CN112866334A - Video streaming media load balancing method based on dynamic load feedback - Google Patents
Video streaming media load balancing method based on dynamic load feedback Download PDFInfo
- Publication number
- CN112866334A CN112866334A CN202011597863.3A CN202011597863A CN112866334A CN 112866334 A CN112866334 A CN 112866334A CN 202011597863 A CN202011597863 A CN 202011597863A CN 112866334 A CN112866334 A CN 112866334A
- Authority
- CN
- China
- Prior art keywords
- node
- streaming media
- video streaming
- cluster system
- service capacity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention relates to the field of load balancing of video streaming media distribution, in particular to a video streaming media load balancing method based on dynamic load feedback, which is characterized by comprising the following steps of: step 1: aiming at the video streaming media server cluster, forming each server node; step 2: obtaining the whole service capacity of the cluster system; and step 3: obtaining the current load of the whole cluster system; and 4, step 4: obtaining the integral residual service capacity of the cluster system; and 5: obtaining the ratio of the residual service capacity of each node to the whole residual service capacity of the cluster system; step 6: determining each node SiThe number of new task requests that should be obtained; and 7: carrying out task allocation of each server node; and 8: and in the next period, repeating the steps 2 to 7, and calculating and distributing tasks of all the nodes. The invention ensures that the load balance of each node is ensured while ensuring that each single node resource in the server cluster is fully utilized.
Description
Technical Field
The invention relates to the field of load balancing of video streaming media distribution, in particular to a video streaming media load balancing method based on dynamic load feedback.
Background
With the rapid development of the mobile internet, video streaming media becomes an essential part in daily life of people, the service mode of the internet is gradually changed from traditional webpage information browsing, character and picture information and the like to novel service modes of short videos, live broadcasts and the like, and the streaming media service becomes one of the most popular services of the internet. However, the access amount of the video stream is greatly increased, the consumption of network bandwidth and server resources is increasingly serious, and uncontrollable situations such as jamming and the like often occur in the playing process of the media stream, which results in poor user experience.
In view of the above, to overcome the above technical defects, it is an urgent problem in the art to provide a video streaming media load balancing method based on dynamic load feedback.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a video streaming media load balancing method based on dynamic load feedback, which ensures that the load balance of each node is ensured while the resource of each single node in a server cluster is fully utilized.
In order to solve the technical problems, the technical scheme of the invention is as follows: a video streaming media load balancing method based on dynamic load feedback is characterized by comprising the following steps:
step 1: aiming at the video streaming media server cluster, forming each server node SiWherein i is 1,2, … M, and M is the number of server cluster nodes;
step 2: testing each node S by a pressure test methodiService capacity C ofiObtaining the integral service capacity C of the cluster system;
and step 3: setting each node SiCurrent load of liObtaining the current load L of the whole cluster system;
and 4, step 4: determining each node SiIs left overTraffic capacity riObtaining the integral residual service capacity R of the cluster system;
and 5: get each node SiIs remaining service capacity riRatio P of remaining service capacity R of cluster systemi；
Step 6: setting the number N of newly arrived task requests of the cluster system, and determining each node SiNumber of new task requests e that should be obtainedi；
And 7: setting a dynamic feedback period T, and distributing tasks of each server node according to the number of new tasks within one dynamic feedback period T;
and 8: and in the next period T, repeating the steps 2 to 7, and calculating and distributing tasks of all the nodes.
According to the scheme, in the step 2, the whole service capacity of the cluster system
According to the above scheme, in the step 3, the current load of the whole cluster system
According to the above scheme, in the step 4, the remaining service capacity of the whole cluster system
According to the scheme, in the step 5, each node SiIs remaining service capacity riRatio P of remaining service capacity R of cluster systemi＝ri/R。
According to the scheme, the step 6 comprises the following substeps:
step 61: setting the number N of newly arrived task requests of the cluster system;
step 62: obtaining each node S according to the calculation method of the residual capacityiNumber of new task requests that should be obtained
And step 63: obtaining each node S according to the algorithm of the overall load balancing ratioiNumber of new task requests that should be obtained
Step 64: introducing weighted average coefficients a1 and a2 according to the values obtained in the step 63 and the step 64, wherein a1+ a2 is 1, and obtaining each node SiNumber of new task requests e that should be obtainedi。
According to the above scheme, in the step 62,
according to the scheme, in the step 63,
according to the scheme, in the step 64, each node SiNumber of new task requests that should be obtained
Compared with the prior art, the invention has the beneficial characteristics that: by deploying the load balancing device at the server cluster end and performing video streaming media load balancing of the video server cluster based on a dynamic load feedback mechanism, the load balancing of each node is ensured while the resources of each single node in the server cluster are fully utilized, and the method has great significance for the development of video streaming media services.
Drawings
FIG. 1 is a block diagram of an embodiment of the present invention;
FIG. 2 is a flow chart of 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 will be described in further detail with reference to the accompanying drawings and specific 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.
