2, and RTR are physical hardware entities. The entities VM1, VFW, VLB, and VSR are software entities operating on HOST1, and the entities VM2, VFW, VLB, and VSR are software entities operating on HOST2. Configuring physical and virtual entities into a network 200 are known to those skilled in the art and are therefore not described in further detail.
Models
Different schemas are used for modeling entities in the datacenter. The general relationships and properties classifying networks, and entities within networks, are referred to as models, model schemas, or schemas. The specific port connections, relationships, performance data, configuration data, and property values for the configured networks and network entities are referred to as data, data models, data model schemas, and/or storage schemas.
FIG. 7 shows one example of a model/schema 119. Model/schema 119 is one example of an object relational graph used in the datacenter for representing entities in the data center and representing the general relationships between the entities in the datacenter. This is just one example of multiple different model schemas used in the datacenter.
In one example, an object representing a datacenter entity may be classified as a concrete entity or a logical entity. The concrete entity may be further classified as a server, router, or switch. The object for the server may be classified as either a host or a VM. An object for the router may be identified as having multiple remote direct memory access (RDMA) network interface cards (RNIC). An object for the switch may be classified as having multiple switch ports.
An object for the host may have the property of containing VMs and having multiple network interface cards (NICs). An object for the VM may have the property of having multiple NICs. The object for the RNIC may have a connection relationship with a switch port. The object for the switch port may have a connection relationship with the NIC.
The object for the logical entity may be classified as being a L2 network, a L3 network, or a network path. The object for the L2 network may have a layer 2 graph that further identifies the nodes and edges for connecting to other entities in the datacenter. Attributes may be associated with the graph nodes and edge connections, such as transmission speed, etc. The object for the L3 network may have a layer 3 graph that further identifies the nodes and edges for connecting to other entities in the L3 network.
Thus, model schema 119 may include:
entities {concrete entity, server, router, switch, host, VM, switch port, VNIC, logical entity, L2 Network, L3 network, layer 2 graph, layer 3 graph}
An entity, such as a VM, may have a set of properties defined by another model/schema, such as:
VirtualMachine (VM) {name (string), primaryIpAddress (string), hostName (string), memory (integer, unit=MB), number of CPUs (integer), power status (binary), routing configuration (string), DNS configuration (string), os (integer), vnics (Vnic)}.
The NIC object in model/schema 119 may have a set of properties defined by another model/schema, such as:
VNIC {MTU, IP addresses, NICId, operational status, speed, duplex type}.
Model/schemas 119 are used by the search engine for understanding types of entities, relationships between entities, and valid and invalid properties for entity objects. For example, a user may enter the query: FIND VMs WITH NO VNICS. The search engine uses model/schema 119 to understand that a VM has an array of entries referred to as VNICs and the VNIC entry must be empty to satisfy the query.
In another example, the user may enter the query: HOSTs IN 192.168.1.1/16. The query intent is to find hosts whose IP address in 192.168.*.* (where * is don't care operator). The search engine uses model/schemas 119 to identify where the IP address is stored in a host object.
FIG. 8 depicts example of data models 120 used in network 200 of FIG. 6. Data models 120 are alternatively referred to as a data schema or storage schema. Data models 120 may include different entity data models 202, relationship data models 204, and graph data models 206. Entity data models 202 contains or identify the performance and configuration data for different physical and virtual entities, such as hosts, virtual machines, virtual firewalls, switches, routers, or the like, or any combination thereof. For example, an entity data model 202A may represent the data for a virtual machine VM1, entity data model 202B may represent the data for virtual firewall VFW, and entity data model 202C may represent the data for switch SW1.
Entity data models 202 are bound together by one or more relationship data models 204. Relationship data models 204 are collections of layer 1 entity data models 202 combining properties, configurations, and relationships between the different entities. Relationship data models 204 may identify the physical and logical connections between different entities. For example, relationship model 204 may contain configuration data identifying relationships between entity data model 202A for VM1 and entity data model 202B for virtual firewall VFW, entity data model 202D for HOST1 (FIG. 6), and entity data model 202C for switch SW1.
Graph data models 206 contain configuration data that identify logical relationships and/or networks established between entities and are used for monitoring the communications and topology between different entities in different locations in the datacenter, such a communication path between VM1 and VM2. Graph data models 206 enable high level topology and connectivity analysis and are traversed in various ways to detect configuration inconsistencies such as a MTU mismatch or a VTEP missing condition. Examples graph data models 206 may include, but are not limited to, VXLAN networks, layer 2 networks, and layer 3 logical networks. In some instances, model manager 230 dynamically generates graph data models 206 in response to user configuration events. For example, a user may configure a VXLAN. Model manager 230 may detect the entities and entity ports configured for the VXLAN and dynamically generate an associated graph data model 206.
Data models 120 represent a domain abstraction for the software defined datacenter and can be mapped from the various vendor models and optimized for depicting aspects of the domain. Data models 120 are used for generating model driven analytics that drive troubleshooting in datacenter 102. Model driven analytics can identify configuration inconsistencies which are not typically used for driving anomaly detection/troubleshooting in datacenters.
Data models 120 are flexible in terms of the various data facets it captures as well as the form in which model data is consumed. Some data models 120 may include runtime objects that capture runtime data and represent entities with frequently changing properties, such as an address resolution protocol (ARP) table within a NIC or a media access control (MAC) table within a switch.
