CN109451017B - Dynamic cloud computing resource management method based on granular computing in cloud environment - Google Patents
Dynamic cloud computing resource management method based on granular computing in cloud environment Download PDFInfo
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Abstract
The invention discloses a dynamic cloud computing resource management method based on granularity computing in a cloud environment, which comprises the following steps: s101, establishing an intelligent composite granularity dynamic cloud resource organization model; s102, establishing a dynamic calculation model of a large-scale composite granularity cloud user service request; s103, designing a dynamic cloud service resource discovery and scheduling algorithm based on semantic computation. The invention introduces the divide-and-conquer characteristic of granularity computing to reduce the complexity of dynamic cloud computing resources and user requests in the cloud environment, introduces the semantic computing idea, fully extracts the semantics of the cloud resources and the cloud user requests, designs the resource discovery and scheduling algorithm based on the semantics, realizes the high-efficiency sharing of cloud computing service resources, has important significance and value in the aspects of theoretical research and practical application, is applied to cloud computing service enterprises, improves the application effect by about 30 percent, and greatly promotes the forward development of cloud computing.
Description
Technical Field
The invention relates to the field of cloud computing, in particular to a dynamic cloud computing resource management method based on granular computing in a cloud environment.
Background
Cloud computing, as a new service form in the field of information technology, is regarded as the core of a new generation of information technology application mode and technology revolution, is receiving increasing attention from the industry and various countries, and has a very high application value. The Ministry of industry and belief starts planning aiming at cloud computing, and focuses on cultivating leading enterprises to play the radiation role of leading enterprises in industrial development and build a cloud computing industrial chain. Therefore, cloud computing is increasingly becoming a research hotspot in the information field, and the research on cloud computing has higher research value and extremely high application value.
The cloud computing service resources are core management objects of the cloud computing system, the resource management is a core function of the cloud computing system, and how to manage the cloud computing service resources is a key for successful application of the cloud computing system. However, the current cloud computing resource management has the following five problems:
(1) cloud resource management rarely considers changes in the capacity of resource nodes, which is contrary to the original intention that a cloud computing system must satisfy "dynamic changes adaptive to the supply and demand relationships of cloud resources and dynamically adjust resource management online".
(2) The organization model of the cloud computing resources generally has the phenomena of poor flexibility and expansibility. The number of cloud resources increases in a geometric progression, and the existing cloud resource organization sets an upper limit on the resource scale, so that the performance of a cloud system is sharply reduced.
(3) The cloud resource discovery method has the problems of low resource discovery efficiency, poor reliability and the like. The cloud resources have strong wide-area distribution, and the unreliability and the limitation of bandwidth and the like of the existing network cause the searching of the resources to be time-consuming and labor-consuming, and the efficiency of resource positioning directly influences the performance of the whole system. In addition, due to the diversity of cloud resources, finding resources that do not match the task needs of the cloud user often results.
(4) The resource scheduling generally has the problems of high communication overhead, single-point failure, low efficiency of a task scheduling algorithm and the like. In addition, the diversity and complexity of the cloud computing resources determine that a plurality of available cloud resources meeting the requirements of the cloud user task may exist in the cloud computing system, and the performance and the cost of running the cloud user task on different cloud resources are different.
(5) Resource management is lack of a QoS guarantee mechanism. The user can find a plurality of resources meeting the requirements, and the quality must be guaranteed by adopting the Qos. At present, resource management hardly ensures that the response time of a cloud task is reduced to a certain reasonable degree, and only best-effort service can be provided for cloud users, so that a Qos guarantee mechanism is relatively lacking.
Disclosure of Invention
The invention aims to solve the problems mentioned in the background technology part by using a dynamic cloud computing resource management method based on granular computing in a cloud environment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dynamic cloud computing resource management method based on granular computing in a cloud environment comprises the following steps:
s101, establishing an intelligent composite granularity dynamic cloud resource organization model;
s102, establishing a dynamic calculation model of a large-scale composite granularity cloud user service request;
s103, designing a dynamic cloud service resource discovery and scheduling algorithm based on semantic computation.
In particular, the intelligent composite granularity dynamic cloud resource organization model in step S101 is a composite granularity dynamic computing model based on a combination of a rough set and a quotient space theory.
