CN113626161B - Distributed multi-user data scheduling method and system - Google Patents
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Abstract
The invention discloses a data scheduling method and a system for distributed multi-users, comprising the steps of obtaining routing information of a working node B bound by a user B1 by analyzing scheduling data sent by the user A1, and generating a scheduling task T i The method comprises the steps of carrying out a first treatment on the surface of the Will schedule task T i Storing the task queues in corresponding task queues; parsing each scheduling task T i And classifies the batch execution of data scheduling tasks to enable user B1 to obtain the scheduling data from the private data queue of the working node B. The invention dispersedly binds different users on dispersed data dispatching working nodes, dispatches the data among the users, adopts an asynchronous batch classification executing strategy, and uses the management node to manage the working nodes, thereby improving the dispatching load capacity, stability and reliability.
Description
Technical Field
The present invention relates to the field of network communications, and in particular, to a method and system for data scheduling of distributed multiple users.
Background
The distributed scheduling of big data plays a role in the data migration process, and runs through the processes of data production, exchange, consumption and the like. The realization of the scheduling of the computer task can be triggered by depending on the timing task of the operating system, but the scheduling of the task is orderly operated in a service scene, so that the characteristics of low delay, accuracy, reliability, convenience, monitoring and the like of the data scheduling are very complex. The distributed scheduling is developed on the basis of single point scheduling, and is a qualitative process from single point scheduling to distributed scheduling, and has many characteristics which are not possessed by single point scheduling, such as decentralized service, flexible expansion, multi-link scheduling and multi-destination scheduling. In the case of distributed scheduling, how to look at each scheduled task state as well as the scheduling node state is an important issue. In addition, in the service scenario of multiple users, how to provide a unified data scheduling interface and meet the data isolation requirement of user scheduling is also an important problem. Therefore, it is very difficult to design a scheduling system and a scheduling method thereof that meet the above requirements.
Disclosure of Invention
In order to overcome the existing difficulty, the invention provides a data scheduling method and system for distributed multi-users, which utilizes sixteen modules such as a node management module, a user management module, a topology updating module, a link detection module, a task generation module, a task execution module, a task retransmission module, a data consumption module, a task monitoring module, a task reporting module, a state acquisition module, a state reporting module, a task checking module, a state checking module, a command issuing module and a command execution module, and realizes the characteristics of decentralization, low delay, reliability, stability, user data isolation, convenience in checking of scheduling tasks, manageability of scheduling service working nodes and the like of data scheduling services.
The technical scheme of the invention comprises the following steps:
a data scheduling method of distributed multi-user is applicable to a network composed of a management node and a plurality of working nodes, the management node respectively generates unique working node route information or user ID according to the information of each working node and the user information, the steps include:
1) The working node A periodically inquires the global topology and acquires global topology information from the management node;
2) Analyzing the scheduling data sent by the user A1, acquiring the data type, the scheduling data body, the priority and the user ID of the user B1 of the scheduling data, and acquiring the routing information of the working node B bound by the user B1 according to the user ID and the global topology information of the user B1;
3) Generating a scheduling task T according to the generated data scheduling task ID, the user ID of the user B1, the routing information of the working node B, the data type and the scheduling data body i And combine the priority to schedule task T i Storing the task number i into a corresponding task queue;
4) Parsing each scheduling task T i And classifies the batch execution of data scheduling tasks to enable user B1 to obtain the scheduling data from the private data queue of the working node B.
Further, the interface mode for transmitting the scheduling data by the user A1 includes: HTTP/HTTPS RESTful interface mode.
Further, the interface URL is: v1/send_data/{ data_type }, where { data_type } represents a data type; the interface HTTP header field includes: a USER-AGENT field, a LEVEL field, and a DES-USER field, wherein the USER-AGENT field is a USER ID of the USER A1, the LEVEL field is a priority of data, and the DES-USER field is a USER ID of the USER B1; the scheduling data body is stored in the http request body.
Further, the generation rule of the data scheduling task ID includes: working node number_event stamp_n-bit random number, where n.gtoreq.1.
Further, if the scheduling data is not successfully scheduled to the working node B, the failed task is stored in the task retransmission queue and re-executed until the task retransmission is successfully executed.
Further, the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task are recorded, and the record is reported to the management node.
Further, reporting the server hardware state, the service index state network bandwidth occupation state and the command execution state of the working node to a management node; the server hardware state includes: CPU utilization rate, memory utilization rate and disk utilization rate; the business index state includes: task queue backlog number and user data queue backlog number; the network bandwidth occupancy state includes: network egress traffic speed and network ingress traffic speed.
