CN105471985A - Load balance method, cloud platform computing method and cloud platform - Google Patents
Load balance method, cloud platform computing method and cloud platform Download PDFInfo
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- CN105471985A CN105471985A CN201510815416.3A CN201510815416A CN105471985A CN 105471985 A CN105471985 A CN 105471985A CN 201510815416 A CN201510815416 A CN 201510815416A CN 105471985 A CN105471985 A CN 105471985A
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Abstract
The invention relates to a load balance method, a cloud platform computing method and a cloud platform. The load balance method includes the following steps: obtaining resources and the latest finish time for completion of each fine grain task, and ordering all the fine grain tasks according to the latest finish time so that a sequence S is obtained; obtaining the real-time load degree of each node, and conducting ordering according to the real-time load degree of each node so that a sequence S1 is obtained; and selecting nodes from the sequence S1 and distributing the nodes to each fine grain task in the sequence S, wherein the distributed nodes need to be minimum in real-time load degree and meet requirements of resources for completion of the fine grain tasks. The cloud platform computing method and the cloud platform provided by the invention are achieved based on the above load balance method. In this way, loads of nodes can be basically balanced, the utilization rate of resources is increased, the execution time span of tasks is reduced, and an aim of basic load balance is achieved.
Description
Technical field
The present invention relates to field of cloud computer technology, particularly relate to a kind of load-balancing method and cloud platform computational methods, cloud platform.
Background technology
Cloud computing be continue the 1980's mainframe computer to client-server big change after another great change, be Distributed Calculation (DistributedComputing), parallel computation (ParallelComputing), effectiveness calculate the product that (UtilityComputing), the network storage (NetworkStorageTechnologies), virtual (Virtualization), load balancing (LoadBalance), the traditional computer such as hot-standby redundancy (HighAvailable) and network technical development merge.Cloud computing is by making Computation distribution on a large amount of distributed computers, but not in local computer or remote server, and this makes enterprise can by resource switch in the application needed, access computer and storage system according to demand.Cloud computing platform provides access to netwoks available, easily, as required to user.User enters configurable computing resource sharing pond (resource comprises network, server, storage, application software, service), can when drop into little management work and seldom mutual with service provision end, the above-mentioned resource of quick obtaining.
Cloud computing platform needs, in the face of a large amount of users, to need to provide different services according to the demand of these users.Cloud computing platform, in the process of task scheduling and Resourse Distribute, if choose the node of inefficient node or overload, then can reduce the execution performance of cloud computing platform greatly.Therefore how for different user resource allocations and the equilibrium assignment that realizes resource are the required problems solved of this cloud computing platform.Current solution cloud computing platform load-balancing method mainly comprises static equilibrium strategy and dynamic equalization strategy two kinds of modes.Static equilibrium strategy utilizes mathematical function dispatching algorithm to select node to realize distributing, executing the task.But dynamically can not adjust cloud computing platform interior joint information, thus make the utilance of part of nodes lower.Dynamic Load-Balancing Strategy is each peer distribution task according to platform current state or nearest Determines.If there is node tasks overload, then the task transfers that will overload gives other node processing, thus reaches the object of dynamic equalization.But the transfer of overload task can bring extra burden to platform.
Summary of the invention
One of them object of the present invention is to provide a kind of load-balancing method and cloud platform computational methods, cloud platform, to bring the technical problem of added burden to solve the lower or part of nodes of part of nodes utilance in prior art overload task transfers of carrying out overloading to platform.
For achieving the above object, first aspect, embodiments provides a kind of load-balancing method, comprising:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
Alternatively, described peer distribution of choosing in described sequence S1 is to each finegrained tasks in described sequence S, and the node distributed needs real time load degree minimum, and meets after each finegrained tasks completes the step of the demand of resource requirement, comprising:
Upgrade the real time load degree of each node.
Alternatively, following formula is adopted to obtain real time load degree in the step of the real time load degree of each node of described acquisition:
In formula,
for real time load degree; δ
ifor weight coefficient, and 0≤δ
i≤ 1; E
ibe i-th calculating factor; N is calculating factor sum.
Second aspect, the embodiment of the present invention additionally provides a kind of cloud platform computational methods based on load balancing, comprising:
When receiving the service request of client, cloud platform obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to described parallel processing cluster, to obtain multiple logic task;
Each logic task is resolved into multiple finegrained tasks;
Load-balancing method is utilized to be each finegrained tasks Resources allocation;
Analysis result is back to client according to the performance of each finegrained tasks by cloud platform.
