CN110399206B - IDC virtualization scheduling energy-saving system based on cloud computing environment - Google Patents
IDC virtualization scheduling energy-saving system based on cloud computing environment Download PDFInfo
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Abstract
The invention discloses an IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment, which comprises a CenterManager, an Executor, a Zoo-Keeper and a ClientAPI (application program interface), wherein the CenterManager is divided into a ResourceManager submodule and a JobScheduler submodule, and a user submits, inquires and controls operations to the CenterManager through a command line, WebPortal and the ClientAPI and inquires the resource state of an application cluster or a single node; according to the scheme, system resources need to be reasonably scheduled, the higher utilization rate of the system is guaranteed, the different customization requirements of the operation need to be fully met, a conversion mechanism of operation soft and hard constraints is established, batch processing operations of different types and the internet resident service application are effectively managed in a unified mode, the scale of the application sharing cluster is greatly increased, the application performance bottlenecks of different types and the characteristic of complementation of high and low peak time periods are fully utilized, the resource utilization rate of the application cluster is remarkably improved, and the total ownership cost of the data center is reduced.
Description
Technical Field
The invention belongs to the technical field of scheduling systems, and particularly relates to an IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment. Meanwhile, the invention also relates to a method for IDC virtualization scheduling of the energy-saving system based on the cloud computing environment.
Background
The internet service has the characteristics of multiple types, various performance bottlenecks, complex deployment process, large load fluctuation, dependency relationship among applications and the like, so different constraint conditions are provided for the resource requirements. There is a class of constraints that must be satisfied. Due to the limitations of the development and compilation environment, some applications may require that the version of the node operating system, GLIBC or GCC must be higher than a certain version; in order to ensure the performance, the application of image processing, biological DNA sequence matching and the like, a node is required to be configured with a high-performance GPU to replace a common CPU; matrix operation application requires a node to have a high-performance CPU and a large-capacity memory, and the like, and we call the constraint condition that must be satisfied as "hard constraint".
In addition, application programming models may also present some job constraints in order to optimize job performance. For example, Hadoop requires that jobs be issued to nodes storing data to run so as to save network bandwidth; network intensive applications require jobs to be issued to and run on nodes of the same or adjacent switches to make full use of the relatively high network bandwidth of the nodes of the same or adjacent switches. These constraints are usually required to be satisfied first, and we call "soft constraints".
To support a hybrid job, the job scheduling policy must satisfy the above-described multiple different types of job constraints simultaneously. The existing job constraint scheduling is oriented to batch processing application scenes, the requirements of batch processing and service type mixed application scenes cannot be met, the performance optimization requirements of diversified jobs of different types cannot be met, and the resource waste is serious.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment.
In order to achieve the purpose, the invention provides the following technical scheme: an IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment comprises a center manager module, an executive module, a Zoo-Keeper module and a ClientAPI (application programming interface) module, wherein the center manager module is divided into two sub-modules, namely a resource manager module and a JobScheduler module, and a user submits, queries and controls jobs to the center manager module through a command line, WebPortal and ClientAPI and queries the resource state of an application cluster or a single node; the JobScheduler is responsible for application grouping management and job scheduling, selects application groups with high priority and waiting jobs to perform job scheduling according to the resource quota of the application groups and the number of used resources, performs job scheduling according to a job scheduling algorithm selected by the application groups, and sends the selected job information to a resource manager to perform resource matching; the resource manager is responsible for state monitoring, management and job resource matching of all nodes of the application cluster, and the executive is responsible for starting, executing and state monitoring of jobs and regularly reports node resource state information to the Res0 resource manager; the zo-Keeper is a configuration center, and stores all modules and application cluster nodes, and meanwhile, Zoo-Keeper is configuration information of a core point of a high availability mechanism; it also functions as a name service; when the CenterManager is down or the service is unavailable, the backup CenterManager selects a new main CenterManager through the ZooKeeper; the JobScheduler writes all job running state information into the ZooKeeper in real time, and a user and an application can acquire the running state, node distribution, network port and other job information of the job through a job name; the CenterManager adopts a distributed file, solves the problems of submission and deployment of jobs, storage of job data and log information and data sharing among applications, and in order to improve the application performance, the system adopts a resource container instead of a traditional virtual machine to realize the performance and safety isolation of the applications; the CenterManager also includes a dynamic port: for jobs which do not require a fixed port, such as a business logic layer, a MySQL database and the like, a user sets a job attribute dynamic. When the resources are matched, the resource manager dynamically allocates an available port number for the node according to the occupation condition of the node port; when the Executor starts the operation, the port number is written to the ZooKeeper name service, so that the query of other operations is facilitated;
the CenterManager further comprises a dependent job module: the characteristic of large load fluctuation of service type jobs enables the deployment situation of jobs in the data center nodes to be dynamically changed; jobs can only be submitted by specifying the names of the jobs they depend on; when the job resources are matched, the resource manager queries a node list operated by the dependent job from the name service according to the job name to generate a job dependent constraint condition; the CenterManager further comprises a node failure handling module: similar to programming frames such as MapReduce and Hadoop, a set of processing mechanisms for dealing with automatic node failure needs to be formulated by a scheduling strategy; however, the automatic failure processing of the dependent jobs requires that the dependency relationship between jobs is maintained, that is, after the dependent jobs are rescheduled, issued and executed, the dependent jobs are automatically issued to the same node for running; this makes it necessary for the ResourceManager to dynamically generate a combined constraint that satisfies both operational requirements in conjunction with the constraints of the dependent job and the dependent job when rescheduling the dependent job.
