CN102722413A - Distributed resource scheduling method for desktop cloud cluster - Google Patents
Distributed resource scheduling method for desktop cloud cluster Download PDFInfo
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Abstract
The invention aims to disclose a distributed resource scheduling method for a desktop cloud cluster. Compared with the prior art, through a resource quantification model and a uniform resource view of the cluster at any operating moment, the method comprises the following steps of: (A), performing active resource monitoring to prevent divulgence of resources during operating; (B), accurately predicting the resource consumption condition of a single physical server in the cluster and even the whole cluster based on resource snapshot, sending resource injection prediction in advance, and timely injecting the resources by dynamically expanding the cluster to realize non-stop lasting operation of the cluster; and (C), determining the distribution condition of the resources in the whole cluster according to the resource snapshot of the cluster, and thus implementing different resource scheduling strategies. Therefore, various defects caused by deficiency of resource scheduling means in cluster operation scheduling are overcome, dynamic balance and lasting operation of the resources in the cluster operating process are kept, and aims of the invention are fulfilled.
Description
Technical field
The present invention relates to a kind of resource regulating method, the distributed resource scheduling method that the desktop cloud cluster of particularly a kind of operation that is used for large-scale distributed virtual cluster and scheduling of resource (being the IaaS field of cloud computing) uses.
Background technology
Any one is ripe, do not have resource reveal and IaaS system that can the lasting stability operation all needs resource that a cover can guarantee cluster accurately, effective scheduled resources dispatching method; The scheduling result of this resource regulating method has directly determined whole resource utilization, resource service precision (having No Assets to reveal), the task throughput of whole cluster and has carried out efficient, has been the core of distributed type assemblies running scheduling system.
The empty levelization cluster operation system in cloud computing field exists serious deficiency and defective (as shown in Figure 1) aspect dynamic resource scheduling that determines cluster and the Task Distribution mostly in the market, mainly shows the following aspects:
(1) lack the quantitative model of resource, can't be with the quantification of targets of resource management and control, thereby can't carry out accurate task scheduling according to the resource load data of cluster;
(2) can't the resource load situation of XM in the cluster be quantized and include in Decision of Allocation during task scheduling; The physical server that mostly just is a state of random choose " health " simply is as current virtual machine operations task executions node, but when actual task is carried out, tends to again carry out failure because of reasons such as " inadequate resources " causes task;
(3) can't predict " resource load " and " resource critical point " of each physical server in the cluster and even whole cluster in advance; Also just can't make in advance to the physical server that is in the resource critical conditions and increase internal memory, hard disk even dynamically increase initiatively dilatation measure such as physical server for cluster; Can only be helplessly when " resource critical point " arrives because " inadequate resource " waits mistake and " shutdown " dilatation passively, thereby can't guarantee that whole group system moves enduringly.
Common cloud computing IaaS system mostly rests on the thick mad state that moves with open loop because the existence of above problem causes at present; Instantaneous resource view of conforming cluster or snapshot can't be provided; Thereby can't implement any effective perspective resources and closed-loop control; Can't effectively prevent the resource dynamic filling that resource is revealed and execution is necessary; Frequently remedy after reaching the standard grade, can't lasting good commercial service be provided, do not reach the commercial off-the-shelf grade for the client because of problem integral body machines of delaying such as " inadequate resource ", " resource leakages ".
Therefore, the distributed resource scheduling method that needs a kind of desktop cloud cluster to use especially is in order to solve the problem of above-mentioned existing existence.
Summary of the invention
The distributed resource scheduling method that the object of the present invention is to provide a kind of desktop cloud cluster to use; Defective to above-mentioned existing technology existence; In the cluster running scheduling, lack all drawbacks that the scheduling of resource means are brought before solving at one stroke, keep resource dynamic balance and lasting operation in the cluster operational process.
