CN113254146A - Cloud platform service trust value calculation, task scheduling and load balancing system and method - Google Patents
Cloud platform service trust value calculation, task scheduling and load balancing system and method Download PDFInfo
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Abstract
The invention belongs to the technical field of cloud computing, and discloses a cloud platform service trust value computing, task scheduling and load balancing system and method, wherein the cloud platform service trust value computing, task scheduling and load balancing method comprises the following steps: firstly, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy. According to the cloud platform service trust value calculation, task scheduling and load balancing method, the influence of trust attack of a malicious service candidate on task scheduling is considered, the balance between system benefit and energy consumption is comprehensively considered, the cloud task scheduling efficiency can be improved, and the attack of a malicious service provider on the cloud platform is reduced.
Description
Technical Field
The invention belongs to the technical field of cloud computing, and particularly relates to a cloud platform service trust value computing, task scheduling and load balancing system and method.
Background
Currently, with the development of wireless communication and information technology, the cloud has become a new paradigm for providing services on the internet. A service refers to a relationship-based interaction between a service provider and a service consumer that has achieved a particular business objective or solution. With the increasing diversification and adoption of services, a way for a third party providing different services to deploy the services by using a SaaS platform is becoming popular. Many service providers deploy data centers in different regions around the world and provide various services to customers, such as large-scale artificial intelligence-based recommendations, image recognition, natural language processing, big data analysis, and the like.
At present, in the prior art of cloud platform task scheduling, scheduling efficiency is improved based on prediction of request arrival amount, the influence of service quality on scheduling is mainly considered, and a trust value is updated through a large amount of historical behavior data. The drawbacks of these methods are: existing work improves scheduling efficiency based on prediction of request arrival volume, and in fact such prediction is not accurate, if cloud platform routing is not flexible, dynamic requests may be distributed to inappropriate virtual machines, resulting in queue backlog and platform profit loss. And existing work only discusses the impact of quality of service on cloud service scheduling. Nowadays, the problem of energy consumption is also more and more emphasized. Since better quality of service is often at the cost of increased energy consumption, there is a trade-off between the advantages and disadvantages. Meanwhile, in the SaaS platform, dishonest service providers may provide false quality of service to attract more service requests and obtain higher revenue. Today's research into cloud platform trust requires huge storage space to record each service provider's historical behavior, especially when high frequency and dynamic requests occur. Therefore, a lightweight trust update algorithm needs to be designed to reduce memory space while restricting the service provider's fraud.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) existing work improves scheduling efficiency based on prediction of request arrival volume, and in fact such prediction is not accurate, if cloud platform routing is not flexible, dynamic requests may be distributed to inappropriate virtual machines, resulting in queue backlog and platform profit loss.
(2) Existing work only discusses the impact of quality of service on cloud service scheduling, with better quality of service often being at the cost of increased energy consumption.
(3) In SaaS platforms, dishonest service providers may provide a false quality of service to attract more service requests and obtain higher revenues.
The difficulty in solving the above problems and defects is: in a cloud platform, the arrival of service requests is high frequency and dynamic, and if the cloud platform routing is not flexible, dynamic requests may be distributed to inappropriate virtual machines, resulting in queue backlog and platform profit loss. And since better service quality is often at the cost of increased energy consumption, a trade-off between service quality and energy consumption is needed when the cloud platform scheduling problem is researched. Meanwhile, the cloud platform trust value needs to be calculated according to the historical behavior information of the virtual machine service, but as time increases, the information amount of the historical behavior also linearly increases, which easily causes insufficient storage space. There is therefore a need to devise a lightweight trust update algorithm to reduce memory space while constraining the fraud of the service provider.
The significance of solving the problems and the defects is as follows: the cloud task scheduling efficiency can be improved and the attack of malicious service providers to the cloud platform can be reduced by researching the service trust value calculation, task scheduling and load balancing system method of the cloud platform.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a cloud platform service trust value calculation, task scheduling and load balancing system and method.
The invention is realized in such a way that a cloud platform service trust value calculation, task scheduling and load balancing method comprises the following steps:
step one, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue';
step two, the cloud server controls the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment;
and step three, the cloud computing center updates the trust value of the service candidate according to the lightweight trust management strategy.
Further, in step one, the request scheduling policy of "joining the optimal queue" determines the workload of the user request to be scheduled to the virtual machine at each time according to the following formula:
wherein, γijRepresenting a trust value of the virtual machine; lambda [ alpha ]QoSAn influence coefficient indicating a quality of service;
representing the service quality of the virtual machine uploading; qij(t) represents the queue load of the virtual machine at time t; a. theij(t) represents the workload requested by the user at time t; bijRepresenting the relationship between the user request type and the virtual machine service type; v denotes the lyapunov optimization parameter.
