CN104270421A - Multi-user cloud platform task scheduling method supporting bandwidth guarantee - Google Patents
Multi-user cloud platform task scheduling method supporting bandwidth guarantee Download PDFInfo
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- CN104270421A CN104270421A CN201410466319.3A CN201410466319A CN104270421A CN 104270421 A CN104270421 A CN 104270421A CN 201410466319 A CN201410466319 A CN 201410466319A CN 104270421 A CN104270421 A CN 104270421A
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The invention relates to a multi-user cloud platform task scheduling method supporting a bandwidth guarantee, and belongs to the field of network virtualization. According to the multi-user cloud platform task scheduling method supporting the bandwidth guarantee, a user makes a request for virtual resources needed by a task according to personal service needs and sends the request to a cloud platform; after receiving the task request of the user, the cloud platform searches the number and distribution positions of virtual machines for existing tasks, and then constructs a resource allocation network according to the virtual resource request of the new task and the virtual resource allocation conditions of existing related tasks; the quantity of the virtual resources actually needed by the task is calculated and scheduled, and an optimal scheme is selected; finally, whether the virtual resources are allocated is determined. Through the multi-user cloud platform task scheduling method supporting the bandwidth guarantee, aimed at the virtual resource request of the user and according to the existing resource use conditions of the cloud platform, the task scheduling scheme occupying the least resources can be calculated and the resource utilization rate of the cloud platform is improved.
Description
Technical field
The present invention relates to a kind of many tenants Yun Pingtai method for scheduling task supporting Bandwidth guaranteed, belong to network virtualization field.
Background technology
Along with the appearance of the public data center cloud platform being representative with Amazon EC2, business is moved on cloud platform by local data center by increasing enterprise.In order to support that a large number of users shares hardware (as CPU, storage, bandwidth etc.) resource, cloud platform operation business adopts many tenants technology by abstract for physical hardware resources usually for virtual resource (as virtual machine).Each user (also known as tenant) according to own service demand, can rent the virtual resource of different scales, and according to resource use amount to operator pay.Therefore, many tenants Yun Pingtai has two large advantages: one is, can serve a large amount of tenants, improves the utilance of physical resource, thus improves operator's income; Two are, allow tenant according to the use scale of business demand flexible virtual resource flexibly, farthest avoid the wasting of resources.
But the significant challenge that many tenants platform faces is resource isolation.Existing business cloud platform can ensure to meet all kinds of calculating needed for tenant and storage resources (as CPU, internal memory and memory space etc.), but does not provide any type of network bandwidth to ensure.So numerous tenant competes limited network bandwidth resources, cause the task completion time that operates on virtual net unpredictable, have a strong impact on the end-user experience using its business.In the long run, weakening user by the enthusiasm of business migration to cloud platform, is hindered large-scale promotion and the commercialization of cloud platform by a certain extent.In view of this, design a kind of efficient cross-domain virtual network mapping method and there is huge economy and social value.For this reason, we devise the many tenants Yun Pingtai method for scheduling task supporting Bandwidth guaranteed, both can ensure the task completion time of tenant, can improve again the resource utilization of cloud platform, realize the doulbe-sides' victory of cloud platform operation business and tenant.
Summary of the invention
The present invention is based on following reasonably hypothesis: the server of cloud platform adopts tree topology to connect.When new task arrives, if many tenants Yun Pingtai can meet its resource requirement, then this task is scheduled execution; Otherwise this task is rejected.
Particular content comprises:
Step one, tenant according to own service demand, the virtual resource request of offering the challenge required, and this request is sent to cloud platform: a complete task requests is by four-tuple <N, a B
a, B
es> states, and wherein, N represents the virtual machine quantity of task needs, virtual machine is the set of virtual computing resource (as CPU) and virtual storage resource (as memory size, memory capacity), is also the base unit of virtual resource in cloud platform.B
arepresent internal virtual amount of bandwidth, namely every platform virtual machine of this task communicates with other virtual machines belonging to this task required amount of bandwidth; B
erepresent external Virtual amount of bandwidth, namely every platform virtual machine of this task communicates with the virtual machine belonging to other tasks required amount of bandwidth; S represents the set with this task with other tasks associating (namely communicating) relation.
After the task requests from tenant received by step 2, cloud platform, retrieve the position of the physical machine (as server) that the every platform virtual machine in the virtual machine quantity of existing task, this task is deposited, the existing internal virtual amount of bandwidth of task and the external Virtual amount of bandwidth of existing task.
