CN113641495A - Distributed scheduling method and system based on big data calculation - Google Patents
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
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- G06F9/44—Arrangements for executing specific programs
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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Abstract
The invention relates to a distributed scheduling method and a distributed scheduling system based on big data calculation, wherein the distributed scheduling method comprises the following steps: creating an exclusive resource space Zone for the user according to the application of the user; a user creates a plurality of task channels in a resource space Zone, creates and submits a task Job of the user to a CR and distributes the task Job to the corresponding channels; the Channel calls the RM to allocate containers to each node in the Channel, and issues a notice to the NM and runs the AM, and meanwhile, the NM monitors the running state of Job in real time and splits the Job into tasks; and the AM distributes the task to the other node and monitors the running state of the task in real time. The channel is divided in the zone space, so that the jobtask of the user is isolated, the mutual resource competition of task levels is avoided, and the tasks of different levels can adopt server hardware configuration of different levels.
Description
Technical Field
The invention relates to the technical field of big data distributed resource scheduling, in particular to a distributed scheduling method and system based on big data calculation.
Background
In a Hadoop2.0 distributed cluster, Hadoop consists of MapReduce, HDFS and Yarn, wherein the MapReduce is a distributed computing framework, the HDFS is responsible for storage, and the Yarn is an independent resource management framework responsible for resource management and scheduling; all the computing tasks submitted to the big data cluster are managed by Yarn, which is responsible for allocating resources and scheduling, so that the resource allocation, resource competition and task priority of the cluster are managed by the Yarn.
While the Scheduler of Yarn only supports FIFO (first in first out), Capacity (queue sharing) and Fair (Fair allocation). However, in all three modes, resource competition and mutual exclusion exist, multi-user isolation of resources cannot be realized, and therefore when a large number of concurrent tasks are scheduled and executed simultaneously, task scheduling is stuck or even deadlocked due to resource problems, user tasks cannot be operated in an isolated manner, and large-area tasks may not be executed according to a normal scheduling plan.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a distributed scheduling method and a distributed scheduling system based on big data calculation, and solves the problems that resources are wasted and the normal execution of calculation tasks is influenced due to unreasonable application and use of resources under the situation of resource competition of multi-user and multi-task concurrent operation in a big data cluster environment.
The purpose of the invention is realized by the following technical scheme: a distributed scheduling method based on big data calculation, the distributed scheduling method comprising:
creating an exclusive resource space Zone for the user according to the application of the user;
a user creates a plurality of task channels in a resource space Zone, and creates and submits a task Job of the user to a ChannelRoute, and the ChannelRoute distributes the received Job to the corresponding channels;
the Channel calls a resource manager to allocate containers to each node in the Channel, and issues a notice to a NodeManger and runs an AppMaster, and meanwhile, the NodeManger monitors the running state of Job in real time and splits the Job into tasks;
the AppMaster distributes the task to another node, monitors the running state of the task in real time, sends heartbeat data to a Resourcemanager and logs off to release resources.
The assigning, by the ChannelRoute, the received Job to the corresponding Channel comprises:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
Each time the user issues a task Job, the amount of resource space of the size of the task Job is subtracted from the resource space Zone, and when the task Job is deleted, the amount of resource space of the size of the task Job is restored.
Different Channel resources in a plurality of task channels created in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among Jobs of different levels, and the Jobs of different levels adopt server hardware configurations of different levels.
A distributed scheduling system based on big data calculation comprises a creating module, a scheduling module and a scheduling module, wherein the creating module is used for creating an exclusive resource space Zone and creating a plurality of task channels in the resource space Zone according to the requirements of users;
ChannelRoute module: the system comprises a Channel, a task Job, a resource allocation module and a resource allocation module, wherein the Channel is used for receiving the task Job submitted by a user and allocating the task Job to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space;
ResourceManager module: and allocating containers and computing nodes in the cluster to each node in the Channel according to Job allocated by the ChannelRoute module.
Each Channel comprises a plurality of nodes, and each node comprises a NodeManger, an AppMaster and a Container distributed by the Resourcemanager module; the NodeManger is used for receiving the notification sent by the Resourcemanager module, monitoring the running state of Job in real time and splitting the Job into tasks; the AppMaster is used for distributing the task to another node, monitoring the running state of the task in real time, sending heartbeat data to a Resourcemanager, and logging off to release resources.
The allocating to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space comprises:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
Different Channel resources in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among different levels of Job, and different levels of Job adopt different levels of server hardware configuration.
