CN102891864A - Method for acquiring and analyzing credible data of cloud resources based on distributed Agent - Google Patents

Abstract

The present invention proposes a method for obtaining and analyzing trusted data of cloud resources based on distributed agents. When calculating the overall credibility of cloud resources, the distributed agent technology is used to dynamically monitor the service process of cloud computing, and the monitored The obtained context data is used as an important basis for credibility evaluation, which can make the evaluation results more dynamic and real-time, so that the evaluation results are more impressive and credible. The implementation scheme is divided into two levels, the calculation agent layer and the monitoring agent layer. The monitoring proxy layer is responsible for the monitoring of raw data, and the computing proxy layer is responsible for data preprocessing, so that the two layers can work in parallel, greatly improving the execution efficiency of the system.

Description

A Distributed Agent-Based Acquisition and Analysis Method of Cloud Resource Trusted Data

technical field

The invention belongs to the field of distributed computing, and specifically relates to integrating various technologies, such as cloud computing technology, distributed agent (Agent) technology, network monitoring technology, etc., to realize a data acquisition and analysis method for cloud resource credibility evaluation.

Background technique

Cloud computing is the development of distributed processing, parallel processing and grid computing, or the commercial implementation of these computer science concepts. Cloud computing is also an important innovation in the business model of computer resource sharing. Through cloud computing, users will be freed from heavy, complex, and error-prone computer resource management, and only need to focus on business logic. This separation of business logic and computing resources will greatly reduce the complexity of enterprise informatization. More importantly, the service integration and on-demand supply brought by cloud computing will greatly improve the utilization rate of current computing resources, reduce the energy consumption of services, and effectively prevent errors in computing resources.

Why can users hand over the most sensitive data to the cloud service center for management? It is based on the trust of public users in cloud computing services. Therefore, the issue of credibility is the cornerstone of cloud computing.

In the cloud computing environment, due to the use of virtualization technology, the resource and user management methods of the cloud service provider are open and completely distributed. Driven by commercial profits, there will be some fraudulent behaviors in cloud services (resources), which will affect the applications running on the cloud platform. In addition, due to the completely open computing environment, in the absence of an authoritative management center to supervise, there will be some selfish service providers who provide some false resource information and disrupt the operation of the entire cloud system. At the same time, facing the diverse needs of users, the services requested by users may require collaboration among multiple cloud resources, and the prerequisite for collaboration is that cloud resources have a good trustworthy relationship with each other. From the above analysis, it can be seen that trusted management, as an effective new technology of network security, is also a core technology to realize the security of cloud computing resources. And with the continuous popularization of cloud computing, the importance of trustworthiness is gradually increasing, which has become an important factor restricting its development.

This patent proposes a new method for monitoring and analyzing trusted data of cloud computing resources through distributed Agent technology. Through dynamic monitoring and analysis of trusted data related to cloud computing resources, potential malicious behaviors in the cloud computing environment can be discovered, preventing Before it happens.

Contents of the invention

The present invention proposes to monitor and analyze the changes of contexts related to the credibility of various cloud resources through distributed Agent technology. The main cause of hidden dangers. By monitoring the behavior of cloud resources, potential malicious behaviors in the cloud computing environment can be found to prevent problems before they happen. The present invention monitors the service behavior of cloud resources through the distributed Agent, and the monitored context data is used as evidence for resource credibility evaluation and prediction, and the result of the credibility assessment can be used as a certificate for resource scheduling and allocation on the cloud computing platform .

In order to achieve the above purpose, as shown in FIG. 1 , the technical solution of the present invention is divided into two levels, the calculation agent layer and the monitoring agent layer.

Technical scheme of the present invention is divided into following several basic execution steps:

Step 1: The agent manager starts a plurality of distributed Agents to dynamically monitor the service data of different cloud resources; the specific monitoring objects include: memory and central processing unit (CPU) usage, network ports, network traffic, Logs, application scheduling processes, processes and system calls, etc.;

Step 2: Put the monitored context data into the storage device storing the original data;

Step 3: The computing agent layer invokes the monitored raw data through the context event service module, performs data preprocessing, and stores the processed data in the relevant storage device, and the agent manager that manages the behavior context according to the event The degree of urgency can directly trigger the context event service module to re-evaluate the credibility in real time;

Step 4: The trust evaluation module of the cloud computing platform evaluates and predicts the trust degree of cloud resources according to the preprocessed data. In the process of evaluation and prediction, the trusted evaluation module uses the information in the context information database, which is collected through the real-time monitoring of cloud resources by the network monitoring module;

Step 5: According to the calculation reliability evaluation result of the trust evaluation module, the task scheduling and allocation of cloud computing resources is performed.