Step 3, the cloud platform virtual resource demand according to new task and the virtual resource allocation situation of existing associated task, structure resource distribution grid.
The process of establishing of resource distribution grid is: if new task is designated as Q, and hypothesis only has existing task P and Q to associate.To the physical link l in any cloud platform,
1) calculate the existing virtual machine number of task in the left subtree of l, so, remaining virtual machine is then arranged in its right subtree, and left subtree and right subtree virtual machine sum are the node that each virtual machine structure of existing task is corresponding.
2) be the node of each virtual machine structure correspondence of new task Q.
3) add source node and destination node, the virtual machine that wherein source node is positioned at link l left subtree respectively with existing task and new task is connected, and destination node is positioned at link l right subtree respectively virtual machine with existing task and new task is connected.
4) be each task creation two internal nodes and two external nodes.The virtual machine node of each task is connected respectively to corresponding internal node and external node, and the capacity on limit is virtual machine internal communication bandwidth quantity and PERCOM peripheral communication amount of bandwidth.
5) by interconnected for the internal node of same task.Have multiple tasks of association (namely communicating) relation, its external node interconnects.So far, resource distribution grid structure is complete.
Step 4, cloud platform calculate the virtual resource quantity of this task actual needs of scheduling according to resource distribution grid, take following policy selection optimal case V: from the root node of tree topology, for some independent subtrees of its lower one deck, select in single subtree, to hold the maximum scheme of virtual machine.Iteration is carried out successively, until leaf node.
Whether step 5, detection optimal case V are empty.If it is empty, cloud platform does not have this task of enough schedule virtual resources, and this task is rejected; If not empty, cloud platform is this task matching virtual resource according to optimal case V.
Beneficial effect
The present invention for the virtual resource request of tenant task, according to the resource service condition of existing cloud platform, can calculate and take the minimum task scheduling approach of resource.Many tenants Yun Pingtai method for scheduling task that the present invention proposes both can ensure the task completion time of tenant, can improve again the resource utilization of cloud platform, was conducive to the large-scale application promoting cloud platform.
Accompanying drawing explanation
Fig. 1. flow chart;
Fig. 2. application example schematic diagram of the present invention;
Fig. 3. dual channel model schematic diagram;
Fig. 4. resource distribution grid schematic diagram;
Fig. 5. task average completion time experiment block diagram;
Fig. 6. cloud platform resource utilance experiment block diagram.
Embodiment
Below in conjunction with accompanying drawing, the present invention is specifically described:
Specific works flow process of the present invention as shown in Figure 1.
As shown in Figure 2, according to step one tenant according to own service demand, the virtual resource request of offering the challenge required, and this request is sent to cloud platform.A complete task requests is stated by a four-tuple, as shown in Figure 3;
After receiving the task requests from tenant according to step 2, cloud platform, the virtual machine quantity retrieving existing task P is N
p, distribution locations is A
p, the internal virtual amount of bandwidth of existing task is B
p a, the external Virtual amount of bandwidth of existing task is B
p e.Wherein, A
pbe length be N
pvector, the numbering of the physical machine that this virtual machine of each element representation is deposited.
According to step 3, the virtual resource demand of cloud platform according to new task and the virtual resource allocation situation of existing associated task, structure resource distribution grid, as shown in Figure 4.For the ease of statement, title new task is Q.And hypothesis only in steps in two existing task P and the Q of retrieval associate.To the physical link l in any cloud platform, the foundation of resource distribution grid comprises the following steps:
(1) the VM number of calculation task P in the left subtree of l, has been designated as p
1; So, remaining VM is then arranged in its right subtree, is designated as p
2.N
P＝p
1+p
2。For the node that each VM structure of task P is corresponding.
(2) Q is made to have q
1and q
2in the left subtree that individual VM lays respectively at link l and right subtree, N
q=q
1+ q
2.For the node that each VM structure of task Q is corresponding.
(3) add source node and destination node, the VM that wherein source node is positioned at link l left subtree respectively with task P and Q is connected, and destination node is positioned at link l right subtree respectively VM with task P and Q is connected.
(4) for each task creation two internal nodes, (subscript intra represents, as p
i intraand p
o intra) and two external nodes (subscript intro represents, as p
i interand p
o inter).The VM node of each task is connected respectively to corresponding internal node and external node, and the capacity on limit is VM intercommunication amount of bandwidth and PERCOM peripheral communication amount of bandwidth.
