CN109246487B - Intelligent scheduling system - Google Patents
Intelligent scheduling system Download PDFInfo
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- CN109246487B CN109246487B CN201810937415.XA CN201810937415A CN109246487B CN 109246487 B CN109246487 B CN 109246487B CN 201810937415 A CN201810937415 A CN 201810937415A CN 109246487 B CN109246487 B CN 109246487B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/845—Structuring of content, e.g. decomposing content into time segments
- H04N21/8456—Structuring of content, e.g. decomposing content into time segments by decomposing the content in the time domain, e.g. in time segments
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/231—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion
- H04N21/23103—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion using load balancing strategies, e.g. by placing or distributing content on different disks, different memories or different servers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/231—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion
- H04N21/23106—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion involving caching operations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25808—Management of client data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
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- Engineering & Computer Science (AREA)
- Multimedia (AREA)
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- Databases & Information Systems (AREA)
- Computer Graphics (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention relates to an intelligent scheduling system which comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module. And the Hadoop distributed file system is used for storing data, so that the pressure of the server is relieved. User data is collected, and then intelligent analysis is performed by adopting a calculation model based on a big data system, so that optimal scheduling is performed based on user link experience.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an intelligent scheduling system.
Background
In the current P2P streaming media system, the client and the server are no longer so strictly different, and one client also often acts as a server to provide data to other nodes. The bandwidth, processing capacity, data and other resources of each node in the network are different along with the change of time, and how to find a proper node as a service node is an important problem. In addition, the dynamics of each node in the network makes it more difficult for the requesting node to acquire resources. Therefore, it is crucial for a streaming media system to make a reasonably efficient scheduling mechanism for streaming media data.
The problems of data scheduling in the current P2P streaming media system are: (1) when a certain node requests resources, the resources are requested according to the sequence; (2) when a certain resource is requested, a plurality of neighbor nodes can provide service at the same time, and how to select a reasonable neighbor node to receive the resource is achieved so as to achieve the reasonable utilization of the resource; (3) when resources are requested, how to request data from other nodes is avoided, and the load of the server is reduced.
Disclosure of Invention
In view of the above, the present invention provides an intelligent scheduling system that solves or partially solves the above-mentioned problems.
In order to achieve the effect of the technical scheme, the technical scheme of the invention is as follows: an intelligent scheduling system comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module;
the intelligent scheduling system is provided with a plurality of clients, and each client is a node; each client is respectively connected with the video server, the management server and the monitoring center; the video server is connected with the management server; the intelligent scheduling system stores data by using a Hadoop distributed file system, and realizes cloud computing by using Map Reduce in Hadoop;
the video server is used for publishing and storing video files; the video server divides the video file into data blocks with equal size; the video server transmits the data block information to the management server;
the management server is used for managing the client information;
the node management module, the cache module and the scheduling module are integrated on the client;
the node management module is used for managing the information of the node and the partner node and registering the node and the partner node to a management server; the cache module is used for caching the data block and also forwarding the data block for a partner node, and the caching of the data block is realized through a Hadoop distributed file system; the scheduling module is used for making a scheduling scheme and sending a data request to a corresponding partner node according to the scheduling scheme;
the intelligent scheduling system comprises the following working steps:
1) a user selects a video publishing website at a client side by means of mouse clicking or keyboard input, and enters an intelligent scheduling system;
2) the node management module registers on the management server, and the registered content comprises the IP address, the port number, the bandwidth and the video program to be watched of the node;
3) the management server returns to the node management module 20 partner nodes, the partner nodes are the nodes which are watching the video program to be watched, and the principle of partner node selection is that the IP addresses of the nodes are the closest;
4) the scheduling module firstly reads the past video watching behaviors of a user and the behaviors of other nodes on a network watching a video program to be watched, a model is built by using Map Reduce in Hadoop, the next jumping point of the user is predicted through a hidden Markov chain, the next jumping point is the position of a data block which is played when the user finishes watching the video program to be watched, and the data block before the next jumping point needs to be cached;