Further, in step two, the "real-time dynamic adjustment" energy management policy determines the operation state of the service virtual machine at each time according to the following formula:
wherein Q isij(t) represents the virtual machine queue load at time t; lijRepresenting the processing power of the virtual machine;
representing the running energy consumption of the virtual machine at the moment t; y isijAnd (t) represents the running state of the virtual machine at the time t, 1 represents running, and 0 represents idle.
Further, in step three, the lightweight trust management policy of the cloud computing center updates the trust value of the service candidate by the following formula:
wherein the content of the first and second substances,
a trust value representing the virtual machine is represented,
and
is a measure of historical positive and negative behavior;
represents the amount of positive behavior of the virtual machine at time slot t, which can be calculated as
A service rating/5 at a moment, so that the value ranges from 1, 0.8, 0.6, 0.4, 0.2;
representing the temporal decay of the behaviour, wheredRepresenting the attenuation parameter, tnow-t represents a temporal variation; it can be seen that recent behaviors weigh trust values higher.
Another object of the present invention is to provide a cloud platform service trust value calculation, task scheduling, and load balancing system using the cloud platform service trust value calculation, task scheduling, and load balancing method, where the cloud platform service trust value calculation, task scheduling, and load balancing system includes:
the service request is firstly sent to a decision center when a user wants to request a service from the cloud platform, wherein the service is divided into a plurality of categories, and each type represents one service;
the decision center is responsible for service classification, request distribution and trust management; after a user request is sent to the cloud platform, the decision center searches published services capable of meeting the request functions, and after the service trust values are calculated, the request is sent to a proper server virtual machine;
the cloud server is provided with a plurality of cloud servers, and each server is provided with a plurality of services; each cloud server submits the service QoS calculated by the QoS indexes including the availability, the success rate and the reliability to a decision center so as to facilitate the access node decision center to make corresponding decisions; each cloud server has a manager that manages the services deployed thereon while determining the operational status of the server.
Further, the decision center comprises:
the service classification module is used for classifying the received service request type and the service type deployed in the cloud server;
the request scheduling module is used for scheduling the service request of each time slot to a virtual machine on a proper cloud server for processing;
and the trust management module is used for updating the trust value of the virtual machine on each cloud server.
Further, the cloud server manager includes:
the service management module is used for recording and managing service information of each virtual machine in the cloud server;
and the energy consumption management module is used for managing the energy consumption of each virtual machine on the cloud server.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
firstly, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
firstly, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy.
The invention also aims to provide an information data processing terminal, which is used for realizing the cloud platform service trust value calculation, task scheduling and load balancing system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention provides a method for computing a service trust value, scheduling a task and balancing a load of a cloud platform.A cloud computing decision center receives a user request and then schedules the user request to a proper virtual machine according to a request scheduling strategy of 'adding to an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy. The invention provides a service trust value calculation, task scheduling and load balancing system method of a cloud platform, which considers the influence of trust attack of a malicious service candidate on task scheduling, comprehensively considers the balance between system benefit and energy consumption, and is beneficial to the application of the cloud in a complex scene.
The invention designs a cloud platform task scheduling and trust management mechanism, and the technical difficulty is as follows: in a cloud platform, the arrival of service requests is high frequency and dynamic, and if the cloud platform routing is not flexible, dynamic requests may be distributed to inappropriate virtual machines, resulting in queue backlog and platform profit loss. And since better service quality is often at the cost of increased energy consumption, a trade-off between service quality and energy consumption is needed when the cloud platform scheduling problem is researched. Meanwhile, the cloud platform trust value needs to be calculated according to the historical behavior information of the virtual machine service, but as time increases, the information amount of the historical behavior also linearly increases, which easily causes insufficient storage space. There is therefore a need to devise a lightweight trust update algorithm to reduce memory space while constraining the fraud of the service provider. The significance of the invention is as follows: the cloud task scheduling efficiency can be improved and the attack of malicious service providers to the cloud platform can be reduced by researching the service trust value calculation, task scheduling and load balancing system method of the cloud platform.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for computing a trust value of a cloud platform service, scheduling a task, and balancing a load according to an embodiment of the present invention.
Fig. 2 is a model diagram of a service trust value calculation, task scheduling, and load balancing system mechanism of a cloud platform according to an embodiment of the present invention.
Fig. 3 is a flowchart of an algorithm of a "join best queue" request scheduling policy provided by an embodiment of the present invention.
Fig. 4 is a flowchart of an "real-time dynamic adjustment" energy management policy algorithm according to an embodiment of the present invention.
Fig. 5 is a flowchart of a lightweight trust management policy algorithm according to an embodiment of the present invention.
Fig. 6 is a graph of experimental test results provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a system and a method for computing a service trust value of a cloud platform, scheduling a task and balancing load, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the cloud platform service trust value calculation, task scheduling, and load balancing method provided in the embodiment of the present invention includes the following steps:
s101, after receiving a user request, the cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue';
s102, the cloud server controls the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment;
s103, the cloud computing center updates the trust value of the service candidate according to the lightweight trust management strategy.