Specifically, the step S101 includes: establishing a composite granularity dynamic calculation model based on combination of a rough set and a quotient space theory, and driving self-adaptive processing of data, namely intelligent automatic granulation, by using the characteristics of the data aiming at dynamic complex data structure objects generated under cloud calculation, wherein the self-adaptive processing comprises automatically selecting basic elements and dividing in an information structure to form structures between grain layers and in the grain layers.
Specifically, the step S101 includes: constructing interconversion and interdependence relations among worlds with different granularities based on a quotient space theory, and constructing representation and depiction of the granularities and interdependence relations between the granularities and concepts based on a rough set theory; the rough set model is embedded into the quotient space theoretical model, and the object relation among the worlds with different granularities is indexed through a granularity mapping mechanism, so that the automatic granulation of the dynamic complex structure is realized.
In particular, the dynamic computation model of the scaled composite-granularity cloud user service request in step S102 is a composite-granularity computation model based on a combination of a rough set, a probability theory, and a hypergraph theory.
In particular, said step S102 comprises: establishing a composite granularity calculation model based on combination of a rough set, a probability theory and a hypergraph theory, realizing layered parallel selection of basic elements and division aiming at user request data generated in a cloud environment, and realizing scale granulation among granular layers.
In particular, said step S102 comprises: aiming at the fact that the domain of the rough set theory is only a point set of an object, a probability model and a hypergraph model are introduced into a rough set granularity calculation model by combining the dynamic property and the mass property of data in a cloud environment, and the parallel granulation of a complex data structure is realized according to the probability inference rule and the characteristics of the hypergraph structure.
Specifically, the step S103 includes: by using a semantic computing theory and adopting a semantic WEB body, the machine automatically processes and integrates information published on the cloud, respectively extracts cloud resources and semantic information requested by a cloud user, and effectively stores the semantic information.
In particular, the step S103 further comprises: and a semantic search engine is adopted, according to the request of the cloud user, semantic information of the cloud resource is automatically matched by the semantic search engine through the semantic information, and automatic cloud resource discovery and scheduling based on semantics are realized.
The dynamic cloud computing resource management method based on the granularity computing under the cloud environment has the advantages that: in view of the characteristics of heterogeneous, dynamic and complex cloud resources, a rough set and a quotient space theory are introduced, a composite granularity dynamic calculation model combining the two theories is established, and the self characteristics of data are utilized to drive the self-adaptive processing of the data aiming at a dynamic complex data structure object generated under cloud calculation, namely intelligent automatic granulation. And secondly, in view of the characteristics of diversity, dynamics, complexity and the like of cloud user requests, introducing a rough set, a probability theory and a hypergraph theory, establishing a composite granularity calculation model combining the three theories, realizing hierarchical and parallel selection of basic elements and division aiming at massive user request data generated in a cloud environment, and realizing scale granulation among granular layers. After granulation modeling is carried out on the cloud resources and the cloud users, aiming at the two models, the basic idea of semantic computation is introduced, semantic information requested by the cloud resources and the cloud users is extracted, and the semantic information are effectively stored; by utilizing the semantic search engine, when a cloud user request comes, the semantic information of the cloud resource is automatically matched by the semantic search engine through the semantic information of the cloud user, so that the automatic cloud resource discovery and scheduling based on the semantics are realized. The invention introduces the divide-and-conquer characteristic of granularity computing to reduce the complexity of dynamic cloud computing resources and user requests in the cloud environment, introduces the semantic computing idea on the basis, fully extracts the semantics of the cloud resources and the cloud user requests, and designs a resource discovery and scheduling algorithm based on the semantics. The method realizes the efficient sharing of cloud computing service resources, and has important significance and value in the aspects of theoretical research and practical application. The dynamic cloud resource management model based on the granular computing and the resource discovery and scheduling algorithm based on the semantics are applied to cloud computing service enterprises, the application effect is improved by about 30%, and the forward development of the cloud computing is greatly promoted.
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Fig. 1 is a flowchart of a dynamic cloud computing resource management method based on granular computing in a cloud environment according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It is also to be noted that, for the convenience of description, only a part of the contents, not all of the contents, which are related to the present invention, are shown in the drawings, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a dynamic cloud computing resource management method based on granular computing in a cloud environment according to an embodiment of the present invention.
In this embodiment, the method for managing dynamic cloud computing resources based on granular computing in a cloud environment includes the following steps:
s101, establishing an intelligent composite granularity dynamic cloud resource organization model.
