CN105242956A - Virtual function service chain deployment system and deployment method therefor - Google Patents
Virtual function service chain deployment system and deployment method therefor Download PDFInfo
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Abstract
The present invention relates to a virtual function service chain deployment system, and a deployment method therefor. The system comprises: a virtual function interference prediction module, which is used for predicting performance interference among virtual functions and providing a decision-making basis for the dynamic adjustment of a service chain; a service chain mapping module, which is capable of handling customer service requests, real-time monitoring a service chain operating state, and implementing the service chain mapping; and a virtual function carrying platform, which is used for receiving and handling a function activation request and a feedback activation message from the service chain mapping module, monitoring a virtual function service running state, and producing a corresponding instance by the function activation request. The system and the method of the present invention directing to the performance interference of dependence, sequence, and function deployment among each virtual network function unit in the service chain, and on the basis of the condition of the virtual function interference prediction, can effectively improve execution performance of the service chain, and can reach a target of deployment optimization, so as to greatly reduce costs.
Description
Technical field
The present invention relates to computer network field, particularly a kind of virtual functions service chaining deployment system and dispositions method thereof.
Background technology
Along with the continuous growth of internet scale and continuing to bring out of new network service, how to make full use of Internet resources and adjustment, on-premise network function becomes problem demanding prompt solution.It is traditional that by increasing hardware in a network, to change the method cost of function high and lack dirigibility.To this, researcher proposes network function to be separated with hardware platform, makes network function by flexible deployment on different physical platforms, can realize the effective utilization to Internet resources.In this context, the extensive concern of people is caused as the virtual functions service chaining realizing one of virtual network function gordian technique.
Virtual functions service chaining is made up of some virtual network function unit and directed connection therebetween.Virtual network function in link is defined it by user.Order between virtual functional units is determined jointly by the dependence between virtual functional units and user's request.Virtual functions service chaining needs to be mapped in physical network to realize service function.Each virtual functions is mapped in the related platform in physical network, is exactly specifically to produce a virtual functions example in relevant available platform, and this example can be carried by virtual machine.In service chaining deployment, different network function examples can produce different impacts to the speed of data stream, may there are some dependences between network function unit.Especially, when different virtual network function examples is deployed to Same Physical platform, can generationly can disturb between each network function example.The execution performance of each example is not a state determined with the contact between virtual resource, and it is continuous dynamic change.Mainly because the execution performance of virtual network function example is subject to the impact of itself operating load size, if operating load changes, even if the virtual resource configuration distributing to example does not change, the execution performance of example also will change.
In addition, because virtual network function example is deployed on respective physical platform, although current Intel Virtualization Technology can for providing some effective performance isolation mechanism between these examples, when but physical system resources is distributed to different virtual network function example by the monitoring management unit on physical platform, these examples are fought for the physical resource of platform, cause the change of virtual resource service ability, namely form interference mutually between example.Therefore, when virtual functions service chaining being mapped in physical network, the annoyance level that the example that its virtual network function unit maps goes out is subject at corresponding platform must be considered.
Summary of the invention
For deficiency of the prior art, the invention provides a kind of virtual functions service chaining deployment system and dispositions method thereof.
According to design proposal provided by the present invention, a kind of virtual functions service chaining deployment system, comprises:
Virtual functions interference prediction module, for load applications distributes virtual resource, analyzes the relation that virtual functions and virtual resource demand are shown in, the performance interference between prediction virtual functions, carries out information interaction with service chaining mapping block;
Service chaining mapping block, users service needs is converted to formal service module function, according to the schedule dependence between service module function and user's request, service module function is sorted, and select target physical platform, the service module function information of needs and active information are sent to target physical platform;
Virtual functions carrying platform, inclusion platform and platform control system, platform control system for receive and process from service chaining mapping block function activation request, feedback active information, and monitor virtual functions running status, platform control system produces respective instance by function activation request.
Above-mentioned, described physical platform is single server, or for gathering the data center of server composition.
