FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR PERFORMING AUTOMATIC
ANALYSIS OF CRASH EVENTS OF NETWORK NODES”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr.
Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in
which it is to be performed.
2
METHOD AND SYSTEM FOR PERFORMING AUTOMATIC ANALYSIS
OF CRASH EVENTS OF NETWORK NODES
FIELD OF INVENTION
5
[0001] The present disclosure generally relate to network performance
management systems. More particularly, embodiments of the present disclosure
relate to methods and systems for performing automatic analysis of crash events of
network nodes in a network environment.
10
BACKGROUND
[0002] The following description of the related art is intended to provide
background information pertaining to the field of the disclosure. This section may
15 include certain aspects of the art that may be related to various features of the
present disclosure. However, it should be appreciated that this section is used only
to enhance the understanding of the reader with respect to the present disclosure,
and not as admissions of the prior art.
20 [0003] Wireless communication technology has rapidly evolved over the past few
decades, with each generation bringing significant improvements and
advancements. The first generation of wireless communication technology was
based on analog technology and offered only voice services. However, with the
advent of the second-generation (2G) technology, digital communication and data
25 services became possible, and text messaging was introduced. 3G technology
marked the introduction of high-speed internet access, mobile video calling, and
location-based services. The fourth-generation (4G) technology revolutionized
wireless communication with faster data speeds, better network coverage, and
improved security. Currently, the fifth-generation (5G) technology is being
30 deployed, promising even faster data speeds, low latency, and the ability to connect
multiple devices simultaneously. With each generation, wireless communication
3
technology has become more advanced, sophisticated, and capable of delivering
more services to its users.
[0004] The network nodes available in the present wireless communication systems
5 may be implemented in servers which may be prone to malfunction or crash in many
conditions that may be referred to as ‘kernel panic events’ for the purposes of this
disclosure. A 'kernel crash dump' refers to a portion of the contents of volatile
memory that is copied to memory disk, when triggered, whenever the execution of
the kernel is disrupted. The crash dump files can be analyzed for the purposes of
10 debugging and determining the cause of a crash.
[0005] In an existing solution, ‘kdump’ is a service which provides a crash dumping
mechanism. kdump uses the kexec system call to boot into the second kernel (a
capture kernel) without rebooting and then captures the contents of the crashed
15 kernel’s memory and saves it into a file. However, the existing solutions there is no
mechanism that is able to generate the crash dump files automatically in an event
of kernel panic events.
[0006] Thus, there exists an imperative need in the art to provide a method and a
20 system for automatically generating crash dump files of servers of network nodes,
which the present disclosure aims to address.
SUMMARY
25 [0007] This section is provided to introduce certain aspects of the present disclosure
in a simplified form that are further described below in the detailed description.
This summary is not intended to identify the key features or the scope of the claimed
subject matter.
30 [0008] An aspect of the present disclosure may relate to a method for performing
automatic analysis of crash events of network nodes in a network environment. The
4
method includes determining, by a determining unit, a communicative connectivity
between at least a target network node and an automation server. The method further
includes selecting, by a selecting unit, at least an analysis network node. The
method further includes creating, by a creating unit via the automation server, a
5 crash dump wherein the crash dump relates to generating a set of crash data of the
target network node in an instance when the target network node experiences a crash
event. The method further includes executing, by an executing unit via the
automation server, and in response to a crash event in the target network node, the
crash dump Finally, the method includes storing, by a storing unit via the
10 automation server, the generated set of crash data in at least the analysis network
node.
[0009] In an exemplary aspect of the present disclosure, the method further
comprises storing, by the storing unit, in a database, a backup of configuration of at
15 least the target network node and the associated crash dump, prior to the instance
when at least the target network node experiences the crash event.
[0010] In an exemplary aspect of the present disclosure, the method further
comprises performing, by a performing unit, a sanity check of the stored set of crash
20 data in at least the analysis network node.
[0011] In an exemplary aspect of the present disclosure, the method further
comprises generating, by a generating unit, a notification indicative of a status of
the stored set of crash data in at least the analysis network node.
25
[0012] In an exemplary aspect of the present disclosure, at least one of a valid sanity
of the stored set of crash data.
[0013] In an exemplary aspect of the present disclosure, the set of crash data
30 comprises at least a copy of a memory of at least the target network node at the
instance of the crash event of at least the target network node.
5
[0014] In an exemplary aspect of the present disclosure, the crash event
corresponds to a kernel crash of at least the target network node.
5 [0015] In an exemplary aspect of the present disclosure, at least the analysis
network node is adapted to store data.
[0016] Another aspect of the present disclosure may relate to a system for
performing automatic analysis of crash events of network nodes in a network
10 environment. The system comprises a determining unit configured to determine a
communicative connectivity between at least a target network node and an
automation server. The system further comprises a selecting unit configured to
select at least an analysis network node. The system further comprises a creating
unit configured to create, via the automation server, a crash dump, wherein the crash
15 dump relates to generating a set of crash data of the target network node in an
instance when the target network node experiences a crash event. The system
further comprises an executing unit configured to execute, via the automation
server, and in response to a crash event in the target network node, the crash dump.
Finally, a storing unit configured to store, via the automation server, the generate
20 set of crash data in at least the analysis network node.
[0017] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instruction for performing automatic
analysis of crash events of network nodes in a network environment, the
25 instructions include executable code which, when executed by one or more units of
a system, causes a determining unit to determine a communicative connectivity
between at least a target network node and an automation server. The instructions
when executed further causes a selecting unit to select at least an analysis network
node. The instructions when executed further causes a creating unit to create, via
30 the automation server, a crash dump, wherein the crash dump relates to generating
a set of crash data of the target network node in an instance when the target network
6
node experiences a crash event. The instructions when executed further causes an
executing unit to execute, via the automation server, and in response to a crash event
in the target network node, the crash dump. Finally, the instructions when executed
causes a storing unit to store, via the automation server, the generate set of crash
5 data in at least the analysis network node.
OBJECTS OF THE DISCLOSURE
[0018] Some of the objects of the present disclosure, which at least one
10 embodiment disclosed herein satisfies are listed herein below.
[0019] It is an object of the present disclosure to provide a system and a method for
performing automatic analysis of crash events of network nodes in a network
environment.
15
[0020] It is another object of the present disclosure to provide a solution that is able
to boot the network node server, in an event of a kernel crash, from the context of
another kernel that reserves a small amount of memory in the server.
20 [0021] It is yet another object of the present disclosure to provide a solution that
facilitates analysis of crash dump files to determine the exact cause of the system
failure.