The resource structure in the cloud computing environment is relatively complex, highly dynamically changes, and resources are dynamically added at any time. Therefore, the cloud computing system is effectively managed and organized reasonably, and the efficiency of the whole cloud computing system is greatly improved. Aiming at the characteristics of cloud resources, the intelligent composite-granularity dynamic cloud resource organization model in the embodiment is a composite-granularity dynamic computing model based on the combination of a rough set and a quotient space theory, is used for solving the problem of automatic granulation of dynamically generated complex structure objects under cloud computing, and aims at the dynamic complex data structure objects generated under the cloud computing, self-adaptive processing of data is driven by the characteristics of the data, namely intelligent automatic granulation.
In this embodiment, for the construction of a composite granularity dynamic calculation model based on the combination of a rough set and a quotient space theory: firstly, constructing interconversion and interdependence relations among worlds with different granularities based on a quotient space theory, and constructing representation and depiction of the granularities and the interdependence relations between the granularities and concepts based on a rough set theory; secondly, embedding the rough set model into a quotient space theoretical model, and indexing the object relation among the worlds with different granularities through a granularity mapping mechanism to realize automatic granulation of the dynamic complex structure.
And S102, establishing a dynamic calculation model of a large-scale composite granularity cloud user service request.
The cloud user requests in the cloud computing environment are relatively complex, the dynamic computing model of the large-scale composite-granularity cloud user service requests in the embodiment is a composite-granularity computing model based on a combination of a rough set, a probability theory and a hypergraph theory, the model is adopted to carry out classification management on the cloud computing user requests, the parallel granulation problem of dynamically generating mass user complex requests in the cloud environment is solved, namely, the basic elements and the division are hierarchically and parallelly selected aiming at the user request data generated in the cloud environment, the large-scale granulation among grain layers is realized, the granulation progress of the dynamic cloud computing user requests is accelerated, and a good foundation is laid for cloud computing resource discovery and scheduling.
In the embodiment, the domain of theory for the rough set theory is only the point set of the object, the probability model and the hypergraph model are introduced into the rough set granularity calculation model by combining the dynamic property and the mass property of the data in the cloud environment, and the parallel granulation of the complex data structure is realized according to the probability inference rule and the characteristics of the hypergraph structure. The parallel granulation operation is carried out on the complex cloud user request, the response time of the cloud system to the cloud user is reduced, and the service efficiency of the cloud system is improved.
S103, designing a dynamic cloud service resource discovery and scheduling algorithm based on semantic computation.
On the basis of carrying out granularity-based computing modeling on cloud computing resources and cloud computing user requests, a semantic computing theory is used, a semantic WEB body is adopted, so that a network is easier to understand, a machine automatically processes and integrates information published on the cloud, semantic information of the cloud resources and the cloud user requests is respectively extracted, and the semantic information is effectively stored; and further adopting a semantic search engine to realize a cloud resource discovery and scheduling algorithm based on semantic computation. The semantic information is fully utilized, and the resource which is most matched with the submitted cloud user request is found out from the known available cloud resources, so that the optimal scheduling is met. In the embodiment, the semantic search engine is developed by the semantic calculation laboratory of the university of California, Europe and university, realizes the semantic calculation theory, provides a friendly problem-driven interface for a user to search resources, and automatically and quickly establishes a solution according to the requirements of the user. By using the cloud resource discovery and scheduling based on semantics, the efficiency of the cloud system is improved, and the optimal scheduling of the cloud resources is realized.
The technical scheme provided by the invention has the following advantages: in view of the characteristics of heterogeneous, dynamic and complex cloud resources, a rough set and a quotient space theory are introduced, a composite granularity dynamic calculation model combining the two theories is established, and the self characteristics of data are utilized to drive the self-adaptive processing of the data aiming at a dynamic complex data structure object generated under cloud calculation, namely intelligent automatic granulation. And secondly, in view of the characteristics of diversity, dynamics, complexity and the like of cloud user requests, introducing a rough set, a probability theory and a hypergraph theory, establishing a composite granularity calculation model combining the three theories, realizing hierarchical and parallel selection of basic elements and division aiming at massive user request data generated in a cloud environment, and realizing scale granulation among granular layers. After granulation modeling is carried out on the cloud resources and the cloud users, aiming at the two models, the basic idea of semantic computation is introduced, semantic information requested by the cloud resources and the cloud users is extracted, and the semantic information are effectively stored; by utilizing the semantic search engine, when a cloud user request comes, the semantic information of the cloud resource is automatically matched by the semantic search engine through the semantic information of the cloud user, so that the automatic cloud resource discovery and scheduling based on the semantics are realized.