A kind of virtual functions service chaining dispositions method, specifically comprises following steps:
Step 1. service chaining mapping block receives user's services request, and will issue virtual functions interference prediction module after services request formalization;
Step 2. virtual functions interference prediction module obtains the function application carrying data of the physical platform in compass of competency by virtual functions carrying platform, performance interference suffered after disposing New function example is predicted, and will predict the outcome and feed back to service chaining mapping block;
Step 3. service chaining mapping block is assessed service chaining overall interference, selects optimum deployment scheme;
Optimum deployment scheme is mapped to corresponding virtual functions carrying platform by step 4. service chaining mapping block, and mobilizing function example;
Step 5. virtual functions carrying platform real-time monitors physical platform feature service data, service chaining mapping block monitors link channel service data in real time, if judge occur overload or disturb excessive situation according to the testing result of data packetloss rate, returns step 2.
Above-mentioned virtual functions service chaining dispositions method, described step 2 specifically comprises following content:
After the request of step 2.1. virtual functions interference prediction module receives user, the operation information of gleanings platform, recognition resource redundancy platform, and be sent to service chaining mapping block;
Step 2.2. service chaining mapping block, according to resource redundancy platform, is removed the physical platform of connectivity of link difference, and is fed back to virtual functions interference prediction module;
Step 2.3. virtual functions interference prediction module, according to cpu busy percentage and the applicable performance degree of disturbance of I/O bandwidth resources examination physical platform merit, sets up degree of disturbance forecast model;
Step 2.4. optimizes degree of disturbance forecast model, utilizes linear regression method to solve, and will solve result of calculation and be sent to service chaining mapping block.
Preferably, described step 2.4 specifically comprises following content:
Step 2.4.1. is based on degree of disturbance forecast model, define the degree of disturbance attribute of each physical platform, and demarcate a virtual functions, degree of disturbance traversal is carried out to belongings platform, elect for the minimum N number of physical platform of this virtual functions degree of disturbance, wherein, platform quantitative value N determines by optimizing complicacy;
Step 2.4.2. chooses a physical platform at random from N number of physical platform, and the virtual functions demarcated in previous step is deployed to this physical platform, and using the starting point of this physical platform as service chaining;
Step 2.4.3. is according to degree of disturbance forecast model, continue to demarcate remaining virtual functions, by carrying out degree of disturbance traversal to the belongings platform outside the physical platform disposed in removing previous step, calculate the difference of the interference value of the virtual functions demarcated in wherein each physical platform interference value and step 2.4.1, choose M minimum in a difference platform, platform quantitative value M determines according to optimization complicacy;
Step 2.4.4. chooses a physical platform at random from M physical platform, the virtual functions demarcated in step 2.4.3 is deployed on this physical platform, dispose remaining virtual functions successively according to step 2.4.3, and record determines the platform quantitative value optimizing complicacy;
Step 2.4.5. is according to platform quantitative value corresponding to service chaining sequential search, platform quantitative value corresponding to virtual functions equals 1, then check the platform quantitative value of follow-up virtual functions in order successively, until when all platform quantitative values are all 1, stop its functional link to select, when the platform quantitative value that virtual functions is corresponding is greater than 1, then subtracted 1, and simultaneously from former homologue platform, delete the corresponding physical platform of use, and jump in step 2.4.3 and re-execute this virtual functions and dispose;
Step 2.4.6. performs step 2.4.1 ~ 2.4.5 to residue service chaining, calculates the degree of disturbance average of every bar service chaining, the degree of disturbance average of more each service chaining, selects the minimum link of average as optimum deployment scheme.
Above-mentioned virtual functions service chaining dispositions method, described step 5 also comprises:
Step 5.1. checks in users service needs, whether each virtual functions request for utilization is mapped to corresponding physical platform, if the total resources needed for virtual functions request for utilization are less than or equal to the available resource of this physical platform, then carry out next step, otherwise, switching platform examination object, examines or check new physical platform;
Step 5.2. mobilizing function example, and active information is back to service chaining mapping block;
Step 5.3. service chaining mapping block compiles each virtual functions deployment scenario of service chaining, selects non-overloaded passage to create link between each virtual functions deployment platform;
Step 5.4. service chaining mapping block activates whole service chaining and monitors performance ruuning situation and the message transmission rate of each virtual functions.
Above-mentioned virtual functions service chaining dispositions method, services request formalization content in step 1 is for be divided into some execution modules by users service needs, all execution module types, as formal set, in task resolution demand process, call corresponding execution module from set.