5) the scheduling module sends a data request to a corresponding partner node and caches a data block;
the monitoring center is used for evaluating the scheduling scheme and modifying the scheduling scheme; the monitoring center utilizes Map Reduce in Hadoop to calculate the system pressure of the client in real time, and the system pressure is calculated by a formula I:
the formula I is as follows:
wherein ω is the system pressure; a. b, c and d are proportionality coefficients which are rational numbers between 0 and 1, and are adjusted by background personnel according to needs, and the sum of the proportionality coefficients is 1; p is the utilization rate of the uplink bandwidth, which is the ratio of the used uplink bandwidth to the total bandwidth; q is server pressure, which is the ratio of the total number of data blocks stored by the video server to the total number of data blocks being played; r is starting delay, which is the time interval between all data blocks of the video played in the first 5s after the user enters the intelligent scheduling system; t is the playing quality, which is the ratio of the total number of data blocks actually obtained by the node of the video program at the current moment to the total number of the data blocks which should be obtained; omega and P, Q, R, T are rational numbers between 0 and 1;
when the system pressure is less than 0.5, the monitoring center immediately stops the scheduling scheme, background personnel modify the scheduling scheme and execute the scheduling scheme until the system pressure is not less than 0.5, and then the scheduling module is started to make a new scheduling scheme and execute the scheduling scheme.
The beneficial results of the invention are as follows: the invention provides an intelligent scheduling system which comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module. And the Hadoop distributed file system is used for storing data, so that the pressure of the server is relieved. User data is collected, and then intelligent analysis is performed by adopting a calculation model based on a big data system, so that optimal scheduling is performed based on user link experience.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more apparent, the present invention is described in detail below with reference to the embodiments. It should be noted that the specific embodiments described herein are only for illustrating the present invention and are not to be construed as limiting the present invention, and products that can achieve the same functions are included in the scope of the present invention. The specific method comprises the following steps:
example 1: this embodiment specifically introduces a P2P streaming media data scheduling mechanism, as follows:
at present, the data scheduling mechanism mainly comprises four modes of 'push', 'pull', 'push-pull' combination and data coding.
(1) Push data scheduling mechanism
The idea of the "push" data scheduling technique arises from the multicast technique, which is the earliest used scheduling mechanism. The push mode is mainly applied to a streaming media tree structure, and nodes are organized together in a single tree or a plurality of trees. The source service node is used as a root node, and data is distributed from the source node from top to bottom according to the hierarchical relationship of the tree. The father node always acquires data before the child nodes, the child nodes can only wait for receiving the data from the father node, once the child nodes receive the data, the child nodes can immediately forward the data to the next generation, and therefore the data are forwarded layer by layer, and finally all the nodes acquire the data from the source service node.
(2) "pull" data scheduling mechanism
The "pull" mode is a data scheduling mechanism followed by a "push" mode, in which data transmission has a certain direction, and a "pull" mode, in which data transmission has no fixed direction and requires a node to send a request to other neighbor nodes for acquisition. The "pull" mode is also commonly referred to as data-driven. The nodes in the pull mode do not passively receive data forwarded by other nodes like the nodes in the push mode, but select the neighbor nodes to pull the data, and actively request the data from other nodes. In the 'pull' mode data scheduling mechanism, nodes in the network are randomly connected with other nodes, cache images are exchanged periodically, and when data is required to be requested, data requests are sent to neighbor nodes according to cache information obtained through exchange. In "pull" mode data scheduling, a node needs to experience secondary communications from request to data block, which increases latency.
(3) Push-pull combined data scheduling mechanism
The push-pull combination mode takes the advantages of both the push mode and the pull mode into consideration, reduces the control overhead and reduces the data arrival delay, and greatly improves the overall performance of the system. In general, a push-pull mode system divides a coverage network into two levels, namely a tree structure and a mesh structure, and usually, in the tree structure, data is forwarded to other nodes in a push mode, and in the mesh structure, data is acquired from other nodes in a pull mode, so that the nodes can quickly and effectively acquire required data through the mutual cooperation of the two modes. How to coordinate between "push" and "pull" is an important issue that needs to be addressed in the "push-pull" mode. If the two modes are performed simultaneously when the same data block is transferred, the problem of repeated data downloading is caused.