(5) by interconnected for the internal node of same task.Have multiple tasks of association (namely communicating) relation, its external node interconnects.So far, resource distribution grid structure is complete.
Above-mentioned construction process is equally applicable to the scene that there is multiple existing task associated with Q.
Cloud platform according to resource distribution grid, the bandwidth demand of calculation task P and Q on link l.The process of computation bandwidth demand is equivalent to the problem calculating this resource distribution grid max-flow.The scheduling scheme of certain task Q given, if the bandwidth demand of the program to every bar link is all less than or equal to the available bandwidth of this link, then claims the program to be feasible program.
According to step 4, cloud platform according to all feasible programs, choose the best alternatives V, namely determines the N that task Q requires
qthe best placement of individual virtual machine, specifically comprises the following steps:
1) the full null vector A of initialization
q, length is N
q, the physical machine numbering that each virtual machine for logger task Q is deposited.
2) for each physical link l in cloud platform associates a feasible vector FV
l, for representing the virtual machine quantity can distributed in subtree in this link.Each feasible vector has N
q+ 1, n-th (0≤n≤N
q) position is that 1 expression can distribute n VM in this subtree, is that 0 expression cannot.
3) for each physical link l associates a numerical value M
l, represent FV
lin can be the minimum virtual machine number of this task matching.
4) travel through the link be connected with root node, the subtree belonging to every bar link distributes M
lindividual virtual machine, and from N
qin deduct M
l.If after having traveled through, N
qnon-vanishing, then residue virtual machine is assigned in the maximum subtree of free virtual machine.
5) each link is successively traveled through, until leaf node (leaf node is physical machine).The leaf node numbering distributing to task Q is write vectorial A successively
qin.
According to step 5, detect vectorial A
qwhether there is neutral element.If exist, then illustrate that optimal scheduling plan V is empty, cloud platform does not have enough resources to support task Q, and task requests is rejected: if do not exist, then illustrate that optimal scheduling plan V exists, and according to A
qfor task Q distributes virtual resource.
According to above-mentioned execution mode, the performance of this method for scheduling task (referred to as binary channels optimization method) is emulated, and can compare by selection scheduling method with other two kinds.Two kinds of methods for contrasting briefly introduce as follows:
1) single channel method: cloud platform uses existing one channel model to describe the resource requirement of tenant, selects first feasible task scheduling approach as optimal case.
2) dual channel approaches: cloud platform uses dual channel model to describe the resource requirement of tenant, selects first feasible task scheduling approach as optimal case.
Reach speed by control task, different cloud platforms can be realized and expect load.
Fig. 5 is under load expected by different cloud platforms, the task average completion time simulation result using different method for scheduling task to obtain.Can find out, the binary channels optimization method that the present invention proposes effectively can shorten the average completion time of task, and the amplitude of shortening is expected the increase of load with cloud platform and expands.
Fig. 6 is under load expected by different cloud platforms, the cloud platform resource utilance simulation result using different method for scheduling task to obtain.Can find, the binary channels optimization method that the present invention proposes can improve the resource utilization of cloud platform.
Result can be found out by experiment, and many tenants Yun Pingtai method for scheduling task that the present invention proposes both can ensure the task completion time of tenant, can improve again the resource utilization of cloud platform.
Claims (2)
1. support many tenants Yun Pingtai method for scheduling task of Bandwidth guaranteed, it is characterized in that:
Step one, tenant are according to own service demand, the virtual resource request of offering the challenge required, and this request is sent to cloud platform: a complete task requests is by a four-tuple <N, Ba, Be, S> state, wherein, N represents the virtual machine quantity of task needs, and Ba represents internal virtual amount of bandwidth; B
erepresent external Virtual amount of bandwidth; S represents the set of other tasks with this task with incidence relation;
After the task requests from tenant received by step 2, cloud platform, retrieve the position of the physical machine that the every platform virtual machine in the virtual machine quantity of existing task, this task is deposited, the existing internal virtual amount of bandwidth of task and the external Virtual amount of bandwidth of existing task;
Step 3, the cloud platform virtual resource demand according to new task and the virtual resource allocation situation of existing associated task, structure resource distribution grid;
Step 4, cloud platform calculate the virtual resource quantity of this task actual needs of scheduling according to resource distribution grid, take following policy selection optimal case V: from the root node of tree topology, for some independent subtrees of its lower one deck, select in single subtree, to hold the maximum scheme of virtual machine.Iteration is carried out successively, until leaf node;
Whether step 5, detection optimal case V are empty, and if it is empty, cloud platform does not have this task of enough schedule virtual resources, and this task is rejected; If not empty, cloud platform is this task matching virtual resource according to optimal case V.
