CN119211001A

CN119211001A - A solution for IPMI console session playback

Info

Publication number: CN119211001A
Application number: CN202411271573.8A
Authority: CN
Inventors: 李丹; 荆留清
Original assignee: Zhejiang Jiuzhou Future Information Technology Co ltd
Current assignee: Zhejiang Jiuzhou Future Information Technology Co ltd
Priority date: 2024-09-11
Filing date: 2024-09-11
Publication date: 2024-12-27
Anticipated expiration: 2044-09-11
Also published as: CN119211001B

Abstract

The invention discloses a solution for session playback of an IPMI console, which relates to the technical field of computer system management, and comprises the following steps of S1, initializing a session, S2, capturing session data, S2, capturing all input and output data streams by monitoring an IPMI interface in real time by the system, S3, encrypting and compressing the captured data by using an advanced encryption standard AES to ensure data security, and applying a formula E=AES (D, K), wherein K is a secret key, and E is encrypted data. The invention ensures the safety of the data by encrypting the captured data by using an advanced encryption standard, reduces the consumption of storage space by compressing the encrypted data by adopting a GZIP algorithm, and enhances the capability of fault prediction and event analysis by introducing a machine learning algorithm and a deep learning algorithm.

Description

Solution method for session playback of IPMI console

Technical Field

The invention relates to the technical field of computer system management, in particular to a solution method for session playback of an IPMI console.

Background

In current data center and server management environments, administrators often need to remotely access a server's console for troubleshooting and maintenance. Real-time monitoring of console session content can present difficulties due to network latency or connectivity issues.

Through retrieval, the application proposal of China patent application No. 202210579567.3 discloses a network equipment central control intelligent management system and a method, which are characterized by comprising a central control module, wherein the central control module comprises an anti-intrusion control unit, an asset management module, a network management module, an automatic task unit, a distributed terminal control module and a central control module, wherein the central control module is used for uniformly managing inlets of a plurality of data centers, and a user can perform central control management on the states, energy consumption, resource consumption and various instructions of the data centers. The method of the above patent has the disadvantage of having insufficient capabilities for fault prediction and event analysis and yet remains to be improved.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a solution for session playback of an IPMI console.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

A solution for IPMI console session playback, comprising:

S1, initializing a session:

when an administrator initiates an IPMI session, the system initializes an empty data structure D for storing session data;

s2, capturing session data:

Monitoring an IPMI interface in real time by the system, and capturing all input and output data streams;

S3, data encryption and compression:

the method comprises the steps of encrypting captured data by using an advanced encryption standard AES to ensure data security, applying a formula E=AES (D, K), wherein K is a secret key, E is the encrypted data, and compressing the encrypted data by adopting a GZIP algorithm to reduce storage space consumption;

S4, data storage:

Storing the compressed data C in persistent storage equipment;

s5, session playback:

When a playback session is required, loading compressed data C from a storage device, applying a decompression algorithm formula D=GZIP (-1) (C) to obtain original encrypted data E, and using an AES decryption algorithm formula D=AES (-1) (E, K) to obtain original session data;

S6, data processing and playback:

The received data are sequenced and formatted to ensure the correctness of the playback sequence, and the playback speed is synchronized by using the time stamp information T to ensure that the playback speed is consistent with the original session;

s7, performance optimization and intelligent algorithm application:

The method comprises the steps of implementing a caching mechanism, reducing frequent disk read-write operations, improving data loading efficiency, parallel processing data decryption and decompression by using a multithreading technology, optimizing playback starting time, introducing a machine learning algorithm to perform pattern recognition on session data, automatically classifying and marking key events, analyzing historical session data by using a deep learning algorithm, and predicting potential system faults and maintenance requirements;

S8, data insight and report:

And extracting keywords in the session by applying a natural language processing algorithm to generate an operation log abstract.