Data models 120 are also used for capturing and processing vendor, canonical, and user driven events. Lower level events may have simple rules for detection, such as a configuration property inconsistency, while higher level events may look at number of occurrences, multiple events etc.
Data collection proxies may store data from physical entities 104 and virtual entities 106 as vendor data. The vendor data may be mapped to canonical data and stored as canonical models. Model manager 230 may use the vendor data for computing logical domain objects, such as layer 2 models, layer 3 models, etc.
The data collection proxies periodically monitor and store performance data 112 and events and log data 117 associated with data models 120, such as CPU and memory utilization. The proxies also aperiodically detect and store configuration data 114 for the data models 120, such as a change in the number of assigned CPUs and memory. Data models 120 are linked to the performance data 112 and configuration data 114. For example, identifiers in data models 120 include keys or links to the stored performance and configuration data for the associated network entity. The search engine generates search instructions based on received search queries that access the data 112, 114, and 117 associated with data models 120.
FIG. 9 depicts an example table 240 containing different documents (DOCs) 232 associated with data models 202. Table 240 is a logical representation used for explanation purposes. Different versions of documents 232, data models 202, and associated data may be stored in different tables and different storage locations. Some documents 232 and data models 202 may not actually contain the data described below, but may contain links or identifiers to other tables that contain the associated data.
Model manager 230 in FIG. 8 may generate different versions of documents 232 based on different events associated with the data models 202. A first document version (V1) may extend up to the time of an identified event. The model manager may generate a second version (V2) of a document 232 in response to the identified event and extend the time period for the second version V2 of the document indefinitely until another event is identified. This time stamping process converts periodic and aperiodic data into a time series that can more effectively monitor, identify, and visualize datacenter information.
To explain in more detail, first version V1 of document DOC1 is associated with data model 202A for virtual machine VM1. Document DOC1 is assigned an identifier DOC1_V1 in column 242 of table 240 and assigned a starting time T0 in column 244 of table 240. Data associated with data model 202A is stored in one or more columns 246 of document DOC1_V1. For example, data model 202A may identify properties for VM1, such as a network path to VM2. In this example, the VM2 network path is set to TRUE indicating the network path is operational.
Document DOC1_V1 may contain configuration data in column 246 that identifies the host for VM1 and the number of CPUs, NICs, etc. for VM1. Column 246 also may include performance data for VM1, such as memory and CPU utilization, dropped packets, etc. Column 248 of document DOC1_V1 may identify relationships for VM1. For example, VM1 is identified as connected to virtual firewall VFW. As mentioned above, these are logical representations of different properties, configuration data, performance data, and relationships that a data model 202 and associated document DOC may contain or identify for an associated entity.
A first version V1 of a second document (DOC2) is generated for the data model 202B associated with virtual firewall VFW and is assigned an identifier VFW:DOC2_V1 in column 242. Document DOC2_V1 is also assigned a starting time stamp T0 in column 244. Data in column 246 identifies VFW as located in Host1, having a number of CPUs=1, and containing firewall rules FW_RULES=X for transmitting and/or dropping packets. Relationships in column 248 identify VFW as connected with VM1 and SW1.
At time T1, the management system detects an event associated with model VM1. For example, CPU utilization by VM1 may increase over some predefined threshold. In response to the detected high CPU utilization at time T1, model manager 230 in FIG. 9 generates a second version V2 of document DOC1. The first document version DOC1_V1 is assigned an ending time of T1 in column 244 and the second document version DOC1_V2 is assigned a starting time of T1 in column 244.
After time T1, the model manager stores or links data for VM1 with document version DOC1_V2. Table 240 now contains a time series for VM1 that starts with document version DOC1_V1 for time period T0-T1 and continues with document version DOC1_V2 for a time period that starts at time T1 and extends to some yet to be determined ending time.
In response to a rule change in virtual firewall VFW at time T3, the model manager generates a second document version DOC2_V2. The first document version DOC2_V1 is assigned an ending time of T3 in column 244 and the second document version DOC2_V2 is assigned a starting time of T3 in column 244. The model manager now stores or links new data associated with VFW with second document version DOC2_V2. Table 240 now contains time series data for an aperiodic rule change associated with VFW that starts with first document version DOC2_V1 for time period T0-T3 and continues with second document version DOC2_V2 with for a time period that starts at T3 and extends to a yet to be determined ending time.
Table 240 now contains time series data that identifies precise times when rules were changed in virtual firewall VFW. The rule change can be correlated with other performance or configuration data in the datacenter. For example, the user or search engine may correlate the aperiodic rule change at time T3 for VFW with periodically monitored performance data, such as the percentage of packet drops between VM1 and VM2.
Model manager 230 (FIG. 8) may create new document versions in response to any event. For example, the model manager may create a new document version in response to a reconfiguration of a switch, load balancer, VM, or any other entity in the datacenter. The model manager also may create new document versions in response to a relationship or network reconfiguration, such as a VM moving from one physical host to another physical host or a new entity added to or removed from network 200.
FIG. 10 shows an example of how model manager 230 automatically creates graph models 206 in FIG. 8, such as a layer 2 network model. In operation 250A, the model manager identifies layer 2 devices in the datacenter, such as switches, and adds the devices to an inventory. For example, users may register layer 2 devices or the model manager may discover the layer 2 devices via device configuration files.
In operation 250B, the model manager creates mappings for each virtual/physical switching device in the inventory. For example, a mapping is created for each VMWare® distributed virtual switch and for each Cisco® physical switch. The mappings are created for port/port groups. Mappings are created between identified layer 2 devices and layer 2 identifiers for all VLANs, VxLANs, or any other overlay and underlay layer 2 technologies associated with the port. A VLAN Id mapping is created for a VLAN and a segment Id mapping is created for a VxLAN.