Many aspects of the invention are better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, in the several views of the drawings, like reference numerals designate corresponding parts.
The word "exemplary" or "illustrative" as used herein means serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described below are exemplary embodiments provided to enable persons skilled in the art to make and use the examples of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. In other instances, well-known features and methods are described in detail so as not to obscure the invention. For purposes of the description herein, the terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," and derivatives thereof shall relate to the invention as oriented in fig. 1. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
Referring to fig. 1 and fig. 2, the video streaming media load balancing method based on dynamic load feedback of the present invention is different in that the method includes the following steps:
step 1: aiming at the video streaming media server cluster, forming each server node SiWherein i is 1,2, … MM is the number of the server cluster nodes;
step 2: testing each node S by a pressure test methodiService capacity C ofiObtaining the integral service capacity C of the cluster system;
and step 3: setting each node SiCurrent load of liObtaining the current load L of the whole cluster system;
and 4, step 4: determining each node SiIs remaining service capacity riObtaining the integral residual service capacity R of the cluster system;
and 5: get each node SiIs remaining service capacity riRatio P of remaining service capacity R of cluster systemi；
Step 6: setting the number N of newly arrived task requests of the cluster system, and determining each node SiNumber of new task requests e that should be obtainedi；
And 7: setting a dynamic feedback period T, and distributing tasks of each server node according to the number of new tasks within one dynamic feedback period T;
and 8: and in the next period T, repeating the steps 2 to 7, and calculating and distributing tasks of all the nodes.
Specifically, in the step 2, the overall service capacity of the cluster system
Specifically, in the step 3, the current load of the whole cluster system
Specifically, in the step 4, the remaining service capacity of the whole cluster system
Specifically, in step 5, each node SiIs remaining service capacity riRatio of remaining service capacity R of cluster systemPi＝ri/R。
Specifically, the step 6 includes the following substeps:
step 61: setting the number N of newly arrived task requests of the cluster system;
step 62: obtaining each node S according to the calculation method of the residual capacityiNumber of new task requests that should be obtained
And step 63: obtaining each node S according to the algorithm of the overall load balancing ratioiNumber of new task requests that should be obtained
Step 64: introducing weighted average coefficients a1 and a2 according to the values obtained in the step 63 and the step 64, wherein a1+ a2 is 1, and obtaining each node SiNumber of new task requests e that should be obtainedi。
Specifically, in step 62, each node S is obtained according to the remaining capacity calculation methodiNumber of new task requests that should be obtained
Specifically, in step 63, each node S is obtained according to the overall load balancing ratio algorithmiNumber of new task requests that should be obtained
Specifically, in step 64, each node SiNumber of new task requests that should be obtained
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (9)
1. A video streaming media load balancing method based on dynamic load feedback is characterized by comprising the following steps:
step 1: aiming at the video streaming media server cluster, forming each server node SiWherein i is 1,2, … M, and M is the number of server cluster nodes;
step 2: testing each node S by a pressure test methodiService capacity C ofiObtaining the integral service capacity C of the cluster system;
and step 3: setting each node SiCurrent load of liObtaining the current load L of the whole cluster system;
and 4, step 4: determining each node SiIs remaining service capacity riObtaining the integral residual service capacity R of the cluster system;
and 5: get each node SiIs remaining service capacity riRatio P of remaining service capacity R of cluster systemi；
Step 6: setting the number N of newly arrived task requests of the cluster system, and determining each node SiNumber of new task requests e that should be obtainedi；
And 7: setting a dynamic feedback period T, and distributing tasks of each server node according to the number of new tasks within one dynamic feedback period T;
and 8: and in the next period T, repeating the steps 2 to 7, and calculating and distributing tasks of all the nodes.