The technical solution of the present invention will be further described with reference to the following examples.
The model of the service trust value calculation, task scheduling and load balancing system mechanism of the cloud platform provided by the embodiment of the invention is shown in fig. 2, when a user wants to request a service from the cloud platform, the user firstly sends the request to a decision center. The services are divided into a plurality of categories, each type representing a service; the decision center is responsible for classification of services, request distribution and trust management. After a user request is sent to the cloud platform, a decision center on the cloud platform searches published services which can meet the request functions, and then after the service trust values are calculated, the request is sent to a proper server virtual machine; a plurality of cloud servers are deployed in the cloud, and a plurality of services are deployed on each server. Each cloud server submits the service QoS calculated by the QoS indexes such as availability, success rate, reliability and the like to the decision center so that the access node decision center can make corresponding decisions. Each cloud server has a manager that manages the services deployed thereon while determining the operational status of the server. The decision center comprises a service classification module which is responsible for classifying the types of the received service requests and the types of the services deployed in the cloud server; the request scheduling module is responsible for scheduling the service request of each time slot to a virtual machine on a proper cloud server for processing; and the trust management module is responsible for updating the trust value of the virtual machine on each cloud server. The cloud server manager comprises a service management module which is used for recording and managing service information of each virtual machine in the cloud server; and the energy consumption management module is in charge of managing the energy consumption of each virtual machine on the cloud server.
The flow of the "join optimal queue" request scheduling policy algorithm provided by the embodiment of the present invention is shown in fig. 3, wherein the algorithm inputs include a virtual machine trust value, a service quality influence coefficient, a lyapunov parameter, a user unloading queue length, and a service quality uploaded by a virtual machine; the algorithm output is the workload of the service request scheduling to the virtual machine; the initialization queue length is 0. In implementation, the "join best queue" request scheduling policy determines the amount of work scheduled to the virtual machine at each time by the following equation:
wherein, γijRepresenting a trust value of the virtual machine; lambda [ alpha ]QoSAn influence coefficient indicating a quality of service;
representing the service quality of the virtual machine uploading; qij(t) represents the queue load of the virtual machine at time t; a. theij(t) represents the workload requested by the user at time t; bijRepresenting the relationship between the user request type and the virtual machine service type; v denotes the lyapunov optimization parameter.
The energy management strategy algorithm flow of the real-time dynamic adjustment provided by the embodiment of the invention is shown in fig. 4, wherein the algorithm input comprises the queue length of the virtual machine, the processing capacity of the virtual machine and the energy consumption of the virtual machine; initializing the running state of a virtual machine to be 1; the algorithm output is the running state of the virtual machine; in implementation, the "real-time dynamic adjustment" energy management policy determines the running state of the service virtual machine at each time by the following formula:
wherein Q isij(t) represents the virtual machine queue load at time t; lijRepresenting the processing power of the virtual machine;
representing the running energy consumption of the virtual machine at the moment t; y isijAnd (t) represents the running state of the virtual machine at the time t, 1 represents running, and 0 represents idle.
The algorithm flow of the lightweight trust management policy provided by the embodiment of the invention is shown in fig. 5, wherein the algorithm input comprises the rating of the service at the time t; the trust value of the initialized virtual machine is 1, and in implementation, the running state of the service virtual machine at each moment is determined by the following formula of a lightweight trust management strategy:
wherein the content of the first and second substances,
a trust value representing the virtual machine is represented,
and
is a measure of historical positive and negative behavior.
Represents the amount of positive behavior of the virtual machine at time slot t, which can be calculated as
The service rating/5 at the moment, so that the value ranges from 1, 0.8, 0.6, 0.4, 0.2.
Representing the temporal decay of the behaviour, wheredRepresenting the attenuation parameter, tnow-t represents the time variation. It can be seen that recent behaviors weigh trust values higher.