The mapping may be referred to as a DeviceL2Switch and defined by a tuple comprising the device identifier, layer 2 identifier (VLAN, VxLAN etc.), and optionally the port group (in the virtual switch case). The DeviceL2Switch is a layer 2 slice of the switch for the identified device in question. The DeviceL2Switch is modeled as a switching entity and is a powerful representation of layer 2 connectivity which models a connectivity path at the layer 2 level across physical and virtual boundaries.
For example, a switch referred to as Device1 may include two ports. A first port eth100/1/1 may connect to VLANS 212, 213 and a second port eth100/1/2 may connect to VLAN 211. The user or model manager may create three DeviceL2Switch entities associated switch ports. A first Device 1 for DeviceL2Switch is associated with VLAN 212, a second Device1 is associated with VLAN 213, and a third Device 1 is associated with VLAN 211. The model manager associates the first and second Devices 1 with eth100/1/1 port and associates the third Device 1 with eth100/1/2.
For each port/port group, the model manager records the connected numbered network interfaces, such as VNICS for virtual machines and physical NICS for physical machines. These are indirectly associated with the DeviceL2Switch.
In operation 250C, the model manager periodically updates the layer 2 network devices. For example, the model manager may aggregate DeviceL2Switches with the same layer 2 identifier (VLAN, VxLAN etc.) and create distinct logical layer 2 network entities which are primarily identified by the layer 2 identifier. The model manager also may compute different properties of the layer 2 network entity using various techniques. For example, the model manager may derive network addresses from the aggregation of the numbered network interfaces with all associated DeviceL2Switches and ports.
Operation 250D accumulates data associated with the different devices in the layer 2 network. For example, analytics engine 122 (FIG. 1) may monitor and calculate packet rates, packet drops, memory utilization, CPU utilization, or the like, or any combination thereof, specifically associated with the layer 2 network.
Operation 250E may display data associated with the layer 2 network and operation 250F may display a visualization of the layer 2 network. For example, the search engine may identify and display performance and configuration data for the layer 2 network and/or the entities in the layer 2 network in response to a search query. In another example, the analytics engine, model manager and/or search engine may periodically and/or automatically monitor and identify information related to the layer 2 network and display certain events to the user, such as a layer 2 network failure. In operation 250E, the visualization manager may display a topology for the layer 2 network and any associated endpoints. The visualization manager also may visually display any events associated with the topology.
FIG. 11 depicts another example of how analytics engine 122 and model manager 230 update data models and/or data model documents. At time T0 network 200 is operating in a first state 266. Model manager 230 generates a set or version V1 of documents 232 containing data models for the entities in network 200. Model manager 230 may have generated documents 232 and the associated data models either automatically or via user configuration.
Analytics engine 122 or the data models may identify events associated with the entities in network 200. As mentioned above, the events may be related to any performance, configuration, relationship, property, etc. for any entity in network 200. In this example, analytics engine 122 detects an event 268 at time T1 associated with VM1. In one example, event 268 indicates VM1 increased CPU utilization above a predetermined threshold.
Event 268 is relayed to model manager 230. Model manager 230 generates a second document version 232B_V2 for data model VM1. Model manager 230 assigns an ending timestamp T1 to the first document version 232B_V1 and assigns a starting timestamp T1 to second document version 232B_V2. Model manager 230 may associate subsequent data for virtual machine model VM1 with document version 232B_V2.
Event 268 might not affect other entities in network 200. Therefore, model manager 230 may continue to associate performance and configuration data for the unaffected entities in network 200 with existing first document versions 232_V1. For example, analytics engine 122 may continue to accumulate performance data for virtual machine VM2 and associate the performance data with document 232G_V 1. Document version 232G_V1 has a starting timestamp T0 and a currently open-ended ending timestamp.
At a next time T2, analytics engine 122 detects another event 270 that reconfigures network 200 by removing virtual firewall VFW. Model manager 230 determines the relationships have changed for entities VM1 and SW1 and generates new document versions 232B_V3 and 262D_V2, respectively. Model manager 230 also assigns an ending timestamp T2 to the second version of document 232B_V2 and the first version of document 232D_V1.
New document versions 232B_V3 and 232D_V2 are assigned starting timestamps T2 corresponding to the detection of event 270. The relationships for VM1 and SW1 in document versions 232B_V3 and 232D_V2, respectively, no longer include connections to VFW. For example, the new relationships may identify VM1 connected directly to SW1.
Other entities may or may not be affected by events 268 or 270. For example, model manager 230 may determine that the removal of VFW does not affect configuration data in the model for VM2. Therefore, model manager 230 does not create a new version of document 232G. However, at a later time, analytics engine 122 may detect a performance change associated with VM2, such as a substantial increase in CPU utilization due to a denial of service (DNS) attack. The increased CPU utilization may cause analytics engine 122 to send a trigger 264 to model manager 230. In response, model manager 230 may create a new document version 232G_V2 for VM2 with a starting time associated with the increased CPU utilization.
Analytics engine 122 constantly monitors the topology and communications between VM1 and VM2. Any changes in the topology or communications may trigger model manager 230 to create new document versions for the data models affected by the changes. For example, a change in the packet transmit/drop rules may cause a dramatic increase in the number of packets dropped by virtual firewall VFW. Model manager 230 may create a new version of document 232C in response to the rule change.