Beneficial effect of the present invention:
1. the present invention is by virtual functions interference prediction module, and the performance interference between prediction virtual functions, for the dynamic conditioning of service chaining provides decision-making foundation; Service chaining mapping block, can process user's services request, and real-time monitor service chain running status also realizes service chaining mapping; The present invention, when based on virtual functions interference prediction, effectively can improve the execution performance of service chaining, meet the target of disposition optimization, greatly reduce costs.
2. the function that the present invention utilizes simulated annealing thought to design based on degree of disturbance combines and service chaining system of selection, consider the performance interference of the dependence in service chaining between each virtual network function unit, succession, function distributing, greatly reduce the annoyance level of example at corresponding platform, the dirigibility that service chaining is disposed.
accompanying drawing illustrates:
Fig. 1 is virtual functions service chaining deployment system schematic diagram of the present invention;
Fig. 2 is virtual functions service chaining dispositions method schematic flow sheet of the present invention;
Fig. 3 is that prediction schematic flow sheet is carried out in performance of the present invention interference;
Fig. 4 is optimization degree of disturbance forecast model of the present invention and solves schematic flow sheet;
Fig. 5 is that service chaining of the present invention maps schematic flow sheet.
embodiment:
Below in conjunction with accompanying drawing and technical scheme, the present invention is further detailed explanation, and describe embodiments of the present invention in detail by preferred embodiment, but embodiments of the present invention are not limited to this.
Embodiment one, shown in Figure 1, a kind of virtual functions service chaining deployment system, comprises:
Virtual functions interference prediction module, for load applications distributes virtual resource, analyzes the relation that virtual functions and virtual resource demand are shown in, the performance interference between prediction virtual functions, carries out information interaction with service chaining mapping block;
Service chaining mapping block, users service needs is converted to formal service module function, according to the schedule dependence between service module function and user's request, service module function is sorted, and select target physical platform, the service module function information of needs and active information are sent to target physical platform;
Virtual functions carrying platform, inclusion platform and platform control system, platform control system for receive and process from service chaining mapping block function activation request, feedback active information, and monitor virtual functions running status, platform control system produces respective instance by function activation request.
Preferably, described physical platform is single server, or for gathering the data center of server composition.
Embodiment two, shown in Figure 2, a kind of virtual functions service chaining dispositions method, specifically comprises following steps:
Step 1. service chaining mapping block receives user's services request, and will issue virtual functions interference prediction module after services request formalization, and wherein user's services package is containing the type of serving, duration and QoS demand;
Step 2. virtual functions interference prediction module obtains the function application carrying data of the physical platform in compass of competency by virtual functions carrying platform, performance interference suffered after disposing New function example is predicted, and will predict the outcome and feed back to service chaining mapping block;
Step 3. service chaining mapping block is assessed service chaining overall interference, selects optimum deployment scheme;
Optimum deployment scheme is mapped to corresponding virtual functions carrying platform by step 4. service chaining mapping block, and mobilizing function example;
Step 5. virtual functions carrying platform real-time monitors physical platform feature service data, service chaining mapping block monitors link channel service data in real time, if judge occur overload or disturb excessive situation according to the testing result of data packetloss rate, returns step 2.
Embodiment three, shown in Figure 3, substantially identical with embodiment two, difference is:
Described step 2 specifically comprises following content:
After the request of step 2.1. virtual functions interference prediction module receives user, the operation information of gleanings platform, recognition resource redundancy platform, and be sent to service chaining mapping block;
Step 2.2. service chaining mapping block, according to resource redundancy platform, is removed the physical platform of connectivity of link difference, is identified the platform of resource redundancy, and by result feedback to virtual functions interference prediction module;
Step 2.3. virtual functions interference prediction module, according to cpu busy percentage and the applicable performance degree of disturbance of I/O bandwidth resources examination physical platform merit, sets up degree of disturbance forecast model;
Step 2.4. optimizes degree of disturbance forecast model, utilizes linear regression method to solve, and will solve result of calculation and be sent to service chaining mapping block.