Preferably, in the step S1, the initializing the session specifically includes:

When an administrator initiates a remote session through IPMI, the system automatically generates a unique session ID;

creating an empty data structure D for the session ID for storing session data;

Initializing the timestamp logger ensures that each event in the session can be accurately logged.

Preferably, in the step S2, capturing session data specifically includes:

the system configures a network interface and monitors IPMI requests and responses in real time;

for each request and response, recording a time stamp, and storing session data and metadata thereof into a data structure D;

it is ensured that the captured data includes text commands, execution results and any error information.

Preferably, in the step S3, the data encryption and compression specifically includes:

using AES encryption algorithm to encrypt the captured data by adopting a key K;

the formula e=aes (D, K) is applied, where E represents an encrypted data block;

and the GZIP compression algorithm is applied to E, so that the data size is reduced, and the storage and the transmission are convenient.

Preferably, in the step S4, the data storage specifically includes:

Storing the compressed data C in persistent storage equipment;

Indexing each session ID with a corresponding data file for quick retrieval;

And the stored data is backed up regularly, so that the data is prevented from being lost.

Preferably, in the step S5, the session playback specifically includes:

when a playback session is required, the user selects a specific session ID through an interface;

loading compressed data C from a storage device and applying GZIP decompression algorithm formula d=gzip (-1) (C);

the original session data is obtained using the AES decryption algorithm formula d=aes (-1) (E, K).

Preferably, in the step S6, the data processing and playback specifically includes:

the received data are sequenced and formatted, and are synchronized according to the time stamp information T;

Ensuring that the data is played back according to the real sequence of the occurrence of the session;

and calculating playback performance in real time, and ensuring no delay play.

Preferably, in the step S7, the data insight and report specifically includes:

generating a system use condition and a performance report through analysis of playback data;

extracting keywords in the session by using a natural language processing algorithm to generate an operation log abstract;

visual data analysis results are provided, so that an administrator is helped to better understand the running state of the system;

The key words are extracted based on TF-IDF, and the specific formula is as follows:

Wherein:

w is a word;

d is a document;

D is the collection of all documents;

TF (w, d) is the frequency of word w in document d;

N is the total number of documents in the document collection;

the |{ D ε D: w ε D } | is the number of documents containing word w.

Preferably, in the step S7, the application of the performance optimization and intelligent algorithm is specifically as follows:

the caching mechanism is implemented, so that frequent disk read-write operations are reduced, and the data loading efficiency is improved;

data decryption and decompression are processed in parallel by using a multithreading technology, and playback starting time is optimized;

Introducing a machine learning algorithm to perform pattern recognition on session data, and automatically classifying and marking key events;

the historical session data is analyzed using a deep learning algorithm to predict potential system failures and maintenance requirements.

Preferably, in the performance optimization and intelligent algorithm application, when the mode identification is performed, the performance optimization and intelligent algorithm application is performed based on a support vector machine, specifically:

Wherein:

x is an input vector, i.e., a feature vector of the current session data;

x _i is the eigenvector of the ith training data point;

y _i is the class label of the ith training data point;

a _i is Lagrangian multiplier, obtained by training;

k (x _i, x) is a kernel function used to calculate the similarity between two data points;

b is a bias term for the decision boundary;

n is the number of training samples.

The beneficial effects of the invention are as follows:

1. the invention ensures the safety of the data by encrypting the captured data by using an advanced encryption standard, reduces the consumption of storage space by compressing the encrypted data by adopting a GZIP algorithm, and enhances the capability of fault prediction and event analysis by introducing a machine learning algorithm and a deep learning algorithm.

2. The invention improves the data loading efficiency and the playback starting time by implementing a caching mechanism and a multithreading technology, and generates a system service condition and a performance report by analyzing the playback data.

Drawings

Fig. 1 is a flowchart of a solution for session playback of an IPMI console according to the present invention.

Detailed Description

The technical scheme of the invention is further described in detail below with reference to the specific embodiments.

