CN105554149A - Video cloud storage load equalizing method and system based on video ranks - Google Patents
Video cloud storage load equalizing method and system based on video ranks Download PDFInfo
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Abstract
The invention discloses a video cloud storage load equalizing method and system based on a video rank. The method comprises: uploading all video files to a cloud server in advance; calculating the rank of all video files according to a video rank algorithm, storing the video files to specific storage nodes according to the rank; when the video file play request of a client is detected, obtaining the video files of storage nodes for playing. According to the invention, the rank of the video files are calculated through the video rank algorithm; the videos are respectively stored in different storage nodes according to the rank of the video files; therefore many problems resulted from using the original video cloud storage in an on demand system are improved; the integrated resource of the platform is utilized efficiently; and meanwhile the response speed to the user operation is effectively improved.
Description
Technical field
The present invention relates to field of computer technology, particularly relate to a kind of video cloud memory load equalization methods based on video rank and system.
Background technology
Along with the fast development of modern computer network technologies, increasing video software is applied to mobile terminal, pass through video software, user can watch the program searching and oneself like or TV play is watched, also can select to download, buffer memory, in local disk, is not watched when having network to facilitate, more convenient.At present, a lot of people has the demand chasing after play, especially the TV play of hot broadcast, variety show and cartoon, but user manually can only carry out the collection of drama of down loading updating after the video liked upgrades, and inconvenient, user is often not free to download, the video liked can not be namely watched when wanting viewing, and user cache number of videos more time, often can not Timeliness coverage deleting, take memory space.
During existing video cloud stores, only reach balanced on storage resources, do not consider other efficiency of platform, as cpu busy percentage, thus make CPU slow for user operation response speed, the overall resource utilization on platform is low.
Therefore, prior art has yet to be improved and developed.
Summary of the invention
In view of the deficiencies in the prior art, the object of the invention is to provide a kind of video cloud memory load equalization methods based on video rank and system, be intended to solve during existing video cloud stores in prior art, only reach balanced on storage resources, also cannot carry out according to the rank of video the defect stored of classifying.
Technical scheme of the present invention is as follows:
Based on a video cloud memory load equalization methods for video rank, wherein, method comprises:
Based on a video cloud memory load equalization methods for video rank, wherein, method comprises:
A, in advance all video files are uploaded in Cloud Server;
B, calculate the rank of all video files according to video rank algorithm, and according to rank, video file is stored in specific memory node;
C, when the video file playing request of client being detected, obtaining the video file of memory node and playing.
The described video cloud memory load equalization methods based on video rank, wherein, described steps A specifically comprises:
A1, be connected to Cloud Server by storing the intelligent terminal of all video files by network in advance;
All video files upload in Cloud Server by A2, intelligent terminal.
The described video cloud memory load equalization methods based on video rank, wherein, described step B specifically comprises:
B1, rank according to all video files in video rank algorithm calculation server;
B2, obtain current available memory node according to current network bandwidth utilance and cpu busy percentage;
B3, be stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node.
The described video cloud memory load equalization methods based on video rank, wherein, described step C specifically comprises:
C1, when video file playing request client being detected, judge the position of video file memory node that request is play;
If the memory node at the video file place that C2 request is play is on-line storage node, then play-over video file;
If the memory node at the video file place that C3 request is play is offline storage node, then play after video file being transferred to on-line storage node.
The video cloud memory load equalization methods based on video rank described in above-mentioned any one, wherein, described video rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time;
Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file.
Based on a video cloud memory load equalizing system for video rank, wherein, described system comprises:
Go up transmission module in advance, for uploading in Cloud Server by all video files in advance;
Video ranking module, for calculating the rank of all video files according to video rank algorithm, and is stored in specific memory node according to rank by video file;
Detect and playing module, for when the video file playing request of client being detected, the video file obtaining memory node is play.
The described video cloud memory load equalizing system based on video rank, wherein, described transmission module of going up in advance specifically comprises:
Network connection unit, for being connected to Cloud Server by the intelligent terminal storing all video files by network in advance;
Video file uploading unit, uploads to all video files in Cloud Server for intelligent terminal.
The described video cloud memory load equalizing system based on video rank, wherein, described video ranking module specifically comprises:
Rank computing unit, for the rank according to all video files in video rank algorithm calculation server;
Acquiring unit, for obtaining current available memory node according to current network bandwidth utilance and cpu busy percentage;
Memory cell, for being stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node.
The described video cloud memory load equalizing system based on video rank, wherein, described detection and playing module specifically comprise:
Detect and judging unit, for when video file playing request client being detected, judge the position of the video file memory node that request is play;
First broadcast unit, if be on-line storage node for the memory node at the video file place of request broadcasting, then play-overs video file;
Second broadcast unit, if be offline storage node for the memory node at the video file place of request broadcasting, then plays after video file being transferred to on-line storage node.
The video cloud memory load equalizing system based on video rank described in above-mentioned any one, wherein, described rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time;
Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file.