[0022] It is yet another object of the present disclosure to provide a solution that
25 facilitates achieving zero or minimal downtime for network nodes in case of kernel
crash of the servers on which the nodes are implemented.
BRIEF DESCRIPTION OF THE DRAWINGS
30 [0023] The accompanying drawings, which are incorporated herein, and constitute
a part of this disclosure, illustrate exemplary embodiments of the disclosed methods
7
and systems in which like reference numerals refer to the same parts throughout the
different drawings. Components in the drawings are not necessarily to scale,
emphasis instead being placed upon clearly illustrating the principles of the present
disclosure. Also, the embodiments shown in the figures are not to be construed as
5 limiting the disclosure, but the possible variants of the method and system
according to the disclosure are illustrated herein to highlight the advantages of the
disclosure. It will be appreciated by those skilled in the art that disclosure of such
drawings includes disclosure of electrical components or circuitry commonly used
to implement such components.
10
[0024] FIG. 1 illustrates an exemplary block diagram of a computing device upon
which the features of the present disclosure may be implemented in accordance with
exemplary implementation of the present disclosure.
15 [0025] FIG. 2 illustrates an exemplary block diagram of a system for performing
automatic analysis of crash events of network nodes in a network environment, in
accordance with exemplary implementations of the present disclosure.
[0026] FIG. 3 illustrates a method flow diagram for performing automatic analysis
20 of crash events of network nodes in a network environment in accordance with
exemplary implementations of the present disclosure.
[0027] FIG. 4 illustrates a flow diagram for performing automatic analysis of crash
events of network nodes in a network environment, in accordance with exemplary
25 implementations of the present disclosure.
[0028] FIG. 5 illustrates an exemplary process flow diagram for performing
automatic analysis of crash events of network nodes in a network environment, in
accordance with the exemplary implementations of the present disclosure.
30
8
[0029] FIG. 6 illustrates an exemplary process flow diagram for performing
automatic analysis of crash events of network nodes in a network environment, in
accordance with exemplary implementations of the present disclosure.
5 [0030] The foregoing shall be more apparent from the following more detailed
description of the disclosure.
DETAILED DESCRIPTION
10 [0031] In the following description, for the purposes of explanation, various
specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter may each be used independently of one
15 another or with any combination of other features. An individual feature may not
address any of the problems discussed above or might address only some of the
problems discussed above.
[0032] The ensuing description provides exemplary embodiments only, and is not
20 intended to limit the scope, applicability, or configuration of the disclosure. Rather,
the ensuing description of the exemplary embodiments will provide those skilled in
the art with an enabling description for implementing an exemplary embodiment.
It should be understood that various changes may be made in the function and
arrangement of elements without departing from the spirit and scope of the
25 disclosure as set forth.
[0033] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these
30 specific details. For example, circuits, systems, processes, and other components
9
may be shown as components in block diagram form in order not to obscure the
embodiments in unnecessary detail.
[0034] Also, it is noted that individual embodiments may be described as a process
5 which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure
diagram, or a block diagram. Although a flowchart may describe the operations as
a sequential process, many of the operations may be performed in parallel or
concurrently. In addition, the order of the operations may be re-arranged. A process
is terminated when its operations are completed but could have additional steps not
10 included in a figure.
[0035] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
15 aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
20 description or the claims, such terms are intended to be inclusive—in a manner
similar to the term “comprising” as an open transition word—without precluding
any additional or other elements.
[0036] As used herein, a “processing unit” or “processor” or “operating processor”
25 includes one or more processors, wherein processor refers to any logic circuitry for
processing instructions. A processor may be a general-purpose processor, a special
purpose processor, a conventional processor, a digital signal processor, a plurality
of microprocessors, one or more microprocessors in association with a Digital
Signal Processing (DSP) core, a controller, a microcontroller, Application Specific
30 Integrated Circuits, Field Programmable Gate Array circuits, any other type of
integrated circuits, etc. The processor may perform signal coding data processing,
10
input/output processing, and/or any other functionality that enables the working of
the system according to the present disclosure. More specifically, the processor or
processing unit is a hardware processor.
5 [0037] As used herein, “a user equipment”, “a user device”, “a smart-user-device”,
“a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”,
“a wireless communication device”, “a mobile communication device”, “a
communication device” may be any electrical, electronic and/or computing device
or equipment, capable of implementing the features of the present disclosure. The
10 user equipment/device may include, but is not limited to, a mobile phone, smart
phone, laptop, a general-purpose computer, desktop, personal digital assistant,
tablet computer, wearable device or any other computing device which is capable
of implementing the features of the present disclosure. Also, the user device may
contain at least one input means configured to receive an input from unit(s) which
15 are required to implement the features of the present disclosure.
[0038] As used herein, “storage unit” or “memory unit” refers to a machine or
computer-readable medium including any mechanism for storing information in a
form readable by a computer or similar machine. For example, a computer-readable
20 medium includes read-only memory (“ROM”), random access memory (“RAM”),
magnetic disk storage media, optical storage media, flash memory devices or other
types of machine-accessible storage media. The storage unit stores at least the data
that may be required by one or more units of the system to perform their respective
functions.
25
[0039] As used herein “interface” or “user interface refers to a shared boundary
across which two or more separate components of a system exchange information
or data. The interface may also be referred to a set of rules or protocols that define
communication or interaction of one or more modules or one or more units with
30 each other, which also includes the methods, functions, or procedures that may be
called.
11
[0040] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor, a
5 digital signal processor (DSP), a plurality of microprocessors, one or more
microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
circuits (FPGA), any other type of integrated circuits, etc.
10 [0041] As used herein, the crash events are critical boot fault events that prevents
the system from functioning normally. It occurs when the kernel of the core of the
system, undergoes an unrecoverable error during boot or operation.
[0042] As used herein, the network node refers to the connection point among
15 network devices such as routers, printers, or switches that can receive and send data
from one endpoint to the other.
[0043] As used herein the transceiver unit include at least one receiver and at least
one transmitter configured respectively for receiving and transmitting data, signals,
20 information or a combination thereof between units/components within the system
and/or connected with the system.
[0044] As discussed in the background section, the current known solutions have
several shortcomings. The present disclosure aims to overcome the above25 mentioned and other existing problems in this field of technology by providing
method and system for performing automatic analysis of crash events of network
nodes in a network environment.