Further, the working node A1 immediately executes the command instruction which is obtained from the management node and is not executed, and returns the recorded command execution state to the management node
A data scheduling system of distributed multi-user is applicable to a network composed of a management node and a plurality of working nodes, comprising:
the node management module is used for managing the nodes to generate unique working node routing information according to the information of each working node;
the user management module is used for generating a unique user ID according to the user information by the management node;
the topology updating module is used for periodically inquiring global topology by the working node A and acquiring global topology information from the management node;
the task generating module is used for analyzing the scheduling data sent by the user A1, acquiring the data type, the scheduling data body, the priority and the user ID of the user B1 of the scheduling data, and acquiring the routing information of the working node B bound by the user B according to the user ID and the global topology information of the user B1; generating a scheduling task T according to the generated data scheduling task ID, the user ID of the user B1, the routing information of the working node B, the data type and the scheduling data body i And combine the priority to schedule task T i Storing the task number i into a corresponding task queue;
task execution module for analyzing each scheduling task T i The routing information of the data scheduling task is classified and executed in batches;
and the data consumption module is used for the working node A to store the user data of the user B1 in the message middleware in an isolated way, so that the user B1 can consume the data scheduled to the user from the working node B1 to other users.
Further, the system also comprises a link detection module, a task retransmission module, a task monitoring module, a task reporting module, a state acquisition module, a state reporting module, a task checking module, a state checking module, a command issuing module and a command executing module; wherein,
the link detection module is used for inquiring the topological structure by the working node A;
the task retransmission module is used for storing the failed task to the task retransmission queue and re-executing if the scheduling data is not successfully scheduled to the working node B;
the task monitoring module is used for recording the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task;
the task reporting module is used for reporting the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task to the management node;
the system comprises a state acquisition module, a control module and a control module, wherein the state acquisition module is used for acquiring a server hardware state, a service index state, a network bandwidth occupation state and a command execution state of a working node, wherein the server hardware state comprises: CPU utilization rate, memory utilization rate and disk utilization rate; the business index state includes: task queue backlog number and user data queue backlog number; the network bandwidth occupancy state includes: network egress traffic speed and network ingress traffic speed;
the state reporting module is used for reporting the server hardware state, the service index state and the network bandwidth occupation state of the working node to the management node;
the task checking module is used for checking the information of each data scheduling task, the execution progress and the consumption information of the scheduling data;
the state checking module is used for checking the system resource hardware state, the service index state and the network bandwidth occupation state of each working node through the management node;
the command issuing module is used for issuing commands to the designated working nodes through the management nodes, wherein the commands comprise: system restart, cache data cleaning or system update;
the command execution module is used for periodically downloading and executing the unexecuted commands from the management node by the working node, and recording the command execution state.
The method of the invention has the following advantages:
different users are bound in scattered data scheduling working nodes in a scattered mode, data scheduling among the users is performed by adopting an asynchronous batch classification executing strategy, and the working nodes are managed by using a management node. Compared with single-machine data scheduling, the scheduling load capacity, stability and reliability are greatly improved. The invention also provides a perfect scheduling task state monitoring method which comprises the processes of generating, executing, retransmitting and the like of the scheduling task and provides unified data scheduling task state query service through the management node.
Drawings
Fig. 1 is a system architecture diagram of the present invention.
Fig. 2 is a flow chart of the data scheduling of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme adopted by the invention mainly comprises sixteen modules: (1) a node management module: firstly, dividing a scheduling node into a management node and a working node, and registering information of the working node in the management node, wherein the information comprises an IP address and a service port of the working node. The management node generates an identity ID and a communication configuration for each node. The communication configuration content contains the IP address and service port of the management node and the identity ID of the working node. The working node may communicate with the management node via a communication arrangement. (2) a user management module: registering information of a user in a management node, binding the user with a working node, generating a unique user ID for the user, and providing the user with the IP address and service port information of the working node bound by the user. (3) a topology upgrade module: the working nodes periodically acquire the latest topological structure from the management nodes, wherein the topological structure comprises information of all the working nodes and information of users bound with each working node. (4) a link detection module: the working node inquires the topological structure, and periodically sends link detection packets to other working nodes to acquire the network connection state. (5) a task generating module: the user sends the data to be scheduled to other users through the bound working nodes, the working nodes can generate data scheduling tasks, the priorities of the tasks are distinguished, and the tasks are stored in task queues of different levels. (6) a task execution module: the work node gathers the data scheduling tasks, and preferentially gathers the scheduling tasks with high priority. And obtaining a link of each task from the topological structure, classifying and converging the dispatching tasks according to the links, executing the converged dispatching tasks, and dispatching the data to the working nodes bound by the destination end user. (7) task resending module: and the task which is to be failed to be executed is supported to be retransmitted, so that the reliability of data scheduling is improved. (8) a data consumption module: the working node stores the destination end user data in the message middleware in an isolated manner, and the destination end user can consume data scheduled to the destination end user from the bound working node to other users. (9) a task monitoring module: and recording events occurring in each stage of the scheduled task for monitoring, wherein the events comprise scheduled task generation, scheduled task execution, scheduled task retransmission, scheduled data consumption and the like. And (10) a task reporting module: each working node reports the new task monitoring record to the management node at regular time. (11) a state acquisition module: the state of the working node is collected, and the state comprises a hardware state of a working node system, a scheduling task queue state, a data consumption queue state, a network bandwidth use state, a command execution state and the like. (12) a status report module: the working node reports the newly generated state information to the management node at regular time. (13) a task viewing module: the management node can check the information of each data scheduling task, the execution progress and the consumption information of the scheduling data. (14) a status viewing module: the management node can check the hardware state of the system resource, the state of the service index and the occupied state of the network bandwidth of each working node. (15) a command issuing module: the management node can issue commands to the designated working node, wherein the commands comprise commands such as system restart, cache data cleaning, system update and the like. (16) a command execution module: the working node periodically downloads and executes the unexecuted command from the management node, and records the command execution state.