The invention provides a kind of video cloud memory load equalization methods based on video rank and system, the present invention calculates video file rank by video rank algorithm, according to video file rank video is stored in respectively the different memory nodes in Cloud Server, thus improve original video cloud and be stored in many problems for bringing during VOD system, the overall resource of energy efficiency utilization platform, effectively can improve the response speed to user operation simultaneously.
Accompanying drawing explanation
Fig. 1 is the flow chart of the preferred embodiment of a kind of video cloud memory load equalization methods based on video rank of the present invention.
Fig. 2 is the functional schematic block diagram of the preferred embodiment of a kind of video cloud memory load equalizing system based on video rank of the present invention.
Embodiment
For making object of the present invention, technical scheme and effect clearly, clearly, the present invention is described in more detail below.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.
The invention provides a kind of flow chart of preferred embodiment of the video cloud memory load equalization methods based on video rank, as shown in Figure 1, described method comprises:
Step S100, in advance all video files to be uploaded in Cloud Server.
During concrete enforcement, step S100 specifically comprises:
Step S101, be connected to Cloud Server by storing the intelligent terminal of all video files by network in advance;
All video files upload in Cloud Server by step S102, intelligent terminal.
During concrete enforcement, in the embodiment of the present invention, server is designated as video cloud memory.This video cloud memory is a distributed memory system.Therefore first to carry out distributed memory system to build and files passe.First distributed memory system is built; The video file of needs is imported in distributed system.
Step S200, calculate the rank of all video files according to video rank algorithm, and according to rank, video file is stored in specific memory node.
During concrete enforcement, calculate the rank of the video file in Cloud Server according to video rank algorithm, successively respectively video file is stored into specific memory node according to rank.The forward video file of such as rank is stored in can the on-line storage node of quick storage, the be stored to offline storage ranked behind node.Offline storage node access speed is slightly slower than on-line storage node, but has a large amount of memory spaces.
Further, in the embodiment in the present invention, video rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time; Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file.
Particularly, obtain network video file total click volume and in a period of time click volume can obtain the popular degree of current video, preferential also can obtain current comment number and point praises number.Parameter is multiplied by certain weight respectively, can is identical weight, or arranges as required.Numerical value for weight in the present invention does not limit.Obtain all videos according to the mark after algorithm calculating, the mark of video file carries out rank to video file from high to low on time.
In further embodiment, step S200 specifically comprises:
Step S201, rank according to all video files in video rank algorithm calculation server;
Step S202, obtain current available memory node according to current network bandwidth utilance and cpu busy percentage;
Step S203, be stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node.
During concrete enforcement, according to above-mentioned video rank algorithm, rank is carried out to all video files in server, obtain utilance and the cpu busy percentage of current network bandwidth, thus obtain memory node available in distributed server.By video file rank before a predetermined ranking, such as the video file of first 20 is stored in on-line storage node, after this predetermined ranking, video file such as after 20 is stored in off-line node, utilize the memory node of server fully, improve access efficiency, not only reach the equilibrium of storage resources, and consider the utilance of CPU and the network bandwidth, improve the response speed of user.Offline storage also reduces the burden of CPU, thus effectively utilizes the various resources of platform.
Step S300, when the video file playing request of client being detected, obtaining the video file of memory node and playing.
In further embodiment, described step S300 specifically comprises:
Step S301, when video file playing request client being detected, judge the position of video file memory node that request is play;
If the memory node at the video file place that step S302 request is play is on-line storage node, then play-over video file;
If the memory node at the video file place that step S303 request is play is offline storage node, then play after video file being transferred to on-line storage node.
During concrete enforcement, when the video file playing request of client being detected, server judges the position of the video file memory node that request is play, i.e. the type of memory node.If the memory node at the file place that request is play is on-line storage node, play-over the video file of request.If the memory node at the video file place that request is play is offline storage node, need video file to play to on-line storage node from offline storage node motion.
From above embodiment of the method, the invention provides a kind of video cloud memory load equalization methods based on video rank, video file rank is calculated by video rank algorithm, according to video file rank video is stored in respectively the different memory nodes in Cloud Server, thus improve original video cloud and be stored in many problems for bringing during VOD system, the overall resource of energy efficiency utilization platform, effectively can improve the response speed to user operation simultaneously.
On the basis of said method embodiment, present invention also offers a kind of functional schematic block diagram of preferred embodiment of the video cloud memory load equalizing system based on video rank, as shown in Figure 2, described system comprises:
Go up transmission module 100 in advance, for uploading in Cloud Server by all video files in advance; As detailed above.
Video ranking module 200, for calculating the rank of all video files according to video rank algorithm, and is stored in specific memory node according to rank by video file; As detailed above.
Detect and playing module 300, for when the video file playing request of client being detected, the video file obtaining memory node is play; As detailed above.
The described video cloud memory load equalizing system based on video rank, wherein, described transmission module of going up in advance specifically comprises:
Network connection unit, for being connected to Cloud Server by the intelligent terminal storing all video files by network in advance; As detailed above.
Video file uploading unit, uploads in Cloud Server for intelligent terminal by all video files; As detailed above.
The described video cloud memory load equalizing system based on video rank, wherein, described video ranking module specifically comprises:
Rank computing unit, for the rank according to all video files in video rank algorithm calculation server; As detailed above.