Example 1:

A solution for IPMI console session playback, comprising:

S1, initializing a session:

s2, capturing session data:

S3, data encryption and compression:

S4, data storage:

storing the compressed data C in a persistent storage device, such as a hard disk or SSD;

s5, session playback:

S6, data processing and playback:

s7, data insight and report:

and extracting keywords in the session by applying a Natural Language Processing (NLP) algorithm to generate an operation log abstract.

In S1, the initializing the session specifically includes:

creating an empty data structure D for the session ID for storing session data;

In S2, capturing session data specifically includes:

In S3, the data encryption and compression specifically includes:

In S4, the data storage specifically includes:

Indexing each session ID with a corresponding data file for quick retrieval;

In the step S5, the session playback specifically includes:

In the step S6, the data processing and playback specifically includes:

and calculating playback performance in real time, and ensuring no delay play.

In S7, the data insight and report specifically includes:

Wherein:

w is a word;

d is a document;

D is the collection of all documents;

TF (w, d) is the frequency of word w in document d;

N is the total number of documents in the document collection;

the |{ D ε D: w ε D } | is the number of documents containing word w.

Example 2:

A solution for IPMI console session playback, comprising:

S1, initializing a session:

s2, capturing session data:

S3, data encryption and compression:

S4, data storage:

s5, session playback:

S6, data processing and playback:

s7, performance optimization and intelligent algorithm application:

The method comprises the steps of performing a caching mechanism, reducing frequent disk read-write operations, improving data loading efficiency, using a multithreading technology to process data decryption and decompression in parallel, optimizing playback starting time, introducing a machine learning algorithm to perform pattern recognition on session data, automatically classifying and marking key events such as login failure, configuration change and the like, analyzing historical session data by using the deep learning algorithm, and predicting potential system faults and maintenance requirements;

S8, data insight and report:

In S7, the application of the performance optimization and intelligent algorithm is specifically:

Introducing a machine learning algorithm to perform pattern recognition on session data, and automatically classifying and marking key events such as login failure, configuration change and the like;

During pattern recognition, the pattern recognition is performed based on a support vector machine, for example, recognition of key events such as login failure or configuration change is specifically:

Wherein:

x is an input vector, i.e., a feature vector of the current session data;

x _i is the eigenvector of the ith training data point;

y _i is the class label of the ith training data point;

a _i is Lagrangian multiplier, obtained by training;

b is a bias term for the decision boundary;

n is the number of training samples.

When analyzing historical session data and predicting potential system faults and maintenance requirements, the method is realized based on a long-term and short-term memory network, and specifically comprises the following steps:

h_t＝LSTM(x_t,h_t-1,c_t-1)

Wherein:

x _t is the input at time t;

h _t is the hidden state at time t;

h _t-1 is the hidden state at time t-1;

c _t-1 is the cell state at time t-1.

The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.

Claims

1. A solution for IPMI console session playback, characterized by comprising:

S1: Initialize the session:

When an administrator starts an IPMI session, the system initializes an empty data structure D to store session data;

S2: Capture session data:

The system monitors the IPMI interface in real time and captures all input and output data streams;

S3: Data encryption and compression:

The captured data is encrypted using the Advanced Encryption Standard (AES) to ensure data security; the formula E = AES (D, K) is applied, where K is the key and E is the encrypted data; the encrypted data is compressed using the GZIP algorithm to reduce storage space consumption;

S4: Data Storage:

Store the compressed data C in a persistent storage device;

S5: Session playback:

When the session needs to be replayed, the compressed data C is loaded from the storage device; the decompression algorithm formula D = GZIP^(-1)(C) is applied to obtain the original encrypted data E; the AES decryption algorithm formula D = AES^(-1)(E,K) is used to obtain the original session data;

S6: Data processing and playback:

Sort and format the received data to ensure the correct playback order; synchronize the playback speed using the timestamp information T to ensure that the playback speed is consistent with the original session;

S7: Performance optimization and intelligent algorithm application:

Implement a caching mechanism to reduce frequent disk read and write operations and improve data loading efficiency; use multi-threading technology to parallelize data decryption and decompression to optimize playback startup time; introduce machine learning algorithms to perform pattern recognition on session data, automatically classify and mark key events; use deep learning algorithms to analyze historical session data and predict potential system failures and maintenance needs;

S8: Data Insights and Reports:

Generate system usage and performance reports by analyzing playback data; apply natural language processing algorithms to extract keywords in conversations and generate operation log summaries.