2. a kind of many tenants Yun Pingtai method for scheduling task supporting Bandwidth guaranteed as claimed in claim 1, the process of establishing of resource distribution grid is:
New task is designated as Q, to the physical link l in any cloud platform,
1) calculate the existing virtual machine number of task in the left subtree of l, so, remaining virtual machine is then arranged in its right subtree, and left subtree and right subtree virtual machine sum are the node that each virtual machine structure of existing task is corresponding;
2) be the node of each virtual machine structure correspondence of new task Q;
3) add source node and destination node, the virtual machine that wherein source node is positioned at link l left subtree respectively with existing task and new task is connected, and destination node is positioned at link l right subtree respectively virtual machine with existing task and new task is connected;
4) be each task creation two internal nodes and two external nodes, the virtual machine node of each task is connected respectively to corresponding internal node and external node, and the capacity on limit is virtual machine internal communication bandwidth quantity and PERCOM peripheral communication amount of bandwidth;
5) by interconnected for the internal node of same task, have multiple tasks of incidence relation, its external node interconnects.
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Cited By (19)
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| CN105828411B (en) * | 2015-01-28 | 2019-05-10 | 国际商业机器公司 | Ensure the method and system of the bandwidth quality in the WI-FI connection of Internet access points |
| CN105828411A (en) * | 2015-01-28 | 2016-08-03 | 国际商业机器公司 | Ensuring Quality of Bandwidth in a Wi-Fi Connection to an Internet Access Point |
| CN105159780A (en) * | 2015-08-21 | 2015-12-16 | 北京理工大学 | Multi-layer cloud application oriented high-availability virtual network mapping method and apparatus |
| CN105159780B (en) * | 2015-08-21 | 2018-07-20 | 北京理工大学 | The high availability virtual network mapping method and device of oriented multilayer time cloud application |
| CN105224392A (en) * | 2015-10-13 | 2016-01-06 | 中国联合网络通信集团有限公司 | A kind of virtual computing resource quota management method and platform |
| CN105224392B (en) * | 2015-10-13 | 2018-07-27 | 中国联合网络通信集团有限公司 | A kind of virtual computing resource quota management method and platform |
| CN105915470A (en) * | 2016-01-27 | 2016-08-31 | 无锡华云数据技术服务有限公司 | Flexible bandwidth configuration method based on Linux flow control |
| CN105577834A (en) * | 2016-02-06 | 2016-05-11 | 清华大学 | Cloud data center two-level bandwidth allocation method and system with predictable performance |
| CN105577834B (en) * | 2016-02-06 | 2018-10-16 | 清华大学 | Two layers of bandwidth allocation methods of cloud data center with Predicable performance and system |
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| CN105871751A (en) * | 2016-03-25 | 2016-08-17 | 中国科学院计算技术研究所 | Method and system for guaranteeing network bandwidth of data center |
| CN107276801A (en) * | 2017-06-14 | 2017-10-20 | 中国石油化工股份有限公司 | A kind of collocation method of the cloud computing platform based on application service |
| CN109327422A (en) * | 2017-08-01 | 2019-02-12 | 中国移动通信集团浙江有限公司 | A kind of the tenant's partition method and device of multi-tenant |
| CN109327422B (en) * | 2017-08-01 | 2021-04-02 | 中国移动通信集团浙江有限公司 | Multi-tenant isolation method and device |
| CN108665157A (en) * | 2018-05-02 | 2018-10-16 | 中山大学 | A method of realizing cloud Workflow system flow instance balance dispatching |
| CN108665157B (en) * | 2018-05-02 | 2021-08-20 | 中山大学 | Method for realizing balanced scheduling of cloud workflow system process instance |
| CN111199033A (en) * | 2020-01-09 | 2020-05-26 | 山东浪潮通软信息科技有限公司 | Method and tool for processing identity card and certificate information by using cloud technology |
| CN112073501A (en) * | 2020-09-02 | 2020-12-11 | 浪潮云信息技术股份公司 | Tenant separation type storage and management method |
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