In the system architecture of an embodiment of the present invention, as shown in fig. 1, when the distributed data scheduling system is deployed, one server is designated as a management node, and the other servers are designated as working nodes. The firewall open ports 443 of all nodes are used for providing services to the outside. The service ports of each node of the TELNET can test whether the service exists.
The information of each working node needs to be registered on a management node, the management node can generate a unique ID for the working node, the ID is a four-digit number and is numbered from 1000, and therefore, a single distributed scheduling cluster can accommodate 9000 working nodes at maximum. And then downloading the communication configuration file of each node on the management node. After each working node configures the communication configuration file, the working node can communicate with the management node to perform the processes of topology updating, task reporting, state reporting, command issuing and the like.
The user information needs to be registered on the management node, the binding working node can be selected by the user, then the management node can generate a unique user ID (identity) for the user, the ID is a 12-bit number, and the generation rule is as follows: the user registration year_work node number_user type_four-bit sequence number, for example 211000010001, represents that the user is registered in 2021, the work node is selected to be bound 1000, the user type is 01, and the user is registered in 0001 th bit. The management node then feeds back the user ID, the IP address of the bound worker node and the service port to the user. The user can query the management node for the IDs of other users. And the user performs a data scheduling flow through the bound working nodes.
The specific implementation manner of the processes is as follows:
the first topology updating process is that the working node obtains the total node information and the user information of each node from the management node, so that the working node can obtain the route information, namely the IP address and the service port, of the working node bound by the target user of the data scheduling task from the global topology information.
The second task reporting process is that the working node records the processes of generating the data scheduling task, executing the data scheduling task, retransmitting the data scheduling task and the like, and then reports the records to the management node. The user can inquire the execution flow state of the task from the management node through the data scheduling task ID. The data scheduling task ID is a unique number generated by the working node, and the generation rule is as follows: the working node number_timestamp_three-bit random number, and the form of the event stamp is as follows: y_m_d_h_m_s_f. Wherein, each symbol has the meaning of: y-year, M-month, D-day, H-time, M-minutes, S-seconds, F-microseconds.
And the third status reporting process is that the working node records the server hardware status, the service index status and the network bandwidth occupation status of the node and reports the recorded status to the management node. The server hardware state includes CPU usage, memory usage, disk usage. The business index state comprises the backlog number of the task queues and the backlog number of the user data queues. Network bandwidth occupancy includes network egress traffic speed and network ingress traffic speed. The user can query from the management node whether there is data scheduled to him by other users.
The fourth command issuing process is that the working node obtains the command instruction which is not executed from the management node, if the command instruction which is not executed is obtained, the command instruction is immediately executed, and the command execution state is recorded. The command execution status can be viewed by the management node. In the distributed case, after a single device fails, the failure phenomenon of the working node can be solved by issuing an appointed command to the failed device. Commands refer to scripts written in the shell programming language.