[0045] FIG. 1 illustrates an exemplary block diagram representation of 5th
30 generation core (5GC) network architecture, in accordance with exemplary
implementation of the present disclosure. As shown in fig. 1, the 5GC network
12
architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session
Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
an Authentication Server Function (AUSF) [112], a Network Slice Specific
5 Authentication and Authorization Function (NSSAAF) [114], a Network Slice
Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122],
a Unified Data Management (UDM) [124], an application function (AF) [126], a
User Plane Function (UPF) [128], a data network (DN) [130], wherein all the
10 components are assumed to be connected to each other in a manner as obvious to
the person skilled in the art for implementing features of the present disclosure.
[0046] Radio Access Network (RAN) [104] is the part of a mobile
telecommunications system that connects user equipment (UE) [102] to the core
15 network (CN) and provides access to different types of networks (e.g., 5G network).
It consists of radio base stations and the radio access technologies that enable
wireless communication.
[0047] Access and Mobility Management Function (AMF) [106] is a 5G core
20 network function responsible for managing access and mobility aspects, such as UE
registration, connection, and reachability. It also handles mobility management
procedures like handovers and paging.
[0048] Session Management Function (SMF) [108] is a 5G core network function
25 responsible for managing session-related aspects, such as establishing, modifying,
and releasing sessions. It coordinates with the User Plane Function (UPF) for data
forwarding and handles IP address allocation and QoS enforcement.
[0049] Service Communication Proxy (SCP) [110] is a network function in the 5G
30 core network that facilitates communication between other network functions by
13
providing a secure and efficient messaging service. It acts as a mediator for servicebased interfaces.
[0050] Authentication Server Function (AUSF) [112] is a network function in the
5 5G core responsible for authenticating UEs during registration and providing
security services. It generates and verifies authentication vectors and tokens.
[0051] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and
10 authorization services specific to network slices. It ensures that UEs can access only
the slices for which they are authorized.
[0052] Network Slice Selection Function (NSSF) [116] is a network function
responsible for selecting the appropriate network slice for a UE based on factors
15 such as subscription, requested services, and network policies.
[0053] Network Exposure Function (NEF) [118] is a network function that exposes
capabilities and services of the 5G network to external applications, enabling
integration with third-party services and applications.
20
[0054] Network Repository Function (NRF) [120] is a network function that acts
as a central repository for information about available network functions and
services. It facilitates the discovery and dynamic registration of network functions.
25 [0055] Policy Control Function (PCF) [122] is a network function responsible for
policy control decisions, such as QoS, charging, and access control, based on
subscriber information and network policies.
[0056] Unified Data Management (UDM) [124] is a network function that
30 centralizes the management of subscriber data, including authentication,
authorization, and subscription information.
14
[0057] Application Function (AF) [126] is a network function that represents
external applications interfacing with the 5G core network to access network
capabilities and services.
5
[0058] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS
enforcement.
10 [0059] Data Network (DN) [130] refers to a network that provides data services to
user equipment (UE) in a telecommunications system. The data services may
include but are not limited to Internet services, private data network related services.
[0060] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
15 upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method for
performing automatic analysis of crash events of network nodes in a network
environment utilising the system [300]. In another implementation, the computing
20 device [200] itself implements the method for performing automatic analysis of
crash events of network nodes in a network environment using one or more units
configured within the computing device [200], wherein said one or more units are
capable of implementing the features as disclosed in the present disclosure.
25 [0061] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a hardware
processor [204] coupled with the bus [202] for processing information. The
hardware processor [204] may be, for example, a general-purpose microprocessor.
The computing device [200] may also include a main memory [206], such as a
30 random-access memory (RAM), or other dynamic storage device, coupled to the
bus [202] for storing information and instructions to be executed by the processor
15
[204]. The main memory [206] also may be used for storing temporary variables or
other intermediate information during execution of the instructions to be executed
by the processor [204]. Such instructions, when stored in non-transitory storage
media accessible to the processor [204], render the computing device [200] into a
5 special-purpose machine that is customized to perform the operations specified in
the instructions. The computing device [200] further includes a read only memory
(ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
10 [0062] A storage device [210], such as a magnetic disk, optical disk, or solid-state
drive is provided and coupled to the bus [202] for storing information and
instructions. The computing device [200] may be coupled via the bus [202] to a
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
15 displaying information to a computer user. An input device [214], including
alphanumeric and other keys, touch screen input means, etc. may be coupled to the
bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as a
mouse, a trackball, or cursor direction keys, for communicating direction
20 information and command selections to the processor [204], and for controlling
cursor movement on the display [212]. The input device typically has two degrees
of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow
the device to specify positions in a plane.
25 [0063] The computing device [200] may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine.
According to one implementation, the techniques herein are performed by the
30 computing device [200] in response to the processor [204] executing one or more
sequences of one or more instructions contained in the main memory [206]. Such
16
instructions may be read into the main memory [206] from another storage medium,
such as the storage device [210]. Execution of the sequences of instructions
contained in the main memory [206] causes the processor [204] to perform the
process steps described herein. In alternative implementations of the present
5 disclosure, hard-wired circuitry may be used in place of or in combination with
software instructions.
[0064] The computing device [200] also may include a communication interface
[218] coupled to the bus [202]. The communication interface [218] provides a two10 way data communication coupling to a network link [220] that is connected to a
local network [222]. For example, the communication interface [218] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or
a modem to provide a data communication connection to a corresponding type of
telephone line. As another example, the communication interface [218] may be a
15 local area network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
implementation, the communication interface [218] sends and receives electrical,
electromagnetic or optical signals that carry digital data streams representing
various types of information.
20
[0065] The computing device [200] can send messages and receive data, including
program code, through the network(s), the network link [220] and the
communication interface [218]. In the Internet example, a server [230] might
transmit a requested code for an application program through the Internet [228], the
25 ISP [226], the local network [222], a host [224] and the communication interface
[218]. The received code may be executed by the processor [204] as it is received,
and/or stored in the storage device [210], or other non-volatile storage for later
execution.
30 [0066] The computing device [200] encompasses a wide range of electronic
devices capable of processing data and performing computations. Examples of
17
computing devices [200] include, but are not limited only to, personal computers,
laptops, tablets, smartphones, servers, and embedded systems. The devices may
operate independently or as part of a network and can perform a variety of tasks
such as data storage, retrieval, and analysis. Additionally, computing devices [200]
5 may include peripheral devices, such as monitors, keyboards, and printers, as well
as integrated components within larger electronic systems, showcasing their
versatility in various technological applications.