The experimental test result provided by the embodiment of the invention is shown in fig. 6, and the system for experimental simulation of three conditions is respectively an ideal condition, a non-trust-based condition and a trust-based condition. The ideal situation refers to that no malicious service candidate exists in the cloud platform, the non-trust-based situation refers to that a malicious service candidate exists in the SaaS platform but no measure for preventing the attack of the malicious service candidate exists, and the trust-based situation is the scheme of the invention. FIG. 6(a) reflects the change in the average profit of the system over time. Compared with the non-trust and ideal conditions, the method effectively recovers the loss of the malicious service provider to the system and improves the profit by 15 percent. And the system yield is similar to the ideal state along with the change of time. FIG. 6(b) evaluates the queue backlog of the present invention, reflecting the change in queue backlog over time. Compared with the ideal situation and the non-trust-based situation, the method greatly reduces the influence of malicious nodes on the stability of the system queue, reduces the queue backlog by 80 percent and approaches to the ideal situation, ensures the stability of the queue and realizes load balance. Combining fig. 6(a) and fig. 6(b), it can be seen that the present invention achieves a balance between system profit and queue backlog, thereby minimizing queue backlog while obtaining optimal profit. Fig. 6(c) reflects the change in the node trust values for different trust levels. It can be seen that the present invention effectively and quickly differentiates service providers of different behaviors into different trust levels, while it allows trust values to be in a stable state without high volatility.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A cloud platform service trust value calculation, task scheduling and load balancing method is characterized by comprising the following steps:
after receiving the user request, the cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue';
the cloud server controls the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment;
and the cloud computing center updates the trust value of the service candidate according to the lightweight trust management strategy.
2. The cloud platform service trust value calculation, task scheduling, and load balancing method of claim 1, wherein the request scheduling policy of "joining an optimal queue" determines the workload of a user request to be scheduled to a virtual machine at each time according to the following formula:
wherein, γijRepresenting a trust value of the virtual machine; lambda [ alpha ]QoSAn influence coefficient indicating a quality of service;
representing the service quality of the virtual machine uploading; qij(t) represents the queue load of the virtual machine at time t; a. theij(t) represents the workload requested by the user at time t; bijRepresenting the relationship between the user request type and the virtual machine service type; v denotes the lyapunov optimization parameter.
3. The cloud platform service trust value calculation, task scheduling, and load balancing method of claim 1, wherein the "real-time dynamically adjusted" energy management policy determines the operating state of the service virtual machine at each time according to the following formula:
wherein Q isij(t) represents the virtual machine queue load at time t; lijRepresenting the processing power of the virtual machine;
representing the running energy consumption of the virtual machine at the moment t; y isijAnd (t) represents the running state of the virtual machine at the time t, 1 represents running, and 0 represents idle.
4. The cloud platform service trust value calculation, task scheduling, and load balancing method of claim 1, wherein the lightweight trust management policy of the cloud computing center updates the trust value of the service candidate by the following formula:
wherein the content of the first and second substances,
a trust value representing the virtual machine is represented,
and
is a measure of historical positive and negative behavior;
represents the amount of positive behavior of the virtual machine at time slot t, which can be calculated as
A service rating/5 at a moment, so that the value ranges from 1, 0.8, 0.6, 0.4, 0.2;
representing the temporal decay of the behaviour, wheredRepresenting the attenuation parameter, tnow-t represents a temporal variation; it can be seen that recent behaviors weigh trust values higher.
5. A cloud platform service trust value calculation, task scheduling and load balancing system implementing the cloud platform service trust value calculation, task scheduling and load balancing method of any one of claims 1 to 4, wherein the cloud platform service trust value calculation, task scheduling and load balancing system comprises:
the service request is firstly sent to a decision center when a user wants to request a service from the cloud platform, wherein the service is divided into a plurality of categories, and each type represents one service;
the decision center is responsible for service classification, request distribution and trust management; after a user request is sent to the cloud platform, the decision center searches published services capable of meeting the request functions, and after the service trust values are calculated, the request is sent to a proper server virtual machine;
the cloud server is provided with a plurality of cloud servers, and each server is provided with a plurality of services; each cloud server submits the service QoS calculated by the QoS indexes including the availability, the success rate and the reliability to a decision center so as to facilitate the access node decision center to make corresponding decisions; each cloud server has a manager that manages the services deployed thereon while determining the operational status of the server.
6. The cloud platform service trust value calculation, task scheduling, and load balancing system of claim 5, wherein the decision center comprises:
the service classification module is used for classifying the received service request type and the service type deployed in the cloud server;
the request scheduling module is used for scheduling the service request of each time slot to a virtual machine on a proper cloud server for processing;
and the trust management module is used for updating the trust value of the virtual machine on each cloud server.
7. The cloud platform service trust value calculation, task scheduling, and load balancing system of claim 5, wherein the cloud server manager comprises:
the service management module is used for recording and managing service information of each virtual machine in the cloud server;
and the energy consumption management module is used for managing the energy consumption of each virtual machine on the cloud server.
8. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
firstly, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
firstly, after receiving a user request, a cloud computing decision center schedules the user request to a proper virtual machine according to a request scheduling strategy of 'joining an optimal queue'; then, the cloud server is used for controlling the running state of the service virtual machine according to the energy management strategy of real-time dynamic adjustment; and finally, the cloud computing center updates the trust value of the virtual machine according to the lightweight trust management strategy.
10. An information data processing terminal, characterized in that the information data processing terminal is used for implementing the cloud platform service trust value calculation, task scheduling and load balancing system according to any one of claims 5 to 7.
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