The data scheduling process is a process that a user schedules data to other users through the working nodes, and the other users acquire the data from the bound working nodes. As shown in fig. 2, when data of the user A1 is scheduled to the user B1, first, the user A1 sends the data to the bound working node a, and sends the data through the HTTP/HTTPs RESTful interface. The interface URL is: v1/send_data/{ data_type }, represents the data type. The HTTP header field of the interface comprises fields such as USER-AGENT, LEVEL, DES-USER, the USER-AGENT field is the USER ID of the USER A1, the LEVEL field is the priority of the data, and the value range is as follows: 0,1,2, respectively represent normal, medium, priority, DES-USER field is USER ID of USER B1. The scheduling data body is stored in the http request body, and the type is binary. The working node A inquires the global topology, acquires the routing information of the binding working node B of the user B1, and generates the current data scheduling task ID. And packaging and storing the data scheduling task ID, the routing information, the user ID of the user A1, the user ID of the user B1, the data type and the scheduling data body into corresponding priority task queues, and feeding back the data scheduling task ID to the user A1. The work node A sequentially reads the tasks of the task queue according to the priority, analyzes the route information of each task and classifies the tasks, and uses multithreading to execute the tasks of the batch according to the route classification in batches. After the data is successfully scheduled to the working node B, the working node B stores the scheduled data into the private data queue of the user B1. If the data is not successfully scheduled to the working node B, the working node A stores the task which fails to be executed into a task retransmission queue, and the priority of the task retransmission queue is given priority. If the task retransmission fails, the failed task is still stored in the task retransmission queue until the task retransmission is successfully executed. After the working node B acquires the data packets sent by other working nodes, the working node B analyzes the data packets to acquire the ID of the user receiving the data packets, and caches the data into a data queue of the user ID. User B1 obtains data from the working node B via HTTP/HTTPs RESTful interface. The interface URL is: v1/get_data. And acquiring the data packet and the binary type from the HTTP feedback of the interface. The user B1 may parse the packet to obtain: data scheduling task ID, routing information, user ID of user A1, user ID of user B1, data type, scheduling data body, and the like.
The above examples are provided for the purpose of describing the present invention only and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalents and modifications that do not depart from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A data scheduling method of distributed multi-user is applicable to a network composed of a management node and a plurality of working nodes, the management node respectively generates unique working node route information or user ID according to the information of each working node and the user information, the steps include:
1) The working node A periodically inquires the global topology and acquires global topology information from the management node;
2) Analyzing the scheduling data sent by the user A1, acquiring the data type, the scheduling data body, the priority and the user ID of the user B1 of the scheduling data, and acquiring the routing information of the working node B bound by the user B1 according to the user ID and the global topology information of the user B1;
3) Generating a scheduling task T according to the generated data scheduling task ID, the user ID of the user B1, the routing information of the working node B, the data type and the scheduling data body i And combine the priority to schedule task T i Storing the task number i in a corresponding task queue；
4) Parsing each scheduling task T i And classifies the batch execution of data scheduling tasks to enable user B1 to obtain the scheduling data from the private data queue of the working node B.
2. The method of claim 1, wherein the interface manner in which the user A1 transmits the scheduling data comprises: HTTP/HTTPS RESTful interface mode.
3. The method of claim 2, wherein the interface URL is: v1/send_data/{ data_type }, where { data_type } represents a data type; the interface HTTP header field includes: a USER-AGENT field, a LEVEL field, and a DES-USER field, wherein the USER-AGENT field is a USER ID of the USER A1, the LEVEL field is a priority of data, and the DES-USER field is a USER ID of the USER B1; the scheduling data body is stored in the http request body.
4. The method of claim 1, wherein the generation rule of the data scheduling task ID comprises: working node number_event stamp_n-bit random number, where n.gtoreq.1.
5. The method of claim 1, wherein if the scheduled data is not successfully scheduled to the worker node B, the failed task is stored in a task retransmission queue and re-executed until the task retransmission is successfully executed.
6. The method according to claim 1, characterized in that the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task are recorded and the record is reported to the management node.
7. The method of claim 1, wherein server hardware status, traffic index status, network bandwidth occupancy status, and command execution status of the present working node are reported to the management node; the server hardware state includes: CPU utilization rate, memory utilization rate and disk utilization rate; the business index state includes: task queue backlog number and user data queue backlog number; the network bandwidth occupancy state includes: network egress traffic speed and network ingress traffic speed.
8. The method of claim 1, wherein the working node a immediately executes the command instruction acquired from the management node not executed and returns the recorded command execution status to the management node.