The technical matters that the present invention solved can adopt following technical scheme to realize:
The distributed resource scheduling method that a kind of desktop cloud cluster uses is characterized in that it comprises the steps:
(1) cluster starts, and sequence starting constitutes the physical host of cluster;
(2) configuration parameter with each physical host carries out the resource conversion, obtains resource datas such as the maximum assignable virtual processor of every physical host, virtual memory and virtual local storage;
(3) physical host is carried out resource registering; The physical host resource datas such as maximum assignable virtual processor, virtual memory and virtual local storage obtain that convert are registered in the resource loadmeter at cluster resource center, as the basic data of scheduling of resource next;
(4) operation system is accepted client requests and is sent the virtual machine task of creating to the cluster resource scheduling engine when operation;
(5) the resource loadmeter of scheduling of resource engine through the cluster resource center obtained the instantaneous resource view of cluster to cluster resource center requests resource snapshot, as the foundation of scheduling of resource next;
(6) the scheduling of resource engine current resource dispatching strategy of cluster of packing into, and carry out scheduling of resource according to the instantaneous resource view of the cluster that obtains calculates in the physical host of whole clusters and possesses the goal task XM that task is carried out qualification most;
(7) the scheduling of resource engine freezes the goal task XM execution resource that obtains;
(8) selected goal task XM begins to carry out the virtual machine task of creating;
(9) judgement goal task XM is created the final execution result of virtual machine task, if task runs succeeded, then gets into step (10); If task is carried out failure, then execution in step (11);
When (10) task runs succeeded, submit the resource freeze to, with the resource data that finally consumes synchronously to the resource loadmeter at cluster resource center, the resource data of more preserving in the new resources loadmeter;
When (11) task was carried out failure, the resource of freezing before the rollback made the resource of being freezed by task in time obtain discharging.
In one embodiment of the invention, also comprise step (12), the supervisor console of cluster is checked with the mode of regular visit and the resource loadmeter at cluster resource center regularly whole cluster is carried out resources.
Further, the foundation of said resources is the distance of the timely resource data of cluster and following each physical host node thereof apart from the resource critical point.
In one embodiment of the invention, also comprise step (13), supervisor console is carried out the critical inspection of resource, if near the resource critical point, then sends dynamic resource and injects instruction; If not near the resource critical point, then do not handle as yet.
Further, dynamic resource that supervisor console sends injects instruction will be looked field condition by cluster and handle flexibly, and the physical host that promptly approaches the resource critical point to resource value is carried additionally physical resource or dynamically added new physical resource to cluster.
Further again, said physical resource comprises concurrent physical processor, physical memory and physical storage device.
Further again, for the new physical resource that adds, all need add the next round scheduling of resource through the supplemental resources registration to the registration of cluster resource center.
Stochastic distribution strategy when in one embodiment of the invention, said resource dispatching strategy comprises heavy traffic and the resource clustering strategy of business during the free time.
In one embodiment of the invention, said frozen stock number depends on task parameters, and how many tasks needs just what freeze, and the resource that this part is freezed will can not taken over for use by other tasks, possesses sufficient resources when carrying out to guarantee task.
In one embodiment of the invention, in the step (11), the resource data of release is also with synchronous resource loadmeter to the cluster resource center, the resource data of more preserving in the new resources loadmeter.
The distributed resource scheduling method that desktop cloud cluster of the present invention uses, compared with prior art, at the consistance resource view of the time of running arbitrarily, carry out: A. is monitoring resource initiatively through resource quantitative model and cluster, reveals when preventing the operation of resource; B. accurately predict the resource consumption situation of separate unit physical server in the cluster even whole cluster based on the resource snapshot, send resource in advance and inject advance notice, and in time inject resource that realizes cluster does not shut down lasting operation through the cluster dynamic capacity-expanding; C. according to the distribution situation of the clear and definite resource of resource snapshot in whole cluster of cluster; Carry out the different resources scheduling strategy in view of the above; In the cluster running scheduling, lack all drawbacks that the scheduling of resource means are brought before solving at one stroke; Keep resource dynamic balance and lasting operation in the cluster operational process, realize the object of the invention.
Characteristics of the present invention can consult this case graphic and below better embodiment detailed description and obtain to be well understood to.