[0067] Referring to FIG. 3, an exemplary block diagram of a system [300] for
10 performing automatic analysis of crash events of network nodes in a network
environment, is shown, in accordance with the exemplary implementations of the
present disclosure. The system [300] comprises at least one determining unit [302],
at least one automation server [304], at least one selecting unit [306], at least one
analysis network node [308], at least one creating unit [310], at least one executing
15 unit [312], at least one storing unit [314], at least one database [316], at least one
performing unit [318], and at least one generating unit [320]. Also, all of the
components/ units of the system [300] are assumed to be connected to each other
unless otherwise indicated below. As shown in the figures all units shown within
the system [300] should also be assumed to be connected to each other. Also, in
20 FIG. 3 only a few units are shown, however, the system [300] may comprise
multiple such units or the system [300] may comprise any such numbers of said
units, as required to implement the features of the present disclosure. Further, in an
implementation, the system [300] may be present in a user device/ user equipment
[102] to implement the features of the present disclosure. The system [300] may be
25 a part of the user device [102]/ or may be independent of but in communication
with the user device [102] (may also referred herein as a UE). In another
implementation, the system [300] may reside in a server or a network entity. In yet
another implementation, the system [300] may reside partly in the server/ network
entity and partly in the user device.
30
18
[0068] The system [300] is configured for performing automatic analysis of crash
events of network nodes in a network environment, with the help of the
interconnection between the components/units of the system [300].
5 [0069] The system [300] comprises a determining unit [302] configured to
determine a communicative connectivity between at least a target network node and
an automation server [304].
[0070] The determining unit [302] is responsible for verifying whether the
10 automation server [304] is capable of establishing and maintaining a
communication link with the target network node, which may be a network function
(NF) in a 5G Core Network (5GCN) or any other network component. For example,
when the determining unit [302] is invoked, it may perform a series of checks, such
as sending and receiving test signals or packets between the target network node
15 and the automation server [304]. The process can involve utilizing protocols like
ICMP (Internet Control Message Protocol) to perform a ping test or checking
TCP/IP connection status by attempting to open a specific port on the target node.
If the determining unit [302] identifies that the connection is stable and responsive,
it confirms that the automation server can communicate with the target node,
20 allowing subsequent tasks such as crash data collection to proceed.
[0071] The determining unit [302] determines the communicative connection in
order to ascertain whether the connectivity between the at least one target network
node and the automation server [304] is successful or not.
25
[0072] In an exemplary aspect, the target network node may include such as but not
limited only to 4G network node, 5G network node etc.
[0073] In an exemplary aspect, the automation server [304] is a jumper server. As
30 used herein, jumper servers (also known as jump box servers) have been used to
safely bypass firewalls and allow cross-network navigation for remote devices.
19
Furthermore, the jumper servers provide practical applications for businesses
wishing to grant remote network access to customers and employees - allowing for
quick integration and interaction.
5 [0074] The system [300] comprises a selecting unit [306] configured to select at
least an analysis network node [308].
[0075] The selecting unit [306] identifies and designates a specific network node
within the system to serve as the analysis network node [308]. The analysis network
10 node [308] may be a server or network function (NF) tasked with receiving,
processing, and storing crash data collected from the target network node.
[0076] The selecting unit [306] selects at least an analysis network node [308]. In
an exemplary aspect, a network administrator selects analysis network node [308]
15 which automatically analyses the crash events of the target network nodes. For
example, in a 5G Core Network (5GCN) environment, where multiple network
nodes operate simultaneously, the selecting unit [306] must choose an analysis
network node that is geographically close to the target node or located within the
same network segment to minimize latency. In this case, if a target node responsible
20 for managing subscriber sessions crashes, the selecting unit [306] will identify a
nearby NF with available resources to store and analyse the crash dump (vmcore)
data. This ensures quick and efficient retrieval of the crash data without overloading
the selected node.
25 [0077] In an exemplary aspect, the network administrator selects at least the
analysis network node [308] using a user interface (UI).
[0078] The system [300] comprises a creating unit [310] configured to create, at
the automation server [304], a crash dump, wherein the crash dump relates to
30 generating a set of crash data of the target network node in an instance when the
target network node experiences a crash event.
20
[0079] The creating unit [310] creates the crash dump at the automation server
[304]. The crash dump generates the set of crash data of the target network node in
the instance when the target network node experiences the crash event. The crash
5 data of the crash events comprises for example Kdump which captures system
memory, process states, call stacks, and CPU register values at the time of a kernel
crash. The crash data further includes loaded kernel modules, system configuration,
and log messages. This data, stored in a vmcore file, helps diagnose the root cause.
10 [0080] The creating unit [310] initiates and configures the crash dump, which is a
specific process designed to capture and generate a detailed memory dump
(vmcore) and other relevant system information when the target node encounters a
failure, such as a kernel panic or unexpected shutdown. For example, in a 5G Core
Network (5GCN) environment, where uninterrupted service is essential, the
15 creating unit [310] will generate a crash dump as soon as a crash event is detected
on a target node, such as a node managing network functions (NFs) responsible for
subscriber data or session management. The crash dump involves gathering a
snapshot of the target node's memory at the time of the crash, including active
processes, system logs, and kernel states. In an exemplary aspect, crash dump acts
20 as a playbook which includes are all scripts associated with crash dump.
[0081] In an exemplary aspect, upon selection of at least the analysis network node
[308] which automatically analyses the crash events of the target network nodes,
the creating unit [310] creates the crash dumps which further generate the set of
25 crash data in the form of crash dump files, in case the crash events i.e. kernel panic
events are experienced, for the selected target network nodes.
[0082] In an exemplary aspect, the crash event corresponds to the kernel crash of
at least the target network node. In an exemplary aspect, crash events are kernel
30 crash that prevents the target network node from continuing its normal operation.
21
The crash occurs when a fatal error prevents the kernel from loading properly,
causing the target network node boot to fail.
[0083] In an exemplary aspect, the set of crash data comprises at least a copy of a
5 memory of at least the target network node at the instance of crash event of at least
the target network node.
[0084] The set of crash data includes at least the copy of the memory of at least the
target network node at the instance it experiences a crash event. In an exemplary
10 aspect, the copy of memory may include memory dumps that exports a memory
image (also known as vmcore) that can be analyzed for the purposes of debugging
and determining the cause of a crash. In an exemplary aspect, the copy of the
memory provides information about the of the target node just before the crash, and
the copy of the memory is further used for determining the cause of the failure of
15 the target network node.
[0085] The system [300] further comprises an executing unit configured to execute,
at the automation server [304], and in response to the crash event in the target
network node, the crashdump.