Model manager 230 also may identify the performance and configuration data most important to network 200. For example, virtual firewall VFW may have hundreds of packet transmit/drop rules. However, only a small subset of rules may be associated with communications between virtual machines VM1 and VM2. Analytics engines 122 may only monitor for changes in the subset of packet drop rules in VFW associated with communications between VM1 and VM2 in network 200. Analytics engine 122 might not create a new version of document 232C for other rule changes in VFW. In another example, analytics engine 122 also may only monitor a subset of routing tables in router RTR that impact VM1 and VM2 or other entities within network 200.
In another example, the relevant context for virtual firewall VFW may comprise endpoints VM1 and VM2 and the network interfaces involved in the network path between VM1 and VM2. Model manager 230 identifies the security groups where endpoints VM1 and VM2 are members. The security groups can be defined at various levels, such as VM, network interface, subnet etc. The rules that refer to these security groups are identified and filtered based on the packet flows that are running on the relevant endpoints, such as Hyper-Text Transfer Protocol (HTTP). The above referenced data modeling scheme is highly scalable since model manager 230 only needs to generate new versions of documents 232 for the data models affected by detected events.
Analytics engine 122 comprises a suite of programs that generate triggers 264 that determine when model manager 230 generates new document versions or updates data models. Triggers 264 may include scheduled triggers that execute on a periodic basis or aperiodic triggers that execute based on events, such as changes in the model configuration data. Analytics engine 122 also may generate triggers 264 for lower level events, such as when a VM moves from one host to another host. Analytics engine 122 also may generate triggers 264 in response to data model additions, updates, or deletions within network 200. Analytics engine 122 also may generate ad-hoc triggers 264 in response to user actions or inputs. Analytics engine 122 also may generate triggers 264 based on events raised by third party programs and vendor systems, such as events associated with alarms or health checks.
Analytics engine 122 may detect configuration inconsistencies using the data models 120 in FIG. 8. For example, analytics engine 122 may detect a speed/MTU mismatch across NICs, switch ports, router NICs, etc. Analytics engine 122 also may detect events associated with the topology of network 200. For example, analytics engine 122 may generate a trigger 264 in response to a missing VTEP or in response to IP addressing related layer 2 network problems. Analytics engine 122 also may confirm all node addresses in network 200 are in the same subnet as the layer 2 subnet or confirm all nodes in network 200 have the same gateway and point to an IP address for the same router.
Analytics engine 122 also may check for configuration and runtime inconsistencies such as inconsistent server and router ARP entries, network black holes, and valid ARP entries across layer 2 MAC addresses. Analytics engine 122 may monitor data from any variety of sources, such as programs/vendor systems and use the data to detect higher level events. Analytics engine 122 may also take into account user feedback from search queries and other user inputs for troubleshooting network 200.
The models, model schemas, or schemas and the data, data models, data model schemas, and/or storage schemas may together be referred to as datacenter entity information, and the datacenter entity information described herein is vast and changing frequently as described, for example, with reference to FIG. 11 depicting analytics engine 122 and model manager 230 updating data models and/or data model documents.
FIG. 12 is a flow diagram of a datacenter entity information storage method 300 by which datacenter entity information may be stored in a compressed data format. For purposes or illustration, storage method 300 is described with reference to data model documents described with reference to FIG. 11, but is similarly applicable to models, model schemas, or schemas and the data, data models, data model schemas, and/or storage schemas in other formats.
Process step 302 indicates that an initial or version 1 datacenter entity information (e.g., set of documents containing data models for the entities in network) is generated at an initial time. The initial or version 1 datacenter entity information (e.g., set of documents and associated data models) may be generated automatically on a pre-scheduled basis or via user configuration, for example.
Process step 304 indicates that the initial or version 1 datacenter entity information is stored as a compressed frame-of-reference of datacenter entity information. For example, initial or version 1 datacenter entity information may be compressed in accordance with any lossless data compression format such as, for example, run-length encoding or Lempel-Ziv compression. For example, the initial datacenter entity information may be referred to as Object(initial).
Process step 306 indicates that new datacenter entity information is generated. For example, the new datacenter entity information may be referred to as Object(new).
Process step 308 indicates an inquiry whether the new datacenter entity information corresponds to a next successive version of the initial datacenter entity information or to new initial datacenter entity information. For example, changes to the datacenter entity information detected for each of a predetermined number of time intervals after the initial time may correspond to a next successive version of the initial datacenter entity information, and changes after the last of the predetermined number of time intervals after the initial time may correspond to a new initial datacenter entity information. In another implementation, the changes to the datacenter entity information may correspond to new initial datacenter entity information if the current initial datacenter entity information together with its successive versions amount to at least a determined extent of data (e.g., data storage) or processing. If the new datacenter entity information corresponds to a next successive version of the initial datacenter entity information, process step 308 proceeds to process step 310. If the new datacenter entity information corresponds to new initial datacenter entity information, process step 308 returns to process step 302.
Process step 310 indicates that changes are identified between the new datacenter entity information and a combination or sum of the initial datacenter entity information and any preceding changes in datacenter entity information. (It will be appreciated that in a first occurrence of step 308 for a set of initial datacenter entity information there will be no preceding changes in datacenter entity information.) For example, the identified changes may be referred to as Delta(current) and the combination or sum of the initial datacenter entity information and any preceding changes in datacenter entity information may be referred to as Object(initial)+Delta(preceding1)+Delta(preceding2)+ . . . Delta(precedingN), for N-number of preceding changes to the datacenter entity information, such that:

Delta(current)=Object(New)−[Object(initial)+Delta(preceding1)+Delta(preceding2)+ . . . Delta(precedingN)]

Process step 312 indicates that the changes identified between the new datacenter entity information and the initial datacenter entity information (e.g., Delta(current)) are stored as compressed delta coding of datacenter entity information with the initial datacenter entity information and the preceding changes (e.g., Delta(preceding1), Delta(preceding2), . . . Delta(precedingN), for N-number of preceding changes). For example, next successive version of datacenter entity information corresponds to the changes or “delta” in the datacenter entity information from the immediately preceding version. The changes or “delta” in the datacenter entity information from the immediately preceding version may be compressed in accordance with any lossless data compression format such as, for example, run-length encoding or Lempel-Ziv compression. Process step 310 returns to process step 308. Process step 312 return to process step 306.
Datacenter entity information storage method 300 functions in a manner analogous to MPEG encoding of video images and provides significant compression of the vast amount of datacenter entity information and also provides with “containerized” user access to time ranges of based upon each initial or version 1 set of datacenter entity information. The reference frame represented by each initial or version 1 set of datacenter entity information, together with the delta-encoded changes corresponding to the initial or version 1 set of datacenter entity information, provides or corresponds to a container of datacenter entity information for a selected time period.
It will be appreciated that the example illustrated by FIG. 11 is merely illustrative and that datacenter entity information storage method 300 is applicable to any or all of the physical or virtual entities included in a datacenter, their operations, interconnections, etc. For example, the virtual entities may include machines, virtual firewalls, logical switches, virtual switches, Vteps, etc. The physical entities may include hosts, switches, routers, load balancers, firewalls, etc. Each set of datacenter entity information represents, describes or characterizes the datacenter entities at a sample time and the successive sets of datacenter entity information at successive sample times indicating changes in the datacenter entities since preceding sample times. Examples of predefined information for exemplary entities are listed in Table 1A-1C:










TABLE 3A







Virtual 





Distributed



Virtual Machine
NSX Firewall
Router
Host







Name
Applicable Firewall 
Name
Name



Rules Count and 





Details as




VLAN Names
Seq ID
NSX Manager
Cluster Name




IP/Name



Logical Switch
Name
Routing 
VLAN Name


names

Interfaces





Count and 





Details



Default Gateway
Source
Interface 
MTU Values


Address

Names



IP Address
Destination
IP Addresses
Power State


Network 
Services
Network
Virtual 


Addresses


Machine





Count


Host name
Action
Operational 
VMKNIC 




Status
Count


CPU Count

MTU
Physical NIC





Count


Memory

Interface Speed
Network





Addresses


Virtual NIC 


Operating 


Count


System Name


Operating 

Route Count
Firewall Status


System Name





Power State

Routing Table
vCenter 




Details
Manager


Data Store 

Network



Name





Resource Pool

Next Hop



Name





Security Group

Source Protocol



Names





Firewall Status

Interface Name



vCenter 

VNI



Manager





IP/Name



















TABLE 3B





VXLAN





Network/Logic


Distributed Virtual 


al Switch/

Physical NIC
Port Group


Virtual Wire
VLAN Network
(PNIC)
(DVPG)







Name
Name
Name
Name


Segment ID
VLAN ID
Operational
VxLAN Name




Status



Network
Network Address
MTU Value
Uplink Status


Address





Virtual
Virtual Machine 
Duplexing
Promiscuous 


Machine Count
Count
Status
Mode


Underlay
Host Names
Administrative
vCenter Manager


VLAN ID

Status
IP/Name


Underlay
Distributed Virtual




Subnet
Switch Name




Host Names
Distributed Virtual 





Port Group Name




VTEPs



















TABLE 3C







L3 Switch Device
Edge Router









Name
Name



Manager IP/Name
IP Addresses



Routing Interfaces Count and Details
Firewall Status



Interface Names
Next Hops



IP Addresses
Power Status



Network
CPU Count



Operational Status
Memory



MTU
Operational Statuses



Interface Speed
MTU Values



Route Count
NSX Manager IP/Name



Routing Table Name and Details




Network
Route Count



Next Hop
Routing Table Details



Source Protocol
Network



Interface Name
Next Hop



VNI
Source Protocol




Interface Name




VNI










FIG. 13 is a flow diagram of a datacenter entity information storage method 400 by which datacenter entity information may be stored in a compressed data format. Datacenter entity information storage method 400 functions as an alternative to or substitute for datacenter entity information storage method 300. For purposes or illustration, storage method 400 is described with reference to data model documents described with reference to FIG. 11, but is similarly applicable to models, model schemas, or schemas and the data, data models, data model schemas, and/or storage schemas in other formats.
Process step 402 indicates that an initial or version 1 datacenter entity information (e.g., set of documents containing data models for the entities in network) is generated at an initial time. The initial or version 1 datacenter entity information (e.g., set of documents and associated data models) may be generated automatically on a pre-scheduled basis or via user configuration, for example.
Process step 404 indicates that the initial or version 1 datacenter entity information is stored as a compressed frame-of-reference of datacenter entity information. For example, initial or version 1 datacenter entity information may be compressed in accordance with any lossless data compression format such as, for example, run-length encoding or Lempel-Ziv compression. For example, the initial datacenter entity information may be referred to as Object(initial).
Process step 406 indicates that new datacenter entity information is generated. For example, the new datacenter entity information may be referred to as Object(new).
Process step 408 indicates an inquiry whether the new datacenter entity information corresponds to a next successive version of the initial datacenter entity information or to new initial datacenter entity information. For example, changes to the datacenter entity information detected for each of a predetermined number of time intervals after the initial time may correspond to a next successive version of the initial datacenter entity information, and changes after the last of the predetermined number of time intervals after the initial time may correspond to a new initial datacenter entity information. In another implementation, the changes to the datacenter entity information may correspond to new initial datacenter entity information if the current initial datacenter entity information together with its successive versions amount to at least a determined extent of data (e.g., data storage) or processing. If the new datacenter entity information corresponds to a next successive version of the initial datacenter entity information, process step 408 proceeds to process step 410. If the new datacenter entity information corresponds to new initial datacenter entity information, process step 408 returns to process step 402.
Process step 410 indicates that changes are identified between the new datacenter entity information and the immediately preceding datacenter entity information, which may be referred to as Object(prior). (It will be appreciated that in a first occurrence of step 408 the immediately preceding datacenter entity information Object(prior) will be the initial datacenter entity information.) The identified changes may be referred to as Delta(preceding1) and may be represented as:

Delta(preceding1)=Object(prior)−Object(New)

and the changes previously identified between each preceding new datacenter entity information and its immediately preceding datacenter entity information, which changes were previously designated Delta(preceding1), Delta(preceding2), . . . Delta(precedingN), for N-number sets of preceding new datacenter entity information, are incrementally redesignated Delta(preceding2), Delta(preceding3), . . . Delta(precedingN+1) to accommodate the new Delta(preceding1).