The invention has the following advantages: a distributed scheduling method and a distributed scheduling system based on big data calculation are provided, wherein channels are divided in a zone space, so that job tasks of users are isolated, service requirements are higher, lower and temporary job tasks are better managed, mutual resource competition of task levels is avoided, and tasks of different levels can adopt server hardware configuration of different levels. The resource and task management is clearer and more reasonable, and the operation and maintenance requirements of better capacity expansion in the later stage of the server resources are facilitated.
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FIG. 1 is a schematic structural diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided below in connection with the appended drawings is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention relates to a distributed scheduling method based on big data computation, where the distributed scheduling method includes:
according to the application of a user for an exclusive resource space, the exclusive resource space comprises the number of cores of a CPU, the size of a memory, the size of a hard disk and the network bandwidth, and an exclusive resource space Zone is created for the exclusive resource space;
a user (Client) creates a plurality of task channels in a resource space Zone, and creates and submits a task Job of the user to ChannelRoute (CR), and the ChannelRoute allocates the received Job to the corresponding Channel;
the resource space zone is created by the user through self initialization, and once the resource space zone is created, the size of the resource space zone is fixed on the premise of not upgrading and expanding the capacity; the resource space is not equal to the server cluster, and the resource space is a network logic space, is not limited to a specific server cluster, and is a distributed concept; the channels are constructed in a resource space, and it can be clear that each Channel is necessarily attributed to a specific server cluster (that is, the cluster is created according to performance and reliability), and each Channel has specific requirements for execution and scheduling performance, and is allocated to a specific cluster environment in the resource space according to the requirements; the job task belongs to a specific user, namely belongs to a specific zone, and then is issued to a specific Channel according to the performance requirement of the job; and then according to the resource occupation condition of the server cluster to which the Channel belongs, handing joba to Resource Manager (RM) timely, and then allocating the computing nodes in the cluster by the RM.
The Channel calls a resource manager to allocate Node containers to a Node A (Node A) and a Node B (Node B) in the Channel, and issues a notice to a NodeManger (Node management) and runs an AppMaster (user operation life cycle management), and meanwhile, the NodeManger monitors the running state of Job in real time and divides the Job into tasks;
the AppMaster distributes a task to another Node B (Node B) and monitors the running state of the task in real time, and sends heartbeat data to a ResourceManager and cancels to release resources.
Further, the assigning of the received Job to the corresponding Channel by the ChannelRoute includes:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
Since the creation of Job is performed in zone, the resource demand of Job is determined preferentially. And then, the CR of the zone routes to the appointed Channel according to the service performance requirement of Job, and finally, the Channel is dispatched to a specific server cluster again according to the cluster resource use condition.
Each time the user issues a task Job, the amount of resource space of the size of the task Job is subtracted from the resource space Zone, and when the task Job is deleted, the amount of resource space of the size of the task Job is restored. The zone is equal to the sum of all the used Channel space sizes of the zone plus the residual space size of the zone, and the Channel resource size is equal to the used resource space size.
Job is a home subscriber's zone, if the zone space is insufficient, execution can be directly refused, namely the space resource is exclusive, when Job issues to a specific Channel, the Channel can efficiently manage the resource use condition, and each Channel is a running Job with the same performance requirement and peculiarity; different Channel resources in a plurality of task channels created in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among Jobs of different levels, and the Jobs of different levels adopt server hardware configuration of different levels.
Another embodiment of the present invention includes a distributed scheduling system based on big data computation, which includes a creating module, where the creating module is configured to create an exclusive resource space Zone, and create a plurality of task channels in the resource space Zone according to the requirements of users;
ChannelRoute module: the system comprises a Channel, a task Job, a resource allocation module and a resource allocation module, wherein the Channel is used for receiving the task Job submitted by a user and allocating the task Job to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space;
ResourceManager module: and allocating containers and computing nodes in the cluster to each node in the Channel according to Job allocated by the ChannelRoute module.
Each Channel comprises a plurality of nodes, and each node comprises a NodeManger, an AppMaster and a Container distributed by the Resourcemanager module; the NodeManger is used for receiving the notification sent by the Resourcemanager module, monitoring the running state of Job in real time and splitting the Job into tasks; the AppMaster is used for distributing the task to another node, monitoring the running state of the task in real time, sending heartbeat data to a Resourcemanager, and logging off to release resources.