(4) Data scheduling mechanism using data encoding
The coding technology is applied to the streaming media system, so that the service quality adapting to the change of the network bandwidth can be provided, and the stability of the system is enhanced. Currently, multiple description coding MDC and network coding NC are widely used in P2P streaming media systems.
Mdc (multiple Description coding) is very effective in error recovery, usually a multiple Description encoder generates multiple descriptions with equal rate and equal importance, each Description only needs to carry information with lower quality but acceptable, so that a user can decode and play media data as long as receiving one of the descriptions, and once more descriptions are received, the obtained streaming media data is increased, thereby improving the quality of the media data and greatly improving the service quality.
Application of NC (network coding) to a streaming media system is beneficial to large-scale content. The throughput of the network is increased, thereby promoting improved system performance. But a node needs to wait until enough data blocks arrive and decode to play the media data, so the network coding cannot be used directly in the on-demand system.
Example 2: this embodiment specifically exemplifies a structure of an intelligent scheduling system, including:
the system comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module;
the intelligent scheduling system is provided with a plurality of clients, and each client is a node; each client is respectively connected with the video server, the management server and the monitoring center; the video server is connected with the management server; the intelligent scheduling system stores data by using a Hadoop distributed file system, and realizes cloud computing by using Map Reduce in Hadoop;
the video server is used for publishing and storing video files; the video server divides the video file into data blocks with equal size; the video server transmits the data block information to the management server;
the management server is used for managing the client information;
the node management module, the cache module and the scheduling module are integrated on the client;
the node management module is used for managing the information of the node and the partner node and registering the node and the partner node to a management server; the cache module is used for caching the data block and also forwarding the data block for a partner node, and the caching of the data block is realized through a Hadoop distributed file system; the scheduling module is used for making a scheduling scheme and sending a data request to a corresponding partner node according to the scheduling scheme;
the intelligent scheduling system comprises the following working steps:
1) a user selects a video publishing website at a client side by means of mouse clicking or keyboard input, and enters an intelligent scheduling system;
2) the node management module registers on the management server, and the registered content comprises the IP address, the port number, the bandwidth and the video program to be watched of the node;
3) the management server returns to the node management module 20 partner nodes, the partner nodes are the nodes which are watching the video program to be watched, and the principle of partner node selection is that the IP addresses of the nodes are the closest;
4) the scheduling module firstly reads the past video watching behaviors of a user and the behaviors of other nodes on a network watching a video program to be watched, a model is built by using Map Reduce in Hadoop, the next jumping point of the user is predicted through a hidden Markov chain, the next jumping point is the position of a data block which is played when the user finishes watching the video program to be watched, and the data block before the next jumping point needs to be cached;
5) the scheduling module sends a data request to a corresponding partner node and caches a data block;
the monitoring center is used for evaluating the scheduling scheme and modifying the scheduling scheme; the monitoring center utilizes Map Reduce in Hadoop to calculate the system pressure of the client in real time, and the system pressure is calculated by a formula I:
the formula I is as follows:
wherein ω is the system pressure; a. b, c and d are proportionality coefficients which are rational numbers between 0 and 1, and are adjusted by background personnel according to needs, and the sum of the proportionality coefficients is 1; p is the utilization rate of the uplink bandwidth, which is the ratio of the used uplink bandwidth to the total bandwidth; q is server pressure, which is the ratio of the total number of data blocks stored by the video server to the total number of data blocks being played; r is starting delay, which is the time interval between all data blocks of the video played in the first 5s after the user enters the intelligent scheduling system; t is the playing quality, which is the ratio of the total number of data blocks actually obtained by the node of the video program at the current moment to the total number of the data blocks which should be obtained; omega and P, Q, R, T are rational numbers between 0 and 1;
when the system pressure is less than 0.5, the monitoring center immediately stops the scheduling scheme, background personnel modify the scheduling scheme and execute the scheduling scheme until the system pressure is not less than 0.5, and then the scheduling module is started to make a new scheduling scheme and execute the scheduling scheme.