An IDC virtualization scheduling energy-saving system method based on a cloud computing environment comprises the following steps:
s1, when the Executor starts the operation, the dynamic information of the operation such as exclusive monopoly of the node rack, the IP address, the port and the disk of the operation and the like can be written into the ZooKeeper name service in real time;
s2, when the job resources are matched, the resource manager queries the job dynamic information from the zooKeeper name service according to the job name, and replaces the job dynamic constraint attribute with the job dynamic information to generate a job dynamic constraint condition;
s3, performing job resource matching by the resource manager according to the job dynamic constraint conditions, and returning the node list information to the JobSchedule;
s4, JobSchedule issues the job to the execution device for execution.
The invention has the technical effects and advantages that:
according to the scheme, system resources need to be reasonably scheduled, the higher utilization rate of the system is guaranteed, the different customization requirements of the operation need to be fully met, a conversion mechanism of operation soft and hard constraints is established, batch processing operations of different types and the internet resident service application are effectively managed in a unified mode, the scale of the application sharing cluster is greatly increased, the application performance bottlenecks of different types and the characteristic of complementation of high and low peak time periods are fully utilized, the resource utilization rate of the application cluster is remarkably improved, and the total ownership cost of the data center is reduced.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
An IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment comprises a center manager module, an executive module, a Zoo-Keeper module and a ClientAPI (application programming interface) module, wherein the center manager module is divided into two sub-modules, namely a resource manager module and a JobScheduler module, and a user submits, queries and controls jobs to the center manager module through a command line, WebPortal and ClientAPI and queries the resource state of an application cluster or a single node; the JobScheduler is responsible for application grouping management and job scheduling, selects application groups with high priority and waiting jobs to schedule jobs according to the resource quotas and the used resource quantity of the application groups, schedules jobs according to a job scheduling algorithm selected by the application groups, and sends the selected job information (JobClassId) to the Resourcemanager to perform resource matching.
Further, the resource manager is responsible for state monitoring, management and job resource matching of all nodes of the application cluster, and the executive is responsible for starting, executing and state monitoring of jobs and periodically reports node resource state information to the Res0 resource manager.
Further, the zo-Keeper is a configuration center, which stores all modules and application cluster nodes, and meanwhile, Zoo-Keeper is also configuration information of a core point of a high availability mechanism; but also acts as a name service.
Further, when the center manager goes down or the service is not available, the backup center manager selects a new master center manager through ZooKeeper.
Further, the JobScheduler writes all job running state information to the ZooKeeper in real time, and the user and the application can obtain the job information such as the running state, the node distribution, the network port and the like of the job through the job name.
Further, the centrmanager adopts distributed files, such as Google File System (GFS), Hadoop Distributed File System (HDFS), Network File System (NFS), and the like, to solve the problems of job submission and deployment, job data and log information storage, and data sharing between applications, in order to improve application performance, the system adopts resource containers (ResourceContainer), such as linux conetainer, and the like, instead of traditional virtual machines, to achieve performance and security isolation of applications, and when the system has high performance, the resource containers do not have operating systems that operate independently, but share operating system kernels and operating environments with host machines, which requires that the system must provide additional support mechanisms, such as unified operating system version and operating environments such as Glibc, Gcc, Java, and the like, to avoid deploying internet applications of the same port at the same node, and the like.
Further, the CenterManager also includes a dynamic port: for jobs which do not require a fixed port, such as a business logic layer, a MySQL database and the like, a user sets a job attribute dynamic. When the resources are matched, the resource manager dynamically allocates an available port number for the node according to the occupation condition of the node port; when the Executor starts the operation, the EI number of the terminal is written into the ZooKeeper name service, so that the inquiry of other operations is facilitated.
Further, the CenterManager also includes a dependent job module: the characteristic of large load fluctuation of service type jobs enables the deployment situation of jobs in the data center nodes to be dynamically changed; jobs can only be submitted by specifying the names of the jobs they depend on; when the job resources are matched, the resource manager queries a node list operated by the dependent job from the name service according to the job name to generate a job dependent constraint condition.
Further, the CenterManager further comprises a node failure handling module: similar to programming frames such as MapReduce and Hadoop, a set of processing mechanisms for dealing with automatic node failure needs to be formulated by a scheduling strategy; however, the automatic failure processing of the dependent jobs requires that the dependency relationship between jobs is maintained, that is, after the dependent jobs are rescheduled, issued and executed, the dependent jobs are automatically issued to the same node for running; this makes it necessary for the ResourceManager to dynamically generate a combined constraint that satisfies both operational requirements in conjunction with the constraints of the dependent job and the dependent job when rescheduling the dependent job.