Description of drawings
Fig. 1 is the schematic flow sheet of the resource regulating method of the common IaaS cluster operation system in existing cloud computing field;
Fig. 2 is the schematic flow sheet of the distributed resource scheduling method of desktop cloud cluster use of the present invention.
Embodiment
For technological means, creation characteristic that the present invention is realized, reach purpose and effect and be easy to understand and understand, below in conjunction with concrete diagram, further set forth the present invention.
Distributed resource scheduling method as shown in Figure 2, that desktop cloud cluster of the present invention uses, it comprises the steps:
(1) cluster starts, and sequence starting constitutes the physical host of cluster;
(2) configuration parameter with each physical host carries out the resource conversion, obtains resource datas such as the maximum assignable virtual processor of every physical host, virtual memory and virtual local storage;
(3) physical host is carried out resource registering; The physical host resource datas such as maximum assignable virtual processor, virtual memory and virtual local storage obtain that convert are registered in the resource loadmeter at cluster resource center, as the basic data of scheduling of resource next;
(4) operation system is accepted client requests and is sent the virtual machine task of creating to the cluster resource scheduling engine when operation;
(5) the resource loadmeter of scheduling of resource engine through the cluster resource center obtained the instantaneous resource view of cluster to cluster resource center requests resource snapshot, as the foundation of scheduling of resource next;
(6) the scheduling of resource engine current resource dispatching strategy of cluster of packing into, and carry out scheduling of resource according to the instantaneous resource view of the cluster that obtains calculates in the physical host of whole clusters and possesses the goal task XM that task is carried out qualification most;
(7) the scheduling of resource engine freezes the goal task XM execution resource that obtains;
(8) selected goal task XM begins to carry out the virtual machine task of creating;
(9) judgement goal task XM is created the final execution result of virtual machine task, if task runs succeeded, then gets into step (10); If task is carried out failure, then execution in step (11);
When (10) task runs succeeded, submit the resource freeze to, with the resource data that finally consumes synchronously to the resource loadmeter at cluster resource center, the resource data of more preserving in the new resources loadmeter;
When (11) task was carried out failure, the resource of freezing before the rollback made the resource of being freezed by task in time obtain discharging.
In the present invention, also comprise step (12), the supervisor console of cluster is checked with the mode of regular visit and the resource loadmeter at cluster resource center regularly whole cluster is carried out resources.
The foundation of said resources is the distance of the timely resource data of cluster and following each physical host node thereof apart from the resource critical point.
In the present invention, also comprise step (13), supervisor console is carried out the critical inspection of resource, if near the resource critical point, then sends dynamic resource and injects instruction; If not near the resource critical point, then do not handle as yet.
Dynamic resource that supervisor console sends injects instruction will be looked field condition by cluster and handle flexibly, and the physical host that promptly approaches the resource critical point to resource value is carried additionally physical resource or dynamically added new physical resource to cluster; To reach the purpose of alleviating the cluster resource shortage, help cluster under non-stop-machine prerequisite, to spend the peak traffic phase based on resources and necessary resource dynamic filling means.
Said physical resource comprises concurrent physical processor, physical memory and physical storage device.
For the new physical resource that adds, all need add the next round scheduling of resource through the supplemental resources registration to the registration of cluster resource center.
Stochastic distribution strategy when in the present invention, said resource dispatching strategy comprises heavy traffic and the resource clustering strategy of business during the free time
The stochastic distribution strategy is that task is distributed in as far as possible extensively and possesses the physical host that resource is executed the task, and so that high as far as possible cluster handling capacity to be provided, this is very effective at busy traffic period.
It is that task drops on same the physical host and carries out in the concentrated area as far as possible that resource converges strategy, and the free time goes out remaining physical host and lets it get into dormancy as much as possible, thereby reduces the whole energy consumption of cluster as far as possible.
In the present invention, said frozen stock number depends on task parameters, and how many tasks needs just what freeze, and the resource that this part is freezed will can not taken over for use by other tasks, possesses sufficient resources when carrying out to guarantee task.