Embodiment four, shown in Figure 4, substantially identical with embodiment two, difference is: described step 2.4 specifically comprises following content:
Step 2.4.1. is based on degree of disturbance forecast model, define the degree of disturbance attribute of each physical platform, and demarcate a virtual functions, degree of disturbance traversal is carried out to belongings platform, elect for the minimum N number of physical platform of this virtual functions degree of disturbance, wherein, platform quantitative value N determines by optimizing complicacy;
Step 2.4.2. chooses a physical platform at random from N number of physical platform, and the virtual functions demarcated in previous step is deployed to this physical platform, and using the starting point of this physical platform as service chaining;
Step 2.4.3. is according to degree of disturbance forecast model, continue to demarcate remaining virtual functions, by carrying out degree of disturbance traversal to the belongings platform outside the physical platform disposed in removing previous step, calculate the difference of the interference value of the virtual functions demarcated in wherein each physical platform interference value and step 2.4.1, choose M minimum in a difference platform, platform quantitative value M determines according to optimization complicacy;
Step 2.4.4. chooses a physical platform at random from M physical platform, the virtual functions demarcated in step 2.4.3 is deployed on this physical platform, dispose remaining virtual functions successively according to step 2.4.3, and record determines the platform quantitative value optimizing complicacy;
Step 2.4.5. is according to platform quantitative value corresponding to service chaining sequential search, platform quantitative value corresponding to virtual functions equals 1, then check the platform quantitative value of follow-up virtual functions in order successively, until when all platform quantitative values are all 1, stop its functional link to select, when the platform quantitative value that virtual functions is corresponding is greater than 1, then subtracted 1, and simultaneously from former homologue platform, delete the corresponding physical platform of use, and jump in step 2.4.3 and re-execute this virtual functions and dispose;
Step 2.4.6. performs step 2.4.1 ~ 2.4.5 to residue service chaining, calculate the degree of disturbance average of every bar service chaining, the degree of disturbance average of more each service chaining, degree of disturbance mean value computation Consideration comprises the degree of disturbance of each platform and the weights of importance of platform in link, more each link interference degree average, selects the minimum link of average as optimum deployment scheme.
Embodiment five, shown in Figure 5, substantially identical with embodiment two, difference is: described step 5 also comprises:
Step 5.1. checks in users service needs, whether each virtual functions request for utilization is mapped to corresponding physical platform, if the total resources needed for virtual functions request for utilization are less than or equal to the available resource of this physical platform, then carry out next step, otherwise, switching platform examination object, examines or check new physical platform;
Step 5.2. mobilizing function example, and active information is back to service chaining mapping block;
Step 5.3. service chaining mapping block compiles each virtual functions deployment scenario of service chaining, selects non-overloaded passage to create link between each virtual functions deployment platform;
Step 5.4. service chaining mapping block activates whole service chaining and monitors performance ruuning situation and the message transmission rate of each virtual functions.
Above-mentioned virtual functions service chaining dispositions method, services request formalization content in step 1 is for be divided into some execution modules by users service needs, all execution module types, as formal set, in task resolution demand process, call corresponding execution module from set.
The present invention is not limited to above-mentioned embodiment, and those skilled in the art also can make multiple change accordingly, but to be anyly equal to the present invention or similar change all should be encompassed in the scope of the claims in the present invention.
Claims (7)
1. a virtual functions service chaining deployment system, is characterized in that: comprise:
Virtual functions interference prediction module, for load applications distributes virtual resource, analyzes the relation that virtual functions and virtual resource demand are shown in, the performance interference between prediction virtual functions, carries out information interaction with service chaining mapping block;
Service chaining mapping block, users service needs is converted to formal service module function, according to the schedule dependence between service module function and user's request, service module function is sorted, and select target physical platform, the service module function information of needs and active information are sent to target physical platform;
Virtual functions carrying platform, inclusion platform and platform control system, platform control system for receive and process from service chaining mapping block function activation request, feedback active information, and monitor virtual functions running status, platform control system produces respective instance by function activation request.
2. virtual functions service chaining deployment system according to claim 1, is characterized in that: described physical platform is single server, or for gathering the data center of server composition.