9. A data scheduling system of distributed multi-user is applicable to a network composed of a management node and a plurality of working nodes, comprising:
the node management module is used for managing the nodes to generate unique working node routing information according to the information of each working node;
the user management module is used for generating a unique user ID according to the user information by the management node;
the topology updating module is used for periodically inquiring global topology by the working node A and acquiring global topology information from the management node;
the task generating module is used for analyzing the scheduling data sent by the user A1, acquiring the data type, the scheduling data body, the priority and the user ID of the user B1 of the scheduling data, and acquiring the routing information of the working node B bound by the user B1 according to the user ID of the user B1 and the global topology information; generating a scheduling task T according to the generated data scheduling task ID, the user ID of the user B1, the routing information of the working node B, the data type and the scheduling data body i And combine the priority to schedule task T i Storing the task number i into a corresponding task queue;
task execution module for analyzing each scheduling task T i The routing information of the data scheduling task is classified and executed in batches;
and the data consumption module is used for the working node A to store the user data of the user B1 in the message middleware in an isolated way, so that the user B1 can consume the data scheduled to the user from the working node B to other users.
10. The system of claim 9, further comprising a link detection module, a task resending module, a task monitoring module, a task reporting module, a status acquisition module, a status reporting module, a task viewing module, a status viewing module, a command issuing module, a command execution module; wherein,
the link detection module is used for inquiring the topological structure by the working node A;
the task retransmission module is used for storing the failed task to the task retransmission queue and re-executing if the scheduling data is not successfully scheduled to the working node B;
the task monitoring module is used for recording the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task;
the task reporting module is used for reporting the generation process of the data scheduling task, the execution process of the data scheduling task and/or the retransmission process of the data scheduling task to the management node;
the system comprises a state acquisition module, a control module and a control module, wherein the state acquisition module is used for acquiring a server hardware state, a service index state, a network bandwidth occupation state and a command execution state of a working node, wherein the server hardware state comprises: CPU utilization rate, memory utilization rate and disk utilization rate; the business index state includes: task queue backlog number and user data queue backlog number; the network bandwidth occupancy state includes: network egress traffic speed and network ingress traffic speed;
the state reporting module is used for reporting the server hardware state, the service index state and the network bandwidth occupation state of the working node to the management node;
the task checking module is used for checking the information of each data scheduling task, the execution progress and the consumption information of the scheduling data;
the state checking module is used for checking the system resource hardware state, the service index state and the network bandwidth occupation state of each working node through the management node;
the command issuing module is used for issuing commands to the designated working nodes through the management nodes, wherein the commands comprise: system restart, cache data cleaning or system update;
the command execution module is used for periodically downloading and executing the unexecuted commands from the management node by the working node, and recording the command execution state.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101309201A (en) * | 2007-05-14 | 2008-11-19 | 华为技术有限公司 | Route processing method, routing processor and router |
| CN101340331A (en) * | 2007-07-06 | 2009-01-07 | 中国电信股份有限公司 | Method for executing system task by idle terminal in P2P network |
| CN102084628A (en) * | 2008-04-24 | 2011-06-01 | 厄塞勒拉特公司 | A traffic manager and a method for a traffic manager |
| CN103279351A (en) * | 2013-05-31 | 2013-09-04 | 北京高森明晨信息科技有限公司 | Method and device for task scheduling |
| CN106126346A (en) * | 2016-07-05 | 2016-11-16 | 东北大学 | A kind of large-scale distributed data collecting system and method |
| CN108268317A (en) * | 2016-12-30 | 2018-07-10 | 华为技术有限公司 | A kind of resource allocation methods and device |
| CN111367630A (en) * | 2019-07-12 | 2020-07-03 | 北京关键科技股份有限公司 | Multi-user multi-priority distributed cooperative processing method based on cloud computing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10447608B2 (en) * | 2014-11-14 | 2019-10-15 | Marvell Semiconductor, Inc. | Packet scheduling using hierarchical scheduling process with priority propagation |
-
2021
- 2021-07-09 CN CN202110778053.6A patent/CN113626161B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101309201A (en) * | 2007-05-14 | 2008-11-19 | 华为技术有限公司 | Route processing method, routing processor and router |
| CN101340331A (en) * | 2007-07-06 | 2009-01-07 | 中国电信股份有限公司 | Method for executing system task by idle terminal in P2P network |
| CN102084628A (en) * | 2008-04-24 | 2011-06-01 | 厄塞勒拉特公司 | A traffic manager and a method for a traffic manager |
| CN103279351A (en) * | 2013-05-31 | 2013-09-04 | 北京高森明晨信息科技有限公司 | Method and device for task scheduling |
| CN106126346A (en) * | 2016-07-05 | 2016-11-16 | 东北大学 | A kind of large-scale distributed data collecting system and method |
| CN108268317A (en) * | 2016-12-30 | 2018-07-10 | 华为技术有限公司 | A kind of resource allocation methods and device |
| CN111367630A (en) * | 2019-07-12 | 2020-07-03 | 北京关键科技股份有限公司 | Multi-user multi-priority distributed cooperative processing method based on cloud computing |
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