The present invention has the following technical characteristics:

(1) The agent manager described in step 1 encapsulates the data access and control function interface of the Agent, and is managed by different agent managers according to the different functions of the distributed Agent. At the same time, the agent manager transfers the data generated by the Agent into the context monitoring database;

(2) The original context monitoring database described in step 2 is mainly used to store the service data of cloud computing resources monitored by multiple agents. Percentage data, such as the average time between failures and the number of times to scan important ports, are specific values within a certain range. Decreasing, that is, the smaller the better;

(3) The calculation agent CA described in step 3 is mainly used for data preprocessing. Due to the diversity of representations of behavior monitoring data, in order to facilitate fusion calculations, it is necessary to preprocess the data representations such as normalization, that is, to express them all as dimensionless values that increase in the positive direction in the [0, 1] interval, which is not only convenient for numerical The fusion calculation is also consistent with the scope and direction of the network entity trust degree measurement.

(4) The credibility assessment module described in step 4 is one of the core modules of the entire trust management middleware. In the process of resource credibility assessment and prediction, the credibility assessment module uses the preprocessed context information database information, which can greatly speed up the operation speed of the entire system, thereby improving the operating efficiency of the credibility management system.

(5) The task scheduling and allocation of cloud computing resources described in step 5 is mainly a dynamic resource scheduling strategy based on credibility evaluation, which is different from traditional static resource scheduling strategies and has better real-time performance.

Description of drawings

Figure 1 is a logical framework diagram of a distributed agent-based cloud resource credibility data monitoring and analysis system.

Figure 2 The main monitoring parameters.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

1. Basic hierarchical structure and functional module division of the system

The technical scheme of the present invention is divided into two levels, a calculation agent layer and a monitoring agent layer.

The monitoring agent layer is mainly responsible for collecting common behavioral parameters in the interaction process of cloud resources and cloud users, such as network bandwidth utilization, memory and CPU utilization, and application behavior hidden dangers (including ports, system calls, etc. that may potentially intrude and malicious behaviors) )wait. The monitoring agent layer is composed of various monitoring agents MA (Monitoring-Agents), agent manager and original context monitoring database.

The calculation proxy layer mainly performs data preprocessing calculations. The calculation agent layer is mainly composed of calculation agents (CA, CalculatingAgents), preprocessed context database, context event service and trust evaluation context access interface.

2. Monitor the main monitoring parameters of the proxy layer

The monitoring agent layer is mainly responsible for collecting common behavioral parameters (or indicators) during the interaction between cloud resources and cloud users. These parameters mainly include: CPU utilization, IP transmission efficiency, memory utilization, bandwidth utilization, average throughput, Resource sharing rate, successful job execution rate, bit error rate, IP packet loss rate, link establishment success rate, mean time between failures, site self-defense capability, number of illegal links, number of important port scans, and number of unauthorized attempts, etc. The main monitoring parameters are shown in Figure 2.

3. Calculate the preprocessing method of proxy layer data

The original context data obtained by monitoring the Agent of the agent layer is expressed as a specific value (physical dimension value) or percentage data within a certain range, such as the average time between failures and the number of important port scans, all of which are within a certain range For specific values in , the mean time between failures increases in the positive direction, that is, the larger the better, and the number of times to scan important ports decreases in the positive direction, that is, the smaller the better. Due to the diversity of representations of behavior monitoring data, in order to facilitate fusion calculations, it is necessary to preprocess the data representations such as normalization, that is, to express them all as dimensionless values that increase in the positive direction in the [0, 1] interval, which is not only convenient for numerical Converged computing is also consistent with the range and direction of cloud resource trustworthiness measurements.

For preprocessing, we use the method of data normalization. The specific method is to arrange the monitoring data obtained by the distributed agent according to time, then there are n sets of monitoring data to be processed at a certain time point, and each set of data in the n sets of data is called a monitoring sample. In this way, at time point n, there will be n samples to be processed X={x ₁ , x ₂ ..., x _n } The attribute set of each sample is expressed as x _j = {x _j1 , x _j2 , ..., x _jm }, which can be expressed as a matrix:

In the formula, m is the number of credible evaluation indicators (also called credible attributes).