The invention provides a method for IDC (internet data center) virtualization scheduling of an energy-saving system based on a cloud computing environment, which comprises the following steps of:
s1, when the Executor starts the operation, the dynamic information of the operation such as exclusive monopoly of the node rack, the IP address, the port and the disk of the operation and the like can be written into the ZooKeeper name service in real time;
s2, when the job resources are matched, the resource manager queries the job dynamic information from the zooKeeper name service according to the job name, and replaces the job dynamic constraint attribute with the job dynamic information to generate a job dynamic constraint condition;
s3, performing job resource matching by the resource manager according to the job dynamic constraint conditions, and returning the node list information to the JobSchedule;
s4, JobSchedule issues the job to the execution device for execution.
Therefore, according to the scheme, system resources need to be reasonably scheduled, the higher utilization rate of the system is guaranteed, the different customization requirements of the operation need to be fully met, a conversion mechanism of operation soft and hard constraints is established, batch processing operations of different types and the internet resident service application are effectively managed in a unified mode, the scale of the application sharing cluster is greatly increased, the characteristics of application performance bottlenecks of different types and complementation of high and low peak time periods are fully utilized, the resource utilization rate of the application cluster is remarkably improved, and the total ownership cost of the data center is reduced.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (2)
1. An IDC (Internet data center) virtualization scheduling energy-saving system based on a cloud computing environment comprises a center manager module, an executive module, a Zoo-Keeper module and a ClientAPI (application programming interface) module, wherein the center manager module is divided into two sub-modules, namely a resource manager module and a JobScheduler module, and a user submits, queries and controls jobs to the center manager module through a command line, WebPortal and ClientAPI and queries the resource state of an application cluster or a single node; the JobScheduler is responsible for application grouping management and job scheduling, selects application groups with high priority and waiting jobs to perform job scheduling according to the resource quota of the application groups and the number of used resources, performs job scheduling according to a job scheduling algorithm selected by the application groups, and sends the selected job information to a resource manager to perform resource matching; the resource manager is responsible for state monitoring, management and job resource matching of all nodes of the application cluster, and the executive is responsible for starting, executing and state monitoring of jobs and regularly reports node resource state information to the Res0 resource manager; the zo-Keeper is a configuration center, and stores all modules and application cluster nodes, and meanwhile, Zoo-Keeper is configuration information of a core point of a high availability mechanism; it also functions as a name service; when the CenterManager is down or the service is unavailable, the backup CenterManager selects a new main CenterManager through the ZooKeeper; the JobScheduler writes all job running state information into the ZooKeeper in real time, and a user and an application can acquire the running state, node distribution and network port job information of the job through a job name; the CenterManager adopts a distributed file, solves the problems of submission and deployment of jobs, storage of job data and log information and data sharing among applications, and in order to improve the application performance, the system adopts a resource container instead of a traditional virtual machine to realize the performance and safety isolation of the applications; the CenterManager also includes a dynamic port: for the operation which does not require a fixed port, setting an operation attribute dynamic.Port attribute to True by a user, and indicating that a dynamic port needs to be allocated to the operation; when the resources are matched, the resource manager dynamically allocates an available port number for the node according to the occupation condition of the node port; when the Executor starts the operation, the port number is written to the ZooKeeper name service, so that the query of other operations is facilitated;
the method is characterized in that: the CenterManager further comprises a dependent job module: the characteristic of large load fluctuation of service type jobs enables the deployment situation of jobs in the data center nodes to be dynamically changed; jobs can only be submitted by specifying the names of the jobs they depend on; when the job resources are matched, the resource manager queries a node list operated by the dependent job from the name service according to the job name to generate a job dependent constraint condition; the CenterManager also comprises a node failure processing module, and a scheduling strategy needs to make a set of processing mechanism for dealing with the automatic failure of the node; however, the automatic failure processing of the dependent jobs requires that the dependency relationship between jobs is maintained, that is, after the dependent jobs are rescheduled, issued and executed, the dependent jobs are automatically issued to the same node for running; this makes it necessary for the ResourceManager to dynamically generate a combined constraint that satisfies both operational requirements in conjunction with the constraints of the dependent job and the dependent job when rescheduling the dependent job.
2. The method for IDC virtualization scheduling of energy saving system in cloud computing environment based on claim 1, characterized by: the method comprises the following steps:
s1, when the Executor starts the operation, the dynamic information of exclusive operation of the node rack, the IP address, the port and the disk of the operation is written into the ZooKeeper name service in real time;
s2, when the job resources are matched, the resource manager queries the job dynamic information from the zooKeeper name service according to the job name, and replaces the job dynamic constraint attribute with the job dynamic information to generate a job dynamic constraint condition;
s3, performing job resource matching by the resource manager according to the job dynamic constraint conditions, and returning the node list information to the JobSchedule;
s4, JobSchedule issues the job to the execution device for execution.
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| CN107341051A (en) * | 2016-05-03 | 2017-11-10 | 北京京东尚科信息技术有限公司 | Cluster task coordination approach, system and device |
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