Process step 412 indicates that the new datacenter entity information Object(new) is compressed and stored, for example in accordance with any lossless data compression format such as, for example, run-length encoding or Lempel-Ziv compression.
Process step 414 indicates that changes relative to the preceding of datacenter entity information, Delta(preceding1) are stored as compressed delta coding, as were preceding datacenter entity information changes now designated Delta(preceding2), Delta(preceding3), . . . Delta(preceding+1). The changes could be stored along with Object(new) or separately with the relevant timestamp. The changes or “delta” in the datacenter entity information from the immediately succeeding version may be compressed in accordance with any lossless data compression format such as, for example, run-length encoding or Lempel-Ziv compression. Process step 412 returns to process step 406. It will be appreciated, however, that in an alternative implementation steps 410 and 412 could be performed together so that the new datacenter entity information Object(new) and all the associated change information Delta(precedingX) could be stored together to enable compression of data from a whole-series perspective. Moreover, new datacenter entity information Object(new), with or without all the associated change information Delta(precedingX), could be stored in place the preceding datacenter entity information Object(prior) to reduce the data storage requirements.
Datacenter entity information storage method 400 functions in a manner analogous to datacenter entity information storage method 300 and provides significant compression of the vast amount of datacenter entity information and also provides with “containerized” user access to time ranges of datacenter entity information. Datacenter entity information storage method 400 differs from datacenter entity information storage method 300 in that storage method 400 utilizes as its reference frame the latest, newest, or most recent datacenter entity information and delta-encodes preceding changes relative to the later reference frame or information. Each such terminal reference frame, represented by the final datacenter entity information, together with the preceding delta-encoded changes, provides or corresponds to a container of datacenter entity information for a selected time period.
It will be appreciated that the example illustrated by FIG. 11 is merely illustrative and that datacenter entity information storage method 400 is applicable to any or all of the physical or virtual entities included in a datacenter, their operations, interconnections, etc. For example, the virtual entities may include machines, virtual firewalls, logical switches, virtual switches, Vteps, etc. The physical entities may include hosts, switches, routers, load balancers, firewalls, etc. Each set of datacenter entity information represents, describes or characterizes the datacenter entities at a sample time and the successive sets of datacenter entity information at successive sample times indicating changes in the datacenter entities since preceding sample times. Examples of predefined information for exemplary entities are listed in Table 1A-1C:
FIG. 14 shows a computing device 1000 that may be used for operating the datacenter management system and performing any combination of processes discussed above. The computing device 1000 may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In other examples, computing device 1000 may be a personal computer (PC), a tablet, a Personal Digital Assistant (PDA), a cellular telephone, a smart phone, a web appliance, or any other machine or device capable of executing instructions 1006 (sequential or otherwise) that specify actions to be taken by that machine.
While only a single computing device 1000 is shown, the computing device 1000 may include any collection of devices or circuitry that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the operations discussed above. Computing device 1000 may be part of an integrated control system or system manager, or may be provided as a portable electronic device configured to interface with a networked system either locally or remotely via wireless transmission.
Processors 1004 may comprise a central processing unit (CPU), a graphics processing unit (GPU), programmable logic devices, dedicated processor systems, micro controllers, or microprocessors that may perform some or all of the operations described above. Processors 1004 may also include, but may not be limited to, an analog processor, a digital processor, a microprocessor, multi-core processor, processor array, network processor, etc.
Some of the operations described above may be implemented in software and other operations may be implemented in hardware. One or more of the operations, processes, or methods described herein may be performed by an apparatus, device, or system similar to those as described herein and with reference to the illustrated figures.
Processors 1004 may execute instructions or “code” 1006 stored in any one of memories 1008, 1010, or 1020. The memories may store data as well. Instructions 1006 and data can also be transmitted or received over a network 1014 via a network interface device 1012 utilizing any one of a number of well-known transfer protocols.
Memories 1008, 1010, and 1020 may be integrated together with processing device 1000, for example RAM or FLASH memory disposed within an integrated circuit microprocessor or the like. In other examples, the memory may comprise an independent device, such as an external disk drive, storage array, or any other storage devices used in database systems. The memory and processing devices may be operatively coupled together, or in communication with each other, for example by an I/O port, network connection, etc. such that the processing device may read a file stored on the memory.
Some memory may be “read only” by design (ROM) by virtue of permission settings, or not. Other examples of memory may include, but may be not limited to, WORM, EPROM, EEPROM, FLASH, etc. which may be implemented in solid state semiconductor devices. Other memories may comprise moving parts, such a conventional rotating disk drive. All such memories may be “machine-readable” in that they may be readable by a processing device.
“Computer-readable storage medium” (or alternatively, “machine-readable storage medium”) may include all of the foregoing types of memory, as well as new technologies that may arise in the future, as long as they may be capable of storing digital information in the nature of a computer program or other data, at least temporarily, in such a manner that the stored information may be “read” by an appropriate processing device. The term “computer-readable” may not be limited to the historical usage of “computer” to imply a complete mainframe, mini-computer, desktop, wireless device, or even a laptop computer. Rather, “computer-readable” may comprise storage medium that may be readable by a processor, processing device, or any computing system. Such media may be any available media that may be locally and/or remotely accessible by a computer or processor, and may include volatile and non-volatile media, and removable and non-removable media.
Computing device 1000 can further include a video display 1016, such as a liquid crystal display (LCD) or a cathode ray tube (CRT)) and a user interface 1018, such as a keyboard, mouse, touch screen, etc. All of the components of computing device 1000 may be connected together via a bus 1002 and/or network.
For the sake of convenience, operations may be described as various interconnected or coupled functional blocks or diagrams. However, there may be cases where these functional blocks or diagrams may be equivalently aggregated into a single logic device, program or operation with unclear boundaries.
It will be obvious to those having skill in the art that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the following claims.

## Claims

1. A method, comprising:
obtaining datacenter entity information associated with a datacenter at an initial time, wherein the datacenter entity information includes physical connections and hierarchal relationships of physical entities of the datacenter, and hierarchal relationships of virtual entities of the datacenter;
compressing and storing the datacenter entity information as a datacenter entity frame of reference;
obtaining datacenter entity information changes associated with the datacenter at times other than the initial time, wherein the datacenter entity information changes includes changes to the physical connections and the hierarchal relationships of the physical entities of the datacenter or changes to the hierarchal relationships of the virtual entities of the datacenter;
compressing and storing the datacenter entity information changes with respect to the datacenter entity frame of reference;
receiving a natural language based search query including a phrase or an abbreviation for information associated with managing operation of the datacenter;
identifying a portion of the datacenter entity information or the datacenter entity information changes relevant to the natural language based search query;
identifying physical connections and hierarchal relationships of the physical entities of the datacenter relevant to the natural language based search query;
identifying hierarchal relationships of the virtual entities of the datacenter relevant to the natural language based search query;
identifying an intent of the natural language based search query based on the identified physical connections and hierarchal relationships of the physical entities and the identified hierarchal relationships of the virtual entities;
performing a natural language based search of data pertaining to operation of the data center based on the intent of the natural language based search query; and
generating results of the search other than results from a keyword based search.

2. The method of claim 1, wherein compressing the datacenter entity information includes compressing the datacenter entity information with a lossless data compression format.

3. The method of claim 1 further comprising:
detecting subsequent datacenter entity information changes associated with the datacenter at times other than the initial time;
compressing and storing the subsequent datacenter entity information changes as a subsequent datacenter entity frame of reference; and
compressing and storing the datacenter entity information changes with respect to the subsequent datacenter entity frame of reference.

4. The method of claim 1, wherein compressing the datacenter entity information changes includes delta coding of the datacenter entity information changes with respect to the datacenter entity frame of reference.