2. The method for video streaming media load balancing based on dynamic load feedback according to claim 1, wherein: in the step 2, the overall service capacity of the cluster system
3. The method for video streaming media load balancing based on dynamic load feedback according to claim 1, wherein: in the step 3, the current load of the whole cluster system
4. The method for video streaming media load balancing based on dynamic load feedback according to claim 1, wherein: in the step 4, the remaining service capacity of the cluster system as a whole
5. The method according to claim 1, wherein in step 5, each node S is configured to perform load balancingiIs remaining service capacity riRatio P of remaining service capacity R of cluster systemi＝ri/R。
6. The method for video streaming media load balancing based on dynamic load feedback according to claim 1, wherein: said step 6 comprises the following sub-steps:
step 61: setting the number N of newly arrived task requests of the cluster system;
step 62: obtaining each node S according to the calculation method of the residual capacityiNumber of new task requests that should be obtained
And step 63: obtaining each node S according to the algorithm of the overall load balancing ratioiNumber of new task requests that should be obtained
Step 64: introducing weighted average coefficients a1 and a2 according to the values obtained in the step 63 and the step 64, wherein a1+ a2 is 1, and obtaining each node SiNumber of new task requests e that should be obtainedi。
7. The method for video streaming media load balancing based on dynamic load feedback according to claim 6, wherein: in the step 62, in the step of,
8. the method for video streaming media load balancing based on dynamic load feedback according to claim 6, wherein: in the step 63, the process is carried out,
9. the method for video streaming media load balancing based on dynamic load feedback according to claim 6, wherein: in the step 64, each node SiNumber of new task requests that should be obtained
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011597863.3A CN112866334A (en) | 2020-12-29 | 2020-12-29 | Video streaming media load balancing method based on dynamic load feedback |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011597863.3A CN112866334A (en) | 2020-12-29 | 2020-12-29 | Video streaming media load balancing method based on dynamic load feedback |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112866334A true CN112866334A (en) | 2021-05-28 |
Family
ID=75998252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011597863.3A Withdrawn CN112866334A (en) | 2020-12-29 | 2020-12-29 | Video streaming media load balancing method based on dynamic load feedback |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112866334A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080225714A1 (en) * | 2007-03-12 | 2008-09-18 | Telefonaktiebolaget Lm Ericsson (Publ) | Dynamic load balancing |
| GB0911004D0 (en) * | 2008-09-02 | 2009-08-12 | Fujitsu Ltd | Load balancer setting method and load balancer setting apparatus |
| CN104994145A (en) * | 2015-06-23 | 2015-10-21 | 山东大学 | Load balancing method based on KVM virtual cluster |
| CN105516360A (en) * | 2016-01-19 | 2016-04-20 | 苏州帕科泰克物联技术有限公司 | Method and device for load balance of computer |
-
2020
- 2020-12-29 CN CN202011597863.3A patent/CN112866334A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080225714A1 (en) * | 2007-03-12 | 2008-09-18 | Telefonaktiebolaget Lm Ericsson (Publ) | Dynamic load balancing |
| GB0911004D0 (en) * | 2008-09-02 | 2009-08-12 | Fujitsu Ltd | Load balancer setting method and load balancer setting apparatus |
| CN104994145A (en) * | 2015-06-23 | 2015-10-21 | 山东大学 | Load balancing method based on KVM virtual cluster |
| CN105516360A (en) * | 2016-01-19 | 2016-04-20 | 苏州帕科泰克物联技术有限公司 | Method and device for load balance of computer |
Non-Patent Citations (1)
| Title |
|---|
| 杨炳钊: "流媒体服务器负载均衡算法研究与应用", 《中国优秀博硕士学位论文全文数据库信息科技辑》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109857546B (en) | Multi-server mobile edge computing unloading method and device based on Lyapunov optimization | |
| US20170142177A1 (en) | Method and system for network dispatching | |
| US8964544B2 (en) | Quality of service adjustments to improve network utilization | |
| CN109819057B (en) | Load balancing method and system | |
| KR102232900B1 (en) | System for cloud streaming service, method of cloud streaming service using optumal gpu and apparatus for the same | |
| Fu et al. | 360SRL: A sequential reinforcement learning approach for ABR tile-based 360 video streaming | |
| CN114501073B (en) | Live broadcast source returning method and device | |
| CN104349177B (en) | It is a kind of to turn to method, virtual machine and the system for playing multimedia file under desktop cloud | |
| CN105337901A (en) | Router intelligent bandwidth allocation method and device | |
| CN112954354B (en) | Video transcoding method, device, equipment and medium | |
| CN113157418A (en) | Server resource allocation method and device, storage medium and electronic equipment | |
| WO2021128293A1 (en) | Model training method and apparatus, and storage medium and program product | |
| Zhao et al. | A version-aware computation and storage trade-off strategy for multi-version VoD systems in the cloud | |
| Darwich et al. | Cost-efficient cloud-based video streaming through measuring hotness | |
| US20230379763A1 (en) | Dynamic continuous quality of service adjustment system | |
| Wang et al. | Intelligent edge learning for personalized crowdsourced livecast: Challenges, opportunities, and solutions | |
| Wang et al. | Robust saliency-driven quality adaptation for mobile 360-degree video streaming | |
| US9350948B2 (en) | Method and system for providing video service | |
| CN104683318B (en) | A kind of edge streaming server caching system of selection and system | |
| Bulkan et al. | On the load balancing of edge computing resources for on-line video delivery | |
| CN112866334A (en) | Video streaming media load balancing method based on dynamic load feedback | |
| Yang et al. | Social-viewport adaptive caching scheme with clustering for virtual reality streaming in an edge computing platform | |
| CN114945097A (en) | Video stream processing method and device | |
| Dubin et al. | A fair server adaptation algorithm for HTTP adaptive streaming using video complexity | |
| Gao et al. | Dhp: A joint video download and dynamic bitrate adaptation algorithm for short video streaming |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210528 |
|
| WW01 | Invention patent application withdrawn after publication |