3. a virtual functions service chaining dispositions method, specifically comprises following steps:
Step 1. service chaining mapping block receives user's services request, and will issue virtual functions interference prediction module after services request formalization;
Step 2. virtual functions interference prediction module obtains the function application carrying data of the physical platform in compass of competency by virtual functions carrying platform, performance interference suffered after disposing New function example is predicted, and will predict the outcome and feed back to service chaining mapping block;
Step 3. service chaining mapping block is assessed service chaining overall interference, selects optimum deployment scheme;
Optimum deployment scheme is mapped to corresponding virtual functions carrying platform by step 4. service chaining mapping block, and mobilizing function example;
Step 5. completes service chaining and maps, virtual functions carrying platform real-time monitors physical platform feature service data, service chaining mapping block monitors link channel service data in real time, if judge occur overload or disturb excessive situation according to the testing result of data packetloss rate, returns step 2.
4. virtual functions service chaining dispositions method according to claim 3, is characterized in that: described step 2 specifically comprises following content:
After the request of step 2.1. virtual functions interference prediction module receives user, the operation information of gleanings platform, recognition resource redundancy platform, and be sent to service chaining mapping block;
Step 2.2. service chaining mapping block, according to resource redundancy platform, is removed the physical platform of connectivity of link difference, and is fed back to virtual functions interference prediction module;
Step 2.3. virtual functions interference prediction module, according to cpu busy percentage and the applicable performance degree of disturbance of I/O bandwidth resources examination physical platform merit, sets up degree of disturbance forecast model;
Step 2.4. optimizes degree of disturbance forecast model, utilizes linear regression method to solve, and will solve result of calculation and be sent to service chaining mapping block.
5. virtual functions service chaining dispositions method according to claim 4, is characterized in that: described step 2.4 specifically comprises following content:
Step 2.4.1. is based on degree of disturbance forecast model, define the degree of disturbance attribute of each physical platform, and demarcate a virtual functions, degree of disturbance traversal is carried out to belongings platform, elect for the minimum N number of physical platform of this virtual functions degree of disturbance, wherein, platform quantitative value N determines by optimizing complicacy;
Step 2.4.2. chooses a physical platform at random from N number of physical platform, and the virtual functions demarcated in previous step is deployed to this physical platform, and using the starting point of this physical platform as service chaining;
Step 2.4.3. is according to degree of disturbance forecast model, continue to demarcate remaining virtual functions, by carrying out degree of disturbance traversal to the belongings platform outside the physical platform disposed in removing previous step, calculate the difference of the interference value of the virtual functions demarcated in wherein each physical platform interference value and step 2.4.1, choose M minimum in a difference platform, platform quantitative value M determines according to optimization complicacy;
Step 2.4.4. chooses a physical platform at random from M physical platform, the virtual functions demarcated in step 2.4.3 is deployed on this physical platform, dispose remaining virtual functions successively according to step 2.4.3, and record determines the platform quantitative value optimizing complicacy;
Step 2.4.5. is according to platform quantitative value corresponding to service chaining sequential search, platform quantitative value corresponding to virtual functions equals 1, then check the platform quantitative value of follow-up virtual functions in order successively, until when all platform quantitative values are all 1, stop its functional link to select, when the platform quantitative value that virtual functions is corresponding is greater than 1, then subtracted 1, and simultaneously from former homologue platform, delete the corresponding physical platform of use, and jump in step 2.4.3 and re-execute this virtual functions and dispose;
Step 2.4.6. performs step 2.4.1 ~ 2.4.5 to residue service chaining, calculates the degree of disturbance average of every bar service chaining, the degree of disturbance average of more each service chaining, selects the minimum link of average as optimum deployment scheme.
6., according to the virtual functions service chaining dispositions method described in claim 3, it is characterized in that: described step 5 also comprises:
Step 5.1. checks in users service needs, whether each virtual functions request for utilization is mapped to corresponding physical platform, if the total resources needed for virtual functions request for utilization are less than or equal to the available resource of this physical platform, then carry out next step, otherwise, switching platform examination object, examines or check new physical platform;
Step 5.2. mobilizing function example, and active information is back to service chaining mapping block;
Step 5.3. service chaining mapping block compiles each virtual functions deployment scenario of service chaining, selects non-overloaded passage to create link between each virtual functions deployment platform;
Step 5.4. service chaining mapping block activates whole service chaining and monitors performance ruuning situation and the message transmission rate of each virtual functions.