5. The method of claim 1, further comprising:
identifying a time period associated with the natural language based search query; and
identifying the portion of the datacenter entity information or the datacenter entity information changes relevant to the identified time period.

6. The method of claim 1, providing a datacenter dictionary containing keywords, sentence structures, and grammar associated with the datacenter, wherein the grammar associates an abbreviation with a part of speech including a noun or a verb,
wherein the datacenter entity information includes the datacenter dictionary, and
wherein the datacenter entity information changes includes changes to the datacenter dictionary.

7. The method of claim 1, further comprising:
identifying a network configured in the datacenter; and
visualizing a topology for the network.

8. The method of claim 7, wherein visualizing the topology includes displaying a series of layers representing at least one of the physical entities and the virtual entities associated with the network.

9. The method of claim 1, wherein the results of the search include a state of a network, and
wherein the method further comprises:
storing the state of the network in a collaboration database; and
enabling access and manipulation of the state of the network by different users.

10. A method, comprising:
obtaining first datacenter entity information associated with a datacenter at a first time, wherein the datacenter entity information includes physical connections and hierarchal relationships of physical entities of the datacenter, and hierarchal relationships of virtual entities of the datacenter;
obtaining second datacenter entity information at a second time subsequent to the first time, wherein the second datacenter entity information includes changes to the physical connections and the hierarchal relationships of the physical entities of the datacenter or changes to the hierarchal relationships of the virtual entities of the datacenter;
compressing and storing the second datacenter entity information as a datacenter entity frame of reference;
identifying differences in the second datacenter entity information relative to the first datacenter entity information;
compressing and storing the differences;
receiving a natural language based search query including a phrase or an abbreviation for information associated with managing operation of the datacenter;
identifying a portion of the first datacenter entity information or the second datacenter entity information relevant to the natural language based search query;
identifying physical connections and hierarchal relationships of the physical entities of the datacenter relevant to the natural language based search query;
identifying hierarchal relationships of the virtual entities of the datacenter relevant to the natural language based search query;
identifying an intent of the natural language based search query based on the identified physical connections and hierarchal relationships of the physical entities and the identified hierarchal relationships of the virtual entities;
performing a natural language based search of data pertaining to operation of the data center based on the intent of the natural language based search query; and
generating results of the search other than results from a keyword based search.

11. The method of claim 10, wherein compressing the second datacenter entity information includes compressing the second datacenter entity information with a lossless data compression format.

12. The method of claim 10, wherein compressing the differences includes delta coding of the differences with respect to the datacenter entity frame of reference.

13. The method of claim 10, further comprising:
identifying a time period associated with the natural language based search query; and
identifying the portion of the first datacenter entity information or the second datacenter entity information relevant to the identified time period.

14. The method of claim 10, providing a datacenter dictionary containing keywords, sentence structures, and grammar associated with the datacenter, wherein the grammar associates an abbreviation with a part of speech including a noun or a verb,
wherein the first datacenter entity information includes the datacenter dictionary, and
wherein the second datacenter entity information includes changes to the datacenter dictionary.

15. The method of claim 10, further comprising:
identifying a network configured in the datacenter; and
visualizing a topology for the network.

16. A non-transitory machine-readable medium storing instructions executable by a processing resource to cause a computing system to:
obtain datacenter entity information associated with a datacenter at an initial time, wherein the datacenter entity information includes physical connections and hierarchal relationships of physical entities of the datacenter, and hierarchal relationships of virtual entities of the datacenter;
compress and store the datacenter entity information as a datacenter entity frame of reference;
obtain a datacenter entity information changes associated with the datacenter at times other than the initial time, wherein the datacenter entity information changes includes changes to the physical connections and the hierarchal relationships of the physical entities of the datacenter or changes to the hierarchal relationships of the virtual entities of the datacenter;
compress and store the datacenter entity information changes with respect to the datacenter entity frame of reference;
receive a natural language based search query including a phrase or an abbreviation for information associated with managing operation of the datacenter;
identify a portion of the datacenter entity information or the datacenter entity information changes relevant to the natural language based search query;
identify physical connections and hierarchal relationships of the physical entities of the datacenter relevant to the natural language based search query;
identify hierarchal relationships of the virtual entities of the datacenter relevant to the natural language based search query;
identifying an intent of the natural language based search query based on the identified physical connections and hierarchal relationships of the physical entities and the identified hierarchal relationships of the virtual entities;
perform a natural language based search of data pertaining to operation of the data center based on the intent of the natural language based search query; and
generate results of the search other than results from a keyword based search.

17. The medium of claim 16, further comprising instructions to compress the datacenter entity information with a lossless data compression format.

18. The medium of claim 16, further comprising instructions to:
detect subsequent datacenter entity information changes associated with the datacenter at times other than the initial time;
compress and store the subsequent datacenter entity information changes as a subsequent datacenter entity frame of reference; and
compress and store the datacenter entity information changes with respect to the subsequent datacenter entity frame of reference.

19. The medium of claim 16, further comprising instructions to delta code the datacenter entity information changes with respect to the datacenter entity frame of reference.

20. The medium of claim 16, further comprising instructions to:
identify a time period associated with the natural language based search query; and
identify the portion of the datacenter entity information or the datacenter entity information changes relevant to the identified time period.