2. A solution for replaying an IPMI console session according to claim 1, characterized in that in S1, initializing the session specifically includes:

Create an empty data structure D for the session ID to store session data;

Initializes the timestamp recorder to ensure that every event in the session can be accurately recorded.

3. A solution for IPMI console session playback according to claim 1, characterized in that in S2, capturing session data specifically comprises:

The system configures the network interface and monitors IPMI requests and responses in real time;

For each request and response, record the timestamp and store the session data and its metadata in data structure D;

Make sure the captured data includes the text command, execution results, and any error messages.

4. A solution for IPMI console session playback according to claim 1, characterized in that in S3, data encryption and compression specifically include:

The captured data is encrypted using the AES encryption algorithm and the key K;

The formula E=AES(D,K) is applied, where E represents the encrypted data block;

Apply the GZIP compression algorithm to E to reduce the data size for easy storage and transmission.

5. A solution for IPMI console session playback according to claim 1, characterized in that in S4, data storage specifically includes:

Store the compressed data C in a persistent storage device;

Index each session ID and the corresponding data file for quick retrieval;

Back up stored data regularly to prevent data loss.

6. A solution for IPMI console session playback according to claim 1, characterized in that in S5, session playback specifically includes:

When a session needs to be replayed, the user selects a specific session ID through the interface;

Load the compressed data C from the storage device and apply the GZIP decompression algorithm formula D = GZIP^(-1)(C);

Use the AES decryption algorithm formula D = AES^(-1)(E,K) to obtain the original session data.

7. A solution for IPMI console session playback according to claim 1, characterized in that in S6, data processing and playback specifically include:

Sort and format the received data and synchronize it according to the timestamp information T;

Ensure that data is played back in the actual order in which the session occurred;

Playback performance is calculated in real time to ensure lag-free playback.

8. A solution for IPMI console session playback according to claim 1, characterized in that in S7, data insight and reporting specifically include:

Generate system usage and performance reports by analyzing playback data;

Apply natural language processing algorithms to extract keywords from conversations and generate operation log summaries;

Provide visual data analysis results to help administrators better understand the system operation status;

Extract keywords based on TF-IDF. The specific formula is:

in:

w is a word;

d is the document;

D is the set of all documents;

TF(w,d) is the frequency of word w in document d;

N is the total number of documents in the document collection;

|{d∈D:w∈d}| is the number of documents containing word w.

9. A solution to IPMI console session playback according to claim 8, characterized in that in S7, the performance optimization and intelligent algorithm application are specifically:

Implement a cache mechanism to reduce frequent disk read and write operations and improve data loading efficiency;

Use multi-threading technology to parallelize data decryption and decompression, optimizing playback startup time;

Introducing machine learning algorithms to perform pattern recognition on conversation data, automatically classify and mark key events;

Analyze historical session data using deep learning algorithms to predict potential system failures and maintenance needs.

10. A solution to IPMI console session playback according to claim 9, characterized in that, in the performance optimization and intelligent algorithm application, when performing pattern recognition, it is performed based on a support vector machine, specifically:

in:

x is the input vector, i.e., the feature vector of the current session data;

_xi is the feature vector of the ith training data point;

_yi is the category label of the i-th training data point;

a _i is the Lagrange multiplier, obtained through training;

K( _xi , x) is the kernel function used to calculate the similarity between two data points; b is the bias term of the decision boundary; n is the number of training samples.