7. virtual functions service chaining dispositions method according to claim 3, it is characterized in that: the services request formalization content in step 1 is for be divided into some execution modules by users service needs, all execution module types are as formal set, in task resolution demand process, from set, call corresponding execution module.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106452842A (en) * | 2016-09-14 | 2017-02-22 | 上海海事大学 | Network function virtualization (NFV) intermediate system framework |
| CN107231412A (en) * | 2017-05-22 | 2017-10-03 | 浙江工商大学 | A kind of service path building method mapped based on virtual net |
| CN107395501A (en) * | 2017-08-29 | 2017-11-24 | 电子科技大学 | A kind of cross-domain dispositions method of network service function chain |
| CN107483286A (en) * | 2017-08-14 | 2017-12-15 | 电子科技大学 | Merge the method with deployment services functional chain under a kind of environment based on cloud and mist |
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| CN107526636A (en) * | 2016-10-26 | 2017-12-29 | 腾讯科技（深圳）有限公司 | Resource identification method and device |
| CN107579852A (en) * | 2017-09-15 | 2018-01-12 | 郑州云海信息技术有限公司 | Virtual network performance isolation system and method based on historical models in Cloud Server |
| CN107682203A (en) * | 2017-10-30 | 2018-02-09 | 北京计算机技术及应用研究所 | A kind of security function dispositions method based on service chaining |
| CN107749801A (en) * | 2017-09-28 | 2018-03-02 | 西南交通大学 | A kind of virtual network function laying method based on population Incremental Learning Algorithm |
| WO2018086465A1 (en) * | 2016-11-14 | 2018-05-17 | Huawei Technologies Co., Ltd. | Integrating physical and virtual network functions in a service-chained network environment |
| CN108075990A (en) * | 2018-01-30 | 2018-05-25 | 北京邮电大学 | The service chaining backup node allocation algorithm and device of resource perception |
| CN108400998A (en) * | 2017-02-07 | 2018-08-14 | 华为技术有限公司 | A kind of method and system of deployment VNF |
| CN109428817A (en) * | 2017-08-31 | 2019-03-05 | 华为技术有限公司 | Business chain processing method, related network elements and system |
| CN109639447A (en) * | 2017-10-09 | 2019-04-16 | 中兴通讯股份有限公司 | The method and device of network function virtualization service chain mapping under annular networking |
| CN111131319A (en) * | 2019-12-30 | 2020-05-08 | 北京天融信网络安全技术有限公司 | Security capability expansion method and device, electronic equipment and storage medium |
| CN111193604A (en) * | 2019-08-23 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Deployment method, device, equipment and storage medium of virtual network function chain |
| CN113315648A (en) * | 2020-02-26 | 2021-08-27 | 三星电子株式会社 | Method and apparatus for performing virtualized network functions |
| CN113422812A (en) * | 2021-06-08 | 2021-09-21 | 北京邮电大学 | Service chain deployment method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102090020A (en) * | 2008-08-26 | 2011-06-08 | 思科技术公司 | Method and apparatus for dynamically instantiating services using a service insertion architecture |
| CN104137482A (en) * | 2014-04-14 | 2014-11-05 | 华为技术有限公司 | Disaster recovery data center configuration method and device under cloud computing framework |
| WO2015062627A1 (en) * | 2013-10-29 | 2015-05-07 | Telefonaktiebolaget L M Ericsson (Publ) | Control of a chain of services |
| CN104679595A (en) * | 2015-03-26 | 2015-06-03 | 南京大学 | Application-oriented dynamic resource allocation method for IaaS (Infrastructure As A Service) layer |
-
2015
- 2015-09-15 CN CN201510584507.0A patent/CN105242956B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102090020A (en) * | 2008-08-26 | 2011-06-08 | 思科技术公司 | Method and apparatus for dynamically instantiating services using a service insertion architecture |
| WO2015062627A1 (en) * | 2013-10-29 | 2015-05-07 | Telefonaktiebolaget L M Ericsson (Publ) | Control of a chain of services |
| CN104137482A (en) * | 2014-04-14 | 2014-11-05 | 华为技术有限公司 | Disaster recovery data center configuration method and device under cloud computing framework |
| CN104679595A (en) * | 2015-03-26 | 2015-06-03 | 南京大学 | Application-oriented dynamic resource allocation method for IaaS (Infrastructure As A Service) layer |