20
[0086] The executing unit [312] executes the crash dump in response to the crash
event in the target network node at the automation server [304]. Upon the detection
of crash event, the executing unit [312] automatically initiates the execution of the
previously created crash dump by the creating unit [310]. In an exemplary aspect,
25 the crash dump includes pre-defined instructions for handling the crash events.
[0087] Upon detecting a crash event, such as a kernel crash or other critical failure
in the target network node, the executing unit is responsible for triggering the
previously created crash dump. For example, in a 5G Core Network (5GCN)
30 environment, where the system must maintain high availability and reliability, the
executing unit would immediately activate the crash dump once a crash event is
22
reported by the target network node. The executing unit [312] would begin by
instructing the automation server [304] to access the target node and perform the
crash data capture as defined in the crash dump. The process may include collecting
memory dumps, system logs, and state information from the target node, ensuring
5 that a comprehensive snapshot of the system is captured at the moment of failure.
[0088] In an exemplary aspect, the automation server [304] i.e. jumper servers may
trigger the automatic execution of crash dump remotely on a desired target node.
10 [0089] The system [300] further comprises a storing unit [314] configured to store,
at the automation server [304], the generated set of crash data in at least the analysis
network node [308].
[0090] The storing unit [314] stores the generated set of crash data in at least the
15 analysis network node using the automation server [304] ensuring that the data
moved to the specific analysis network nodes where it can be further analysed and
examined thereby facilitating effective troubleshooting and resolving of system
issues.
20 [0091] Once the executing unit completes the collection of crash data, the storing
unit [314] ensures that this data is securely transferred and stored in the designated
analysis network node for future analysis. The crash data may include a memory
dump (vmcore), system logs, and other diagnostic information that can be used to
troubleshoot the cause of the crash event in the target network node.
25
[0092] In an exemplary aspect, at least the analysis network node [308] is adapted
to store the set of crash data. In an exemplary aspect, the analysis network node
[308] is configured to store the set of crash data.
30 [0093] The storing unit [314] is further configured to store, in a database [316], a
backup of configuration of at least the target network node and the associated crash
23
dump, prior to the instance when at least the target network node experiences the
crash event.
[0094] Before the target network node experiences a crash event, the storing unit
5 [314] stores the backup of configuration of least target network nodes and the
associated crash dump in the database [316]. This backup of configuration of at
least target network nodes and associated crash dumps allows network
administrators to ascertain the exact state and condition of the node before the target
network node experiences the crash event. This backup of configuration facilitates
10 analysis and troubleshooting of the target network node, in order to determine
configuration issues that contributed to the crash and further ensures that the
recovery can be performed on the target network node based on exact state that
existed prior to the failure.
15 [0095] The system [300] further comprises a performing unit [318] is configured
to perform a sanity check of the stored set of crash data in at least the analysis
network node.
[0096] The performing unit [318] performs the sanity check of the stored set of
20 crash data in at least the analysis network node. In an exemplary aspect, the sanity
check may be performed to check whether the automation task for automatically
generating crash dump is enabled or not, in case target network node experience
crash event. In an exemplary aspect, the sanity check may be referred to as a
successful sanity check in an event the system [300] is able to automatically
25 generate set of crash data, in case of crash events, for automation servers of the
selected target network nodes.
[0097] The system [300] further comprises a generating unit [320] is configured to
generate a notification indicative of a status of the stored set of crash data in at least
30 the analysis network node [308]. Such notifications allow administrators to
24
promptly assess whether the crash data has been successfully stored, whether it
meets predefined sanity checks, or if any errors occurred during the storage process.
[0098] For example, in a network environment such as a 5G Core Network
5 (5GCN), after a crash event occurs and the crash data is stored in the analysis
network node [308], the generating unit [320] will create a notification indicating
the status of the stored data. The notification could confirm that the crash data has
been stored successfully and is ready for analysis, or it might indicate issues such
as incomplete data capture, corruption, or insufficient storage space.
10
[0099] The generating unit [320] generates the notification indictive of the status
of store set of crash data in at least the analysis network node [308].
[0100] In an exemplary aspect, the status comprises a validation status of the stored
15 set of crash data.
[0101] In an exemplary aspect, the status includes valid sanity of the stored crash
data indicates that the stored crash data is complete, and it has been successfully
captured and saved without any corruption or loss of crash data. Once the system
20 [300] validates the sanity of the stored crash data, it can further troubleshoot the
problem more easily and effectively.
[0102] In an exemplary aspect, sanity check refers to a process to check data
discrepancy issues by identifying, analysing and resolving the issues that may help
25 for optimum functioning of the system [300] and reduce downtime specifically
during backend processing of data.
[0103] In an exemplary aspect, the validation of the stored set of crash data has two
phases for validation. One of the phases is used to check configuration and second
30 phase is used for successful generation of core dump.
25
[0104] In an exemplary aspect, the status further includes validation status of the
stored set of crash data may also indicate that the crash data is incomplete, and it
has been unsuccessfully captured in part due to corruption and loss of crash data.
Once the system [300] validates that crash data is incomplete it may not be able to
5 troubleshoot the problem more effectively and efficiently.
[0105] Referring to FIG. 4, an exemplary method [400] flow diagram for
performing automatic analysis of crash events of network nodes in a network
environment, in accordance with exemplary implementations of the present
10 disclosure is shown. In an implementation the method [400] is performed by the
system [300]. Further, in an implementation, the system [300] may be present in a
server device to implement the features of the present disclosure. Also, as shown in
FIG. 4, the method [400] starts at step [402].
15 [0106] At step 404, the method [400] comprises determining, by a determining unit
[302], a communicative connectivity between at least a target network node and an
automation server [304].
[0107] The determining unit [302] is responsible for verifying whether the
20 automation server [304] is capable of establishing and maintaining a
communication link with the target network node, which may be a network function
(NF) in a 5G Core Network (5GCN) or any other network component. For example,
when the determining unit [302] is invoked, it may perform a series of checks, such
as sending and receiving test signals or packets between the target network node
25 and the automation server [304]. The process can involve utilizing protocols like
ICMP (Internet Control Message Protocol) to perform a ping test or checking
TCP/IP connection status by attempting to open a specific port on the target node.
If the determining unit [302] identifies that the connection is stable and responsive,
it confirms that the automation server can communicate with the target node,
30 allowing subsequent tasks such as crash data collection to proceed.
26
[0108] The determining unit [302] determines the communicative connection in
order to ascertain whether the connectivity between the at least one target network
node and the automation server [304] is successful or not.