The allocating to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space comprises:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
Different Channel resources in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among different levels of Job, and different levels of Job adopt different levels of server hardware configuration.
According to the invention, each user has a zone space shared by the user, so that mutual interference is avoided, and the security of the jobs and the data is ensured. The channels are divided in the zone space, so that the job tasks of the users are isolated, the service requirements are higher, lower and temporary job tasks are better managed, the mutual resource competition of task levels is avoided, and the tasks of different levels can adopt server hardware configurations of different levels. The resource and task management is clearer and more reasonable, and the operation and maintenance requirements of better capacity expansion in the later stage of the server resources are facilitated. The system saves not only human resources and issuing efficiency, but also reasonably saves hardware resources and network resources, simultaneously improves the utilization efficiency of the resources, enables services and the resources to be hooked, can realize priority guarantee of important services, avoids mutual interference of tasks of different levels, competes with each other, winds the resources and improves the management efficiency.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A distributed scheduling method based on big data calculation is characterized in that: the distributed scheduling method comprises the following steps:
creating an exclusive resource space Zone for the user according to the application of the user;
a user creates a plurality of task channels in a resource space Zone, and creates and submits a task Job of the user to a ChannelRoute, and the ChannelRoute distributes the received Job to the corresponding channels;
the Channel calls a resource manager to allocate containers to each node in the Channel, and issues a notice to a NodeManger and runs an AppMaster, and meanwhile, the NodeManger monitors the running state of Job in real time and splits the Job into tasks;
the AppMaster distributes the task to another node, monitors the running state of the task in real time, sends heartbeat data to a Resourcemanager and logs off to release resources.
2. The distributed scheduling method based on big data computation of claim 1, wherein: the assigning, by the ChannelRoute, the received Job to the corresponding Channel comprises:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
3. The distributed scheduling method based on big data computation of claim 1, wherein: each time the user issues a task Job, the amount of resource space of the size of the task Job is subtracted from the resource space Zone, and when the task Job is deleted, the amount of resource space of the size of the task Job is restored.
4. The distributed scheduling method based on big data computation of claim 1, wherein: different Channel resources in a plurality of task channels created in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among Jobs of different levels, and the Jobs of different levels adopt server hardware configurations of different levels.
5. A distributed scheduling system based on big data computation is characterized in that: the system comprises a creating module, a processing module and a processing module, wherein the creating module is used for creating an exclusive resource space Zone and creating a plurality of task channels in the resource space Zone according to the requirements of users;
ChannelRoute module: the system comprises a Channel, a task Job, a resource allocation module and a resource allocation module, wherein the Channel is used for receiving the task Job submitted by a user and allocating the task Job to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space;
ResourceManager module: and allocating containers and computing nodes in the cluster to each node in the Channel according to Job allocated by the ChannelRoute module.
6. The big data computing-based distributed scheduling system of claim 5, wherein: each Channel comprises a plurality of nodes, and each node comprises a NodeManger, an AppMaster and a Container distributed by the Resourcemanager module; the NodeManger is used for receiving the notification sent by the Resourcemanager module, monitoring the running state of Job in real time and splitting the Job into tasks; the AppMaster is used for distributing the task to another node, monitoring the running state of the task in real time, sending heartbeat data to a Resourcemanager, and logging off to release resources.
7. The big data computing-based distributed scheduling system of claim 5, wherein: the allocating to the corresponding Channel according to the performance requirement of the task Job and the size of the required resource space comprises:
judging the performance requirement of the Job to be issued and the size of the required resource space, and matching the required resource space with the residual space of the resource space Zone according to the size of the required resource space;
when the matching is not satisfied, refusing to execute Job distribution and issuing, and issuing a capacity expansion prompt to the user;
and when the matching is satisfied, dividing the cluster into specific channels according to the performance requirement, issuing the cluster to a resource manager according to the resource space amount required by the Job, and determining whether the current cluster satisfies the resource space amount required by the Job, if so, issuing, and if not, waiting.
8. The big data computing-based distributed scheduling system of claim 5, wherein: different Channel resources in the resource space Zone are physically isolated, each Channel only corresponds to one task Job so as to avoid resource competition among different levels of Job, and different levels of Job adopt different levels of server hardware configuration.
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| CN105045656A (en) * | 2015-06-30 | 2015-11-11 | 深圳清华大学研究院 | Virtual container based big data storage and management method |
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Application publication date: 20211112 |