Cited By (31)
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|---|---|---|---|---|
| CN107483222A (en) * | 2016-06-08 | 2017-12-15 | 中兴通讯股份有限公司 | A kind of management method and network management system of the virtual network function based on micro services |
| CN107483222B (en) * | 2016-06-08 | 2021-08-27 | 中兴通讯股份有限公司 | Virtual network function management method based on micro-service and network management system |
| CN106452842B (en) * | 2016-09-14 | 2019-09-24 | 上海海事大学 | Network system based on network function virtualization intermediary system architecture |
| CN106452842A (en) * | 2016-09-14 | 2017-02-22 | 上海海事大学 | Network function virtualization (NFV) intermediate system framework |
| CN107526636A (en) * | 2016-10-26 | 2017-12-29 | 腾讯科技（深圳）有限公司 | Resource identification method and device |
| US10187263B2 (en) | 2016-11-14 | 2019-01-22 | Futurewei Technologies, Inc. | Integrating physical and virtual network functions in a service-chained network environment |
| WO2018086465A1 (en) * | 2016-11-14 | 2018-05-17 | Huawei Technologies Co., Ltd. | Integrating physical and virtual network functions in a service-chained network environment |
| CN108400998A (en) * | 2017-02-07 | 2018-08-14 | 华为技术有限公司 | A kind of method and system of deployment VNF |
| CN108400998B (en) * | 2017-02-07 | 2020-03-20 | 华为技术有限公司 | VNF deployment method and system |
| WO2018145615A1 (en) * | 2017-02-07 | 2018-08-16 | 华为技术有限公司 | Vnf deployment method and system |
| CN107231412A (en) * | 2017-05-22 | 2017-10-03 | 浙江工商大学 | A kind of service path building method mapped based on virtual net |
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| CN107395501A (en) * | 2017-08-29 | 2017-11-24 | 电子科技大学 | A kind of cross-domain dispositions method of network service function chain |
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| CN109428817A (en) * | 2017-08-31 | 2019-03-05 | 华为技术有限公司 | Business chain processing method, related network elements and system |
| CN109428817B (en) * | 2017-08-31 | 2021-06-22 | 华为技术有限公司 | Service chain processing method, related network element and system |
| CN107579852A (en) * | 2017-09-15 | 2018-01-12 | 郑州云海信息技术有限公司 | Virtual network performance isolation system and method based on historical models in Cloud Server |
| CN107749801B (en) * | 2017-09-28 | 2019-09-06 | 西南交通大学 | A kind of virtual network function laying method based on population Incremental Learning Algorithm |
| CN107749801A (en) * | 2017-09-28 | 2018-03-02 | 西南交通大学 | A kind of virtual network function laying method based on population Incremental Learning Algorithm |
| CN109639447A (en) * | 2017-10-09 | 2019-04-16 | 中兴通讯股份有限公司 | The method and device of network function virtualization service chain mapping under annular networking |
| CN107682203A (en) * | 2017-10-30 | 2018-02-09 | 北京计算机技术及应用研究所 | A kind of security function dispositions method based on service chaining |
| CN107682203B (en) * | 2017-10-30 | 2020-09-08 | 北京计算机技术及应用研究所 | Security function deployment method based on service chain |
| CN108075990B (en) * | 2018-01-30 | 2020-09-11 | 北京邮电大学 | Resource-aware service chain backup node allocation algorithm and device |
| WO2019148605A1 (en) * | 2018-01-30 | 2019-08-08 | 北京邮电大学 | Resource-aware backup node allocation algorithm for service chain, and device |
| CN108075990A (en) * | 2018-01-30 | 2018-05-25 | 北京邮电大学 | The service chaining backup node allocation algorithm and device of resource perception |
| CN111193604A (en) * | 2019-08-23 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Deployment method, device, equipment and storage medium of virtual network function chain |
| CN111131319A (en) * | 2019-12-30 | 2020-05-08 | 北京天融信网络安全技术有限公司 | Security capability expansion method and device, electronic equipment and storage medium |
| CN113315648A (en) * | 2020-02-26 | 2021-08-27 | 三星电子株式会社 | Method and apparatus for performing virtualized network functions |
| CN113422812A (en) * | 2021-06-08 | 2021-09-21 | 北京邮电大学 | Service chain deployment method and device |
| CN113422812B (en) * | 2021-06-08 | 2022-07-29 | 北京邮电大学 | Service chain deployment method and device |
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