5 [0109] In an exemplary aspect, the target network node may include such as but not
limited only to 4G network node, 5G network node etc.
[0110] In an exemplary aspect, the automation server [304] is a jumper server. As
used herein, jumper servers (also known as jump box servers) have been used to
10 safely bypass firewalls and allow cross-network navigation for remote devices.
Furthermore, the jumper servers provide practical applications for businesses
wishing to grant remote network access to customers and employees - allowing for
quick integration and interaction.
15 [0111] At step 406, the method [400] comprises selecting, by a selecting unit [306],
at least an analysis network node [308].
[0112] The selecting unit [306] identifies and designates a specific network node
within the system to serve as the analysis network node [308]. The analysis network
20 node [308] may be a server or network function (NF) tasked with receiving,
processing, and storing crash data collected from the target network node.
[0113] The selecting unit [306] selects at least an analysis network node [308]. In
an exemplary aspect, a network administrator selects analysis network node [308]
25 which automatically analyses the crash events of the target network nodes. For
example, in a 5G Core Network (5GCN) environment, where multiple network
nodes operate simultaneously, the selecting unit [306] must choose an analysis
network node that is geographically close to the target node or located within the
same network segment to minimize latency. In this case, if a target node responsible
30 for managing subscriber sessions crashes, the selecting unit [306] will identify a
nearby NF with available resources to store and analyse the crash dump (vmcore)
27
data. This ensures quick and efficient retrieval of the crash data without overloading
the selected node.
[0114] In an exemplary aspect, the network administrator selects at least the
5 analysis network node [308] using a user interface (UI).
[0115] At step 408, the method [400] comprises creating, by a creating unit [310]
at the automation server [304], a crash dump, wherein the crash dump relates to
generating a set of crash data of the target network node in an instance when the
10 target network node experiences a crash event.
[0116] The creating unit [310] creates the crash dump at the automation server
[304]. The crash dump generates the set of crash data of the target network node in
the instance when the target network node experiences the crash event.
15
[0117] The creating unit [310] initiates and configures the crash dump, which is a
specific process designed to capture and generate a detailed memory dump
(vmcore) and other relevant system information when the target node encounters a
failure, such as a kernel panic or unexpected shutdown. For example, in a 5G Core
20 Network (5GCN) environment, where uninterrupted service is essential, the
creating unit [310] will generate a crash dump as soon as a crash event is detected
on a target node, such as a node managing network functions (NFs) responsible for
subscriber data or session management. The crash dump involves gathering a
snapshot of the target node's memory at the time of the crash, including active
25 processes, system logs, and kernel states. In an exemplary aspect, crash dump acts
as a playbook which includes are all scripts associated with crash dump.
[0118] In an exemplary aspect, upon selection of at least the analysis network node
[308] which automatically analyses the crash events of the target network nodes,
30 the creating unit [310] creates the crash dumps which further generate the set of
28
crash data in the form of crash dump files, in case the crash events i.e. kernel panic
events is experienced, for the selected target network nodes.
[0119] In an exemplary aspect, the crash event corresponds to the kernel crash of
5 at least the target network node. In an exemplary aspect, crash events are kernel
crash that prevents the target network node from continuing its normal operation.
The crash occurs when a fatal error prevents the kernel from loading properly,
causing the target network node boot to fail.
10 [0120] In an exemplary aspect, the set of crash data comprises at least a copy of a
memory of at least the target network node at the instance of crash event of at least
the target network node.
[0121] The set of crash data includes at least the copy of the memory of at least the
15 target network node at the instance it experiences a crash event. In an exemplary
aspect, the copy of memory may include memory dumps that exports a memory
image (also known as vmcore) that can be analyzed for the purposes of debugging
and determining the cause of a crash. In an exemplary aspect, the copy of the
memory provides information about the of the target node just before the crash, and
20 the copy of the memory is further used for determining the cause of the failure of
the target network node.
[0122] At step 410, the method [400] comprises executing, by an executing unit
[312] at the automation server [304], and in response to a crash event in the target
25 network node, the crashdump.
[0123] The executing unit [312] executes the crash dump in response to the crash
event in the target network node at the automation server [304]. Upon the detection
of crash event, the executing unit [312] automatically initiates the execution of the
30 previously created crash dump by the creating unit [310]. In an exemplary aspect,
the crash dump includes pre-defined instructions for handling the crash events.
29
[0124] Upon detecting a crash event, such as a kernel crash or other critical failure
in the target network node, the executing unit is responsible for triggering the
previously created crash dump. For example, in a 5G Core Network (5GCN)
5 environment, where the system must maintain high availability and reliability, the
executing unit would immediately activate the crash dump once a crash event is
reported by the target network node. The executing unit [312] would begin by
instructing the automation server [304] to access the target node and perform the
crash data capture as defined in the crash dump. The process may include collecting
10 memory dumps, system logs, and state information from the target node, ensuring
that a comprehensive snapshot of the system is captured at the moment of failure.
[0125] In an exemplary aspect, the automation server [304] i.e. jumper servers may
trigger the automatic execution of crash dump remotely on a desired target node.
15
[0126] At step 412, the method [400] comprises storing, by a storing unit [314] at
the automation server [304], the generated set of crash data in at least the analysis
network node [308].
20 [0127] The storing unit [314] stores the generated set of crash data in at least the
analysis network node using the automation server [304] ensuring that the data
moved to the specific analysis network nodes where it can be further analysed and
examined thereby facilitating effective troubleshooting and resolving of system
issues.
25
[0128] Once the executing unit completes the collection of crash data, the storing
unit [314] ensures that this data is securely transferred and stored in the designated
analysis network node for future analysis. The crash data may include a memory
dump (vmcore), system logs, and other diagnostic information that can be used to
30 troubleshoot the cause of the crash event in the target network node.
30
[0129] In an exemplary aspect, at least the analysis network node is adapted to store
the set of crash data. In an exemplary aspect, the analysis network node is
configured to store the set of crash data.
5 [0130] The method [400] further comprises storing, by the storing unit [314], in a
database [316], a backup of configuration of at least the target network node and
the associated crash dump, prior to the instance when at least the target network
node experiences the crash event.
10 [0131] Before the target network node experiences a crash event, the storing unit
[314] stores the backup of configuration of least target network nodes and the
associated crash dump in the database [316]. This backup of configuration of at
least target network nodes and associated crash dumps allows network
administrators to ascertain the exact state and condition of the node before the target
15 network node experiences the crash event. This backup of configuration facilitates
analysis and troubleshooting of the target network node, in order to determine
configuration issues that contributed to the crash and further ensures that the
recovery can be performed on the target network node based on exact state that
existed prior to the failure.