In the present invention, in the step (11), the resource data of release is also with synchronous resource loadmeter to the cluster resource center, the resource data of more preserving in the new resources loadmeter.
More than show and described ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; The present invention is not restricted to the described embodiments; That describes in the foregoing description and the instructions just explains principle of the present invention, and under the prerequisite that does not break away from spirit and scope of the invention, the present invention also has various changes and modifications; These variations and improvement all fall in the scope of the invention that requires protection, and the present invention requires protection domain to be defined by appending claims and equivalent thereof.
Claims (10)
1. the distributed resource scheduling method that the desktop cloud cluster uses is characterized in that it comprises the steps:
(1) cluster starts, and sequence starting constitutes the physical host of cluster;
(2) configuration parameter with each physical host carries out the resource conversion, obtains resource datas such as the maximum assignable virtual processor of every physical host, virtual memory and virtual local storage;
(3) physical host is carried out resource registering; The physical host resource datas such as maximum assignable virtual processor, virtual memory and virtual local storage obtain that convert are registered in the resource loadmeter at cluster resource center, as the basic data of scheduling of resource next;
(4) operation system is accepted client requests and is sent the virtual machine task of creating to the cluster resource scheduling engine when operation;
(5) the resource loadmeter of scheduling of resource engine through the cluster resource center obtained the instantaneous resource view of cluster to cluster resource center requests resource snapshot, as the foundation of scheduling of resource next;
(6) the scheduling of resource engine current resource dispatching strategy of cluster of packing into, and carry out scheduling of resource according to the instantaneous resource view of the cluster that obtains calculates in the physical host of whole clusters and possesses the goal task XM that task is carried out qualification most;
(7) the scheduling of resource engine freezes the goal task XM execution resource that obtains;
(8) selected goal task XM begins to carry out the virtual machine task of creating;
(9) judgement goal task XM is created the final execution result of virtual machine task, if task runs succeeded, then gets into step (10); If task is carried out failure, then execution in step (11);
When (10) task runs succeeded, submit the resource freeze to, with the resource data that finally consumes synchronously to the resource loadmeter at cluster resource center, the resource data of more preserving in the new resources loadmeter;
When (11) task was carried out failure, the resource of freezing before the rollback made the resource of being freezed by task in time obtain discharging.
2. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 1 uses; It is characterized in that; Also comprise step (12), the supervisor console of cluster is checked with the mode of regular visit and the resource loadmeter at cluster resource center regularly whole cluster is carried out resources.
3. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 2 uses is characterized in that, the foundation of said resources is the distance of the timely resource data of cluster and following each physical host node thereof apart from the resource critical point.
4. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 1 uses is characterized in that, also comprises step (13), and supervisor console is carried out the critical inspection of resource, if near the resource critical point, then sends dynamic resource and injects instruction; If not near the resource critical point, then do not handle as yet.
5. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 4 uses; It is characterized in that; Dynamic resource that supervisor console sends injects instruction will be looked field condition by cluster and handle flexibly, and the physical host that promptly approaches the resource critical point to resource value is carried additionally physical resource or dynamically added new physical resource to cluster.
6. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 5 uses is characterized in that said physical resource comprises concurrent physical processor, physical memory and physical storage device.
7. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 5 uses is characterized in that, for the new physical resource that adds, all need add the next round scheduling of resource through the supplemental resources registration to the registration of cluster resource center.
8. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 1 uses is characterized in that, stochastic distribution strategy when said resource dispatching strategy comprises heavy traffic and the resource clustering strategy of business during the free time.
9. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 1 uses; It is characterized in that; Said frozen stock number depends on task parameters; How many tasks needs just what freeze, and the resource that this part is freezed will can not taken over for use by other tasks, possesses sufficient resources when carrying out to guarantee task.
10. the distributed resource scheduling method that desktop cloud cluster as claimed in claim 1 uses; It is characterized in that; In the step (11), the resource data of release is also with synchronous resource loadmeter to the cluster resource center, the resource data of more preserving in the new resources loadmeter.
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