Acquiring unit, for obtaining current available memory node according to current network bandwidth utilance and cpu busy percentage; As detailed above.
Memory cell, for being stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node; As detailed above.
The described video cloud memory load equalizing system based on video rank, wherein, described detection and playing module specifically comprise:
Detect and judging unit, for when video file playing request client being detected, judge the position of the video file memory node that request is play; As detailed above.
First broadcast unit, if be on-line storage node for the memory node at the video file place of request broadcasting, then play-overs video file; As detailed above.
Second broadcast unit, if be offline storage node for the memory node at the video file place of request broadcasting, then plays after video file being transferred to on-line storage node; As detailed above.
The video cloud memory load equalizing system based on video rank described in above-mentioned any one, wherein, described rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time; As detailed above.
Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file; As detailed above.
In sum, the invention provides a kind of video cloud memory load equalization methods based on video rank and system, described method comprises: upload in Cloud Server by all video files in advance; Calculate the rank of all video files according to video rank algorithm, and according to rank, video file is stored in specific memory node; When the video file playing request of client being detected, the video file obtaining memory node is play.The present invention calculates video file rank by video rank algorithm, according to video file rank video is stored in respectively the different memory nodes in Cloud Server, thus improve original video cloud and be stored in many problems for bringing during VOD system, the overall resource of energy efficiency utilization platform, effectively can improve the response speed to user operation simultaneously.
Should be understood that, application of the present invention is not limited to above-mentioned citing, for those of ordinary skills, can be improved according to the above description or convert, and all these improve and convert the protection range that all should belong to claims of the present invention.
Claims (10)
1., based on a video cloud memory load equalization methods for video rank, it is characterized in that, method comprises:
A, in advance all video files are uploaded in Cloud Server;
B, calculate the rank of all video files according to video rank algorithm, and according to rank, video file is stored in specific memory node;
C, when the video file playing request of client being detected, obtaining the video file of memory node and playing.
2. the video cloud memory load equalization methods based on video rank according to claim 1, it is characterized in that, described steps A specifically comprises:
A1, be connected to Cloud Server by storing the intelligent terminal of all video files by network in advance;
All video files upload in Cloud Server by A2, intelligent terminal.
3. the video cloud memory load equalization methods based on video rank according to claim 2, it is characterized in that, described step B specifically comprises:
B1, rank according to all video files in video rank algorithm calculation server;
B2, obtain current available memory node according to current network bandwidth utilance and cpu busy percentage;
B3, be stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node.
4. the video cloud memory load equalization methods based on video rank according to claim 3, it is characterized in that, described step C specifically comprises:
C1, when video file playing request client being detected, judge the position of video file memory node that request is play;
If the memory node at the video file place that C2 request is play is on-line storage node, then play-over video file;
If the memory node at the video file place that C3 request is play is offline storage node, then play after video file being transferred to on-line storage node.
5. the video cloud memory load equalization methods based on video rank according to any one of claim 1-4, it is characterized in that, described video rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time;
Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file.
6., based on a video cloud memory load equalizing system for video rank, it is characterized in that, described system comprises:
Go up transmission module in advance, for uploading in Cloud Server by all video files in advance;
Video ranking module, for calculating the rank of all video files according to video rank algorithm, and is stored in specific memory node according to rank by video file;
Detect and playing module, for when the video file playing request of client being detected, the video file obtaining memory node is play.
7. the video cloud memory load equalizing system based on video rank according to claim 6, is characterized in that, described transmission module of going up in advance specifically comprises:
Network connection unit, for being connected to Cloud Server by the intelligent terminal storing all video files by network in advance;
Video file uploading unit, uploads to all video files in Cloud Server for intelligent terminal.
8. the video cloud memory load equalizing system based on video rank according to claim 7, it is characterized in that, described video ranking module specifically comprises:
Rank computing unit, for the rank according to all video files in video rank algorithm calculation server;
Acquiring unit, for obtaining current available memory node according to current network bandwidth utilance and cpu busy percentage;
Memory cell, for being stored in on-line storage node by the video file of rank before a predetermined ranking, other video files are stored in offline storage node.
9. the video cloud memory load equalizing system based on video rank according to claim 8, it is characterized in that, described detection and playing module specifically comprise:
Detect and judging unit, for when video file playing request client being detected, judge the position of the video file memory node that request is play;
First broadcast unit, if be on-line storage node for the memory node at the video file place of request broadcasting, then play-overs video file;
Second broadcast unit, if be offline storage node for the memory node at the video file place of request broadcasting, then plays after video file being transferred to on-line storage node.
10. the video cloud memory load equalizing system based on video rank according to any one of claim 6-9, it is characterized in that, described rank algorithm is specially:
Obtain total click volume of video file on network, and the recruitment of video file click volume within a period of time;
Obtain the mark of video file after total click volume being superposed according to certain weight respectively with the recruitment of click volume, from high to low rank is carried out to video file according to the mark of video file.
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| CN106506665A (en) * | 2016-11-18 | 2017-03-15 | 郑州云海信息技术有限公司 | A kind of load-balancing method of distributed video monitoring system and platform |
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Application publication date: 20160504 |