20
[0132] The method [400] further comprises performing, by a performing unit [318],
a sanity check of the stored set of crash data in at least the analysis network node.
[0133] The performing unit [318] performs the sanity check of the stored set of
25 crash data in at least the analysis unit. In an exemplary aspect, the sanity check may
be performed to check whether the automation task performed for automatically
generating crash dump is enabled, in case target network node experience crash
event. In an exemplary aspect, the sanity check may be referred to as a successful
sanity check in an event the system [300] is able to automatically generate set of
30 crash data, in case of crash events, for automation servers of the selected target
network nodes.
31
[0134] The method [400] further comprises generating, by a generating unit [320],
a notification indicative of a status of the stored set of crash data in at least the
analysis network node [308]. Such notifications allow administrators to promptly
5 assess whether the crash data has been successfully stored, whether it meets
predefined sanity checks, or if any errors occurred during the storage process.
[0135] For example, in a network environment such as a 5G Core Network
(5GCN), after a crash event occurs and the crash data is stored in the analysis
10 network node [308], the generating unit [320] will create a notification indicating
the status of the stored data. The notification could confirm that the crash data has
been stored successfully and is ready for analysis, or it might indicate issues such
as incomplete data capture, corruption, or insufficient storage space.
15 [0136] The generating unit [320] generated the notification indictive of the status
of store set of crash data in at least the analysis network node.
[0137] In an exemplary aspect, the status comprises at least one of a validation
status of the stored set of crash data.
20
[0138] In an exemplary aspect, the status includes validation status of the stored
crash data indicates that the stored crash data is complete, and it has been
successfully captured and saved without any corruption or loss of crash data. Once
the system [300] validates the sanity of the stored crash data, it can further
25 troubleshoot the problem more easily and effectively.
[0139] In an exemplary aspect, the status further includes validation status of the
stored set of crash data indicates the crash data is incomplete, and it has been
unsuccessfully captured in part due to corruption and loss of crash data. Once the
30 system [300] validates that the crash data is incomplete, it may not be able to
troubleshoot the problem more effectively and efficiently.
32
[0140] In an exemplary aspect, sanity check refers to a process to check data
discrepancy issues by identifying, analysing and resolving the issues that may help
for optimum functioning of the system [300] and reduce downtime specifically
during backend processing of data.
5
[0141] In an exemplary aspect, the validation of the stored set of crash data has two
phases for validation. One of the phases is used to check configuration and second
phase is used for successful generation of core dump. In an exemplary aspect, sanity
check refers to a process to check data discrepancy issues by identifying, analysing
10 and resolving the issues that may help for optimum functioning of the system [300]
and reduce downtime specifically during backend processing of data.
[0142] In an exemplary aspect, the validation of the stored set of crash data has two
phases for validation. One of the phases is used to check configuration and second
15 phase is used for successful generation of core dump.
[0143] Thereafter, at step [414], the method [400] is terminated.
[0144] Referring to FIG. 5, an exemplary process [500] flow diagram for
20 performing automatic analysis of crash events of network nodes in a network
environment, is shown, in accordance with the exemplary implementations of the
present disclosure.
[0145] At step 502, the process [500] is initiated at the jumper server [304].
25
[0146] At step 504, the process [500] comprises selecting, at the jumper server
[304] (also referred to herein as automation server [304]), analysis network node
[308] which automatically analyses the crash events of the target network nodes. In
an exemplary aspect, the network administrator selects at least the analysis network
30 node [308] using a user interface (U.I).
33
[0147] At step 506, the process [500] comprises checking hardware models [500a]
of the one or more vendors connected to the selected target network node.
[0148] At step 508, the process [500] comprises taking, at the storing unit [314] in
5 the database [316], backup of configuration data for taking existing configuration.
Before the target network node experiences a crash event, the storing unit [314]
stores the backup of configuration of least target network nodes and the associated
crash dump in the database [316]. This backup of configuration of at least target
network nodes and associated crash dumps allows network administrators to
10 ascertain the exact state and condition of the node before the target network node
experiences the crash event. This backup of configuration facilitates analysis and
troubleshooting of the target network node, in order to determine configuration
issues that contributed to the crash and further ensures that the recovery can be
performed on the target network node based on exact state that existed prior to the
15 failure.
[0149] At step 510, the process [500] comprises storing, by the storing unit [314]
to further enable and runs the Kdump configuration scripts.
20 [0150] At step 512, the process [500]comprises performing, at performing unit
[318], the sanity check of the stored set of crash data in at least the analysis network
node. In an exemplary aspect, the sanity check may be performed to check whether
the automation task for automatically generating crash dump is enabled or not, in
case target network node experience crash event. In an exemplary aspect, the sanity
25 check may be referred to as a successful sanity check in an event the system [300]
is able to automatically generate set of crash data, in case of crash events, for
automation servers of the selected target network nodes.
[0151] Referring to FIG. 6, an exemplary process [600] flow diagram for
30 performing automatic analysis of crash events of network nodes in a network
34
environment, in accordance with exemplary implementations of the present
disclosure is shown.
[0152] At step 602, the process [600] comprises selecting, by the jumper server, the
5 analysis network node [308] automatically analyses the crash events of the target
network nodes. In an exemplary aspect, the network administrator selects at least
the analysis network node [308] using a user interface (U.I).
[0153] At step 604, the process [600] further comprises listing down nodes of the
10 target servers or target network nodes that experiences crash events.
[0154] At step 606, the process [600] further comprises taking existing kdump
configuration backup with automation script. Before the target network node
experiences a crash event, the storing unit [314] stores the backup of configuration
15 of least target network nodes and the associated crash dump in the database [316].
This backup of configuration of at least target network nodes and associated crash
dumps allows network administrators to ascertain the exact state and setup of the
node before the target network node experiences the crash event.
20 [0155] At step 608, the process [600] further comprises executing Kdump
configuration script. In an exemplary aspect, the executing unit [312] executes the
crash dump in response to the crash event in the target network node at the
automation server [304]. Upon the detection of crash event, the executing unit [312]
automatically initiates the execution of the previously created crash dump by the
25 creating unit [310]. In an exemplary aspect, the crash dump includes pre-defined
instructions for handling the crash events. In an exemplary aspect, the automation
server [304] i.e. jumper servers may trigger the automatic execution of crash dump
remotely on a desired target node.