The beneficial results of the invention are as follows: the invention provides an intelligent scheduling system which comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module. And the Hadoop distributed file system is used for storing data, so that the pressure of the server is relieved. User data is collected, and then intelligent analysis is performed by adopting a calculation model based on a big data system, so that optimal scheduling is performed based on user link experience.
The above description is only for the preferred embodiment of the present invention, and should not be used to limit the scope of the claims of the present invention. While the foregoing description will be understood and appreciated by those skilled in the relevant art, other equivalents may be made thereto without departing from the scope of the claims.
Claims (1)
1. An intelligent scheduling system, comprising: the system comprises a video server, a management server, a monitoring center, a client, a node management module, a cache module and a scheduling module;
the intelligent scheduling system is provided with a plurality of clients, and each client is a node; each client is respectively connected with the video server, the management server and the monitoring center; the video server is connected with the management server; the intelligent scheduling system stores data by using a Hadoop distributed file system and realizes cloud computing by using Map Reduce in Hadoop;
the video server is used for publishing and storing video files; the video server divides the video file into data blocks with equal size; the video server transmits the data block information to the management server;
the management server is used for managing client information;
the node management module, the cache module and the scheduling module are all integrated on the client;
the node management module is used for managing the information of the node and the partner node and registering the node and the partner node to the management server; the cache module is used for caching data blocks and forwarding the data blocks for the partner node, and the caching of the data blocks is realized through the Hadoop distributed file system; the scheduling module is used for making a scheduling scheme and sending a data request to a corresponding partner node according to the scheduling scheme;
the intelligent scheduling system comprises the following working steps:
1) a user selects a video publishing website at the client side by means of mouse clicking or keyboard input, and enters the intelligent scheduling system;
2) the node management module registers on the management server, and the registered content comprises an IP address, a port number and bandwidth of a node and a video program to be watched;
3) the management server returns to the node management module 20 partner nodes, wherein the partner node is a node which is watching the video program to be watched, and the 20 partner nodes are 20 nodes which are closest to the IP address of the node;
4) the scheduling module firstly reads the past video watching behaviors of a user and the behaviors of other nodes on a network watching the video program to be watched, a model is built by using Map Reduce in Hadoop, the next jump point of the user is predicted through a hidden Markov chain, the next jump point is the position of a data block which is played when the user finishes watching the video program to be watched, and the data block before the next jump point needs to be cached;
5) the scheduling module sends a data request to the corresponding partner node and caches a data block;
the monitoring center is used for evaluating a scheduling scheme and modifying the scheduling scheme; the monitoring center utilizes the Map Reduce in the Hadoop to calculate the system pressure of the client in real time, and the system pressure is calculated by a formula I:
the formula I is as follows:
wherein ω is the system pressure; a. b, c and d are proportionality coefficients which are rational numbers between 0 and 1, and are adjusted by background personnel according to needs, and the sum of the proportionality coefficients is 1; p is the utilization rate of the uplink bandwidth, which is the ratio of the used uplink bandwidth to the total bandwidth; q is server pressure, which is the ratio of the total number of data blocks stored by the video server to the total number of data blocks being played; r is starting delay, which is the time interval between all data blocks of the video played in the first 5s after the user enters the intelligent scheduling system; t is the playing quality, which is the ratio of the total number of data blocks actually obtained by the node of the video program at the current time to the total number of data blocks that should be obtained; the omega, the P, the Q, the R and the T are rational numbers between 0 and 1;
and when the system pressure is less than 0.5, the monitoring center immediately stops the scheduling scheme, background personnel modify the scheduling scheme and execute the scheduling scheme until the system pressure is not less than 0.5, and then the scheduling module is started to make a new scheduling scheme and execute the scheduling scheme.
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