The invention introduces the divide-and-conquer characteristic of granularity computing to reduce the complexity of dynamic cloud computing resources and user requests in the cloud environment, introduces the semantic computing idea on the basis, fully extracts the semantics of the cloud resources and the cloud user requests, and designs a resource discovery and scheduling algorithm based on the semantics. The method realizes the efficient sharing of cloud computing service resources, and has important significance and value in the aspects of theoretical research and practical application. The dynamic cloud resource management model based on the granular computing and the resource discovery and scheduling algorithm based on the semantics are applied to cloud computing service enterprises, the application effect is improved by about 30%, and the forward development of the cloud computing is greatly promoted.
It will be understood by those skilled in the art that all or part of the above embodiments may be implemented by the computer program to instruct the relevant hardware, and the program may be stored in a computer readable storage medium, and when executed, may include the procedures of the embodiments of the methods as described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (6)
1. A dynamic cloud computing resource management method based on granular computing in a cloud environment is characterized by comprising the following steps:
s101, establishing an intelligent composite granularity dynamic cloud resource organization model; the intelligent composite granularity dynamic cloud resource organization model in step S101 is a composite granularity dynamic computing model based on a combination of a rough set and a quotient space theory, and specifically, step S101 includes: establishing a composite granularity dynamic calculation model based on combination of a rough set and a quotient space theory, and driving self-adaptive processing of data, namely intelligent automatic granulation, by using the characteristics of the data aiming at dynamic complex data structure objects generated under cloud calculation, wherein the self-adaptive processing comprises automatically selecting basic elements and dividing in an information structure to form structures between grain layers and in the grain layers;
s102, establishing a dynamic calculation model of a large-scale composite granularity cloud user service request; the dynamic calculation model of the large-scale composite granularity cloud user service request is a composite granularity calculation model based on the combination of a rough set, a probability theory and a hypergraph theory;
s103, designing a dynamic cloud service resource discovery and scheduling algorithm based on semantic computation.
2. The method for managing dynamic cloud computing resources based on granular computing in a cloud environment according to claim 1, wherein the step S101 includes: constructing interconversion and interdependence relations among worlds with different granularities based on a quotient space theory, and constructing representation and depiction of the granularities and interdependence relations between the granularities and concepts based on a rough set theory; the rough set model is embedded into the quotient space theoretical model, and the object relation among the worlds with different granularities is indexed through a granularity mapping mechanism, so that the automatic granulation of the dynamic complex structure is realized.
3. The method for managing dynamic cloud computing resources based on granular computing in a cloud environment according to claim 1, wherein the step S102 includes: establishing a composite granularity calculation model based on combination of a rough set, a probability theory and a hypergraph theory, realizing layered parallel selection of basic elements and division aiming at user request data generated in a cloud environment, and realizing scale granulation among granular layers.
4. The method for managing dynamic cloud computing resources based on granular computing in a cloud environment according to claim 3, wherein the step S102 includes: aiming at the fact that the domain of the rough set theory is only a point set of an object, a probability model and a hypergraph model are introduced into a rough set granularity calculation model by combining the dynamic property and the mass property of data in a cloud environment, and the parallel granulation of a complex data structure is realized according to the probability inference rule and the characteristics of the hypergraph structure.
5. The method for managing dynamic cloud computing resources based on granular computing in a cloud environment according to claim 1, wherein the step S103 includes: by using a semantic computing theory and adopting a semantic WEB body, the machine automatically processes and integrates information published on the cloud, respectively extracts cloud resources and semantic information requested by a cloud user, and effectively stores the semantic information.
6. The method for managing dynamic cloud computing resources based on granular computing in a cloud environment according to claim 5, wherein the step S103 further comprises: and a semantic search engine is adopted, according to the request of the cloud user, semantic information of the cloud resource is automatically matched by the semantic search engine through the semantic information, and automatic cloud resource discovery and scheduling based on semantics are realized.
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