30 [0156] At step 610, the process [600] further comprises validating sanity for
Kdump configuration. In an exemplary aspect, the status includes valid sanity of
35
the stored crash data indicates that the stored crash data is complete, and it has been
successfully captured and saved without any corruption or loss of crash data. Once
the system [300] validates the sanity of the stored crash data, it can further
troubleshoot the problem more easily and effectively.
5
[0157] As used herein, crash dump (also referred to herein as Kdump) is a service
which provides a crash dumping mechanism. kdump uses the kexec system which
calls to boot into the second kernel (a capture kernel) without rebooting and then
captures the contents of the crashed kernel’s memory and saves it into a file.
10
[0158] In an exemplary aspect, the status further includes valid sanity of the stored
set of crash data indicates the crash data is incomplete, and it has been
unsuccessfully captured in part due to corruption and loss of crash data. Once the
system [300] validates the sanity of the stored crash data indicates that crash data is
15 incomplete, it may not be able to troubleshoot the problem more effectively and
efficiently.
[0159] The present disclosure further discloses a non-transitory computer readable
storage medium storing instruction for performing automatic analysis of crash
20 events of network nodes in a network environment, the instructions include
executable code which, when executed by one or more units of a system, causes: a
determining unit to determine a communicative connectivity between at least a
target network node and an automation server. The instructions when executed
further causes a selecting unit to select at least an analysis network node. The
25 instructions when executed further causes a creating unit to create, at the automation
server, a crash Kdump, wherein the crash dump relates to generating a set of crash
data of the target network node in an instance when the target network node
experiences a crash event. The instructions when executed further causes an
executing unit to execute, at the automation server, and in response to the crash
30 event in the target network node, the crash dump. The instructions when executed
36
further causes a storing unit to store, at the automation server, the generated set of
crash data in at least the analysis network node.
[0160] As is evident from the above, the present disclosure provides a technically
5 advanced solution for performing automatic analysis of crash events of network
nodes in a network environment. The present solution for automated validation of
peer-to-peer connections of network nodes enables one to boot the network node
server, in an event of a kernel crash, from the context of another kernel that reserves
a small amount of memory in the server. Further, the present solution facilitates
10 analysis of crash dump files to determine the exact cause of the system failure. Also,
the present solution facilitates achieving zero or minimal downtime for network
nodes in case of kernel crash of the servers on which the nodes are implemented.
[0161] Further, in accordance with the present disclosure, it is to be acknowledged
15 that the functionality described for the various components/units can be
implemented interchangeably. While specific embodiments may disclose a
particular functionality of these units for clarity, it is recognized that various
configurations and combinations thereof are within the scope of the disclosure. The
functionality of specific units as disclosed in the disclosure should not be construed
20 as limiting the scope of the present disclosure. Consequently, alternative
arrangements and substitutions of units, provided they achieve the intended
functionality described herein, are considered to be encompassed within the scope
of the present disclosure.
25 [0162] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and
that many changes can be made to the implementations without departing from the
principles of the present disclosure. These and other changes in the implementations
of the present disclosure will be apparent to those skilled in the art, whereby it is to
30 be understood that the foregoing descriptive matter to be implemented is illustrative
and non-limiting.
37
We Claim:
1. A method for performing automatic analysis of crash events of network nodes
in a network environment, the method comprising:
5 - determining, by a determining unit [302], a communicative connectivity
between at least a target network node and an automation server [304];
- selecting, by a selecting unit [306], at least an analysis network node
[308];
- creating, by a creating unit [310] at the automation server [304], a crash
10 dump, wherein the crash dump relates to generating a set of crash data of
the target network node in an instance when the target network node
experiences a crash event;
- executing, by an executing unit [312] at the automation server [304], and
in response to a crash event in the target network node, the crash dump;
15 and
- storing, by a storing unit [314] at the automation server [304], the
generated set of crash data in at least the analysis network node [308].
2. The method as claimed in claim 1, wherein the method comprises storing, by
20 the storing unit [314], in a database [316], a backup of configuration of at
least the target network node and the associated crash dump, prior to the
instance when at least the target network node experiences the crash event.
3. The method as claimed in claim 1, wherein the method comprises performing,
25 by a performing unit [318], a sanity check of the stored set of crash data in at
least the analysis network node.
4. The method as claimed in claim 3, wherein the method comprises generating,
by a generating unit [320], a notification indicative of a status of the stored
30 set of crash data in at least the analysis network node.
38
5. The method as claimed in claim 4, wherein the status comprises at least one
of a validation status of the stored set of crash data.
5 6. The method as claimed in claim 1, wherein the set of crash data comprises at
least a copy of a memory of at least the target network node at the instance of
the crash event of at least the target network node.
7. The method as claimed in claim 1, wherein the crash event corresponds to a
10 kernel crash of at least the target network node.
8. The method as claimed in claim 1, wherein at least the analysis network node
[308] is adapted to store the set of crash data.
15 9. A system for performing automatic analysis of crash events of network nodes
in a network environment, the system comprising:
- a determining unit [302] configured to:
- determine a communicative connectivity between at least a target
network node and an automation server [304];
20 - a selecting unit [306] configured to:
- select at least an analysis network node [308];
- a creating unit [310] configured to:
- create, at the automation server [304], a crash dump, wherein the
crash dump relates to generating a set of crash data of the target
25 network node in an instance when the target network node
experiences a crash event;
- an executing unit [312] configured to:
- execute, at the automation server [304], and in response to the crash
event in the target network node, the crash dump; and
39
- a storing unit [314] configured to:
- store, at the automation server [304], the generated set of crash data
in at least the analysis network node [308].
5 10. The system as claimed in claim 9, wherein the storing unit [314] is
configured to store, in a database [316], a backup of configuration of at least
the target network node and the associated crash dump, prior to the instance
when at least the target network node experiences the crash event.
10 11. The system as claimed in claim 9, wherein a performing unit [318] is
configured to perform a sanity check of the stored set of crash data in at least
the analysis network node [308].
12. The system as claimed in claim 11, wherein a generating unit [320] is
15 configured to generate a notification indicative of a status of the stored set of
crash data in at least the analysis network node [308].
13. The system as claimed in claim 12, wherein the status comprises at least one
of a validation status of the stored set of crash data.
20
14. The system as claimed in claim 9, wherein the set of crash data comprises at
least a copy of a memory of at least the target network node at the instance of
crash event of at least the target network node.
25 15. The system as claimed in claim 9, wherein the crash event corresponds to a
kernel crash of at least the target network node.
16. The system as claimed in claim 9, wherein at least the analysis network node
[306] is adapted to store the set of crash data.