The matrix X is normalized according to the following publicity, and the normalized matrix is expressed as B=(b _ij ) _m×n :

${\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span>\\ <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; )</span>\\ <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; )</span>\end{array}\right.$

(a)表示_xij是正向递增百分比；(b)表示x_ij是正向递减百分比；(c)表示x_ij是正向递增量纲值；(d)表示x_ij是正向递减量纲值。通过规范化的预处理所有的监控数据都可以转换为[0，1]范围的正向递增值。In the formula

(a) indicates that _xi j is a positively increasing percentage; (b) indicates that x _ij is a positively decreasing percentage; (c) indicates that x _ij is a positively increasing dimensional value; (d) indicates that x _ij is a positively decreasing dimensional value. All monitoring data can be converted to positively increasing values in the [0, 1] range through normalized preprocessing.

4. Calculation of resource credibility by the credibility evaluation module

The credibility assessment module mainly evaluates and calculates the credibility of resources by invoking standardized data, and the calculation results can be used as the basis for dynamic scheduling and allocation of cloud resources. It can be seen from the calculation results of the computing agent layer that after standardized processing, the greater the value of each monitoring data, the greater the contribution of this evidence to the credibility evaluation of cloud computing resources. Therefore, we calculate the overall trustworthiness of a resource as follows:

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}}{\mathrm{<span\; class="notranslate">\; m</span>}}$

In the formula, T _P (n) represents the credibility of resource P at time point n, and m is the number of credibility evaluation indicators.

5. Task scheduling and allocation method of cloud computing resources based on credibility

When a user proposes a resource requirement to the cloud computing service provider, the resource manager of the cloud computing can adopt a task scheduling and allocation method based on the credibility of the cloud computing resource, and the specific method is as follows:

(1) sort the optional resources according to the calculated overall credibility of each cloud resource;

(2) According to the sorting result, select the resource with the largest credibility value, and submit the job to the resource for execution;

(3) Wait for the result of the job, and if it is successfully executed on time, follow-up work such as payment of remuneration, download of the result file, etc., and positive evaluation will be given back, and the trust of resources will be increased according to the relevant algorithm;

(4) If the execution times out or fails, a lower evaluation is given back, the credibility of the resource is reduced according to the relevant algorithm, and the resource is deleted from the ranking list.

Claims (6)

1. The method for obtaining and analyzing trusted data of cloud resources based on distributed Agent is characterized in that, when calculating the overall credibility of cloud resources, distributed Agent technology is used to dynamically monitor the service process of cloud computing, and the The monitored context data is an important basis for credibility evaluation, which can make the evaluation results more dynamic and real-time, so that the evaluation results are more impressive and credible. 2. The method according to claim 1, wherein the implementation scheme is divided into two levels, a calculation agent layer and a monitoring agent layer. The monitoring proxy layer is responsible for the monitoring of raw data, and the computing proxy layer is responsible for data preprocessing, so that the two layers can work in parallel, greatly improving the execution efficiency of the system. 3. The method according to claim 1, wherein the monitoring agent layer is mainly responsible for collecting common behavior parameters in the interaction process of cloud resources and cloud users, and these parameters mainly include: CPU utilization rate, IP transmission efficiency, memory utilization rate , bandwidth utilization, average throughput, resource sharing rate, successful job execution rate, bit error rate, IP packet loss rate, link establishment success rate, mean time between failures, site self-defense capability, number of illegal links, and number of important port scans and the number of unauthorized attempts. 4. The method according to claim 1, characterized in that the calculation agent layer mainly performs data preprocessing calculations. The computing agent layer is mainly composed of computing agent (CA), preprocessed context database, context event service and trust evaluation context access interface. 5. The method according to claim 1, characterized in that, we use the data normalization method for preprocessing, and all monitoring data can be converted into positive incremental values in the range of [0, 1] through normalized preprocessing, and every The greater the value of a monitoring data, the greater the contribution of this evidence to the credibility evaluation of cloud computing resources. 6. The method according to claim 1, characterized in that, when a user proposes a resource requirement to the cloud computing service provider, the resource manager of cloud computing can adopt task scheduling and Allocation method.

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