Alternatively, described load-balancing method comprises:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
Alternatively, described peer distribution of choosing in described sequence S1 is to each finegrained tasks in described sequence S, and the node distributed needs real time load degree minimum, and meets after each finegrained tasks completes the step of the demand of resource requirement, comprising:
Upgrade the real time load degree of each node.
Alternatively, when receiving reporting information, this cloud platform carries out metadata description to described reporting information, to obtain the data of consolidation form;
The data of this consolidation form are stored.
Alternatively, expandable mark language XML is adopted to carry out metadata description to described reporting information.
The third aspect, the embodiment of the present invention further provides a kind of cloud platform, realizes, comprising based on cloud platform computational methods mentioned above:
Data memory module, is connected with logic processing module, for the storage and management of reporting information;
Request of data analysis module, is connected with load balancing module with client, logic processing module respectively, for performing following steps: when receiving the service request of client, obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to described parallel processing cluster, to obtain multiple logic task; And,
Data results is returned client;
Logic processing module, is connected with request of data analysis module, data memory module and load balancing module, for storing data according to multiple logic task visit data memory module to obtain, and will store data return data analysis request of data analysis module;
Load balancing module, for performing following steps:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
Alternatively, the cloud platform that the embodiment of the present invention provides also comprises: data report analysis module, is connected with client and data storing platform, for when receiving reporting information, carries out metadata description to described reporting information, to obtain the data of consolidation form; And the data of this consolidation form are stored.
The present invention is decomposed and time-sequencing by finegrained tasks, by the successively Resourse Distribute to finegrained tasks, achieves the balance to each node load situation of cloud platform.And the time-sequencing of finegrained tasks, the task of ensure that can complete in official hour.The present invention can ensure that the load basis equalization of nodes, improve the utilance of resource, save the time of implementation span of task, thus realized executing the task safely and efficiently under cloud computing environment, the target of load basis equalization in system can have been realized again.
Accompanying drawing explanation
Can understanding the features and advantages of the present invention clearly by reference to accompanying drawing, accompanying drawing is schematic and should not be construed as and carry out any restriction to the present invention, in the accompanying drawings:
Fig. 1 is a kind of load-balancing method block diagram that the embodiment of the present invention provides;
Fig. 2 is a kind of cloud platform computational methods schematic flow sheet based on load balancing that the embodiment of the present invention provides;
Fig. 3 is a kind of cloud platform block diagram that the embodiment of the present invention provides.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples for illustration of the present invention, but are not used for limiting the scope of the invention.
First aspect, embodiments provides a kind of load-balancing method, as shown in Figure 1, comprising:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In sequence S1, choose peer distribution to each finegrained tasks in sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
In the embodiment of the present invention, suppose there be m finegrained tasks: s
1, s
2... s
m, calculate each finegrained tasks and complete resource requirement and Late Finish, be expressed as: s
1(r
1, t
1), s
2(r
2, t
2) ... s
m(r
m, t
m), and calculate the resource summation R of all finegrained tasks needs.
In practical application, node N
j(j is positive integer) checks self actual loading degree at regular intervals
actual loading degree
following formula (1) is adopted to calculate:
In formula (1),
for real time load degree; δ
ifor weight coefficient, and 0≤δ
i≤ 1; E
ibe i-th calculating factor; N is calculating factor sum.
Such as, in one embodiment of the invention, the calculating factor of real time load degree comprises: cpu busy percentage E
1, internal memory performance E
2, disk performance E
3, network performance E
4with average response time E
5.Therefore real time load degree computing formula is:
For saving the querying node real time load degree time used, in the embodiment of the present invention, choosing peer distribution to each finegrained tasks in sequence S in sequence S1, the node distributed needs real time load degree minimum, and meet after each finegrained tasks completes the step of the demand of resource requirement, comprising:
Upgrade the real time load degree of each node.
For ensureing to complete each finegrained tasks at the appointed time, according to the Late Finish of each finegrained tasks, chronological order sequence is carried out to all finegrained tasks in the embodiment of the present invention, complete each finegrained tasks successively according to chronological order, thus save the time of implementation span of task.
For improving the utilance of resource and the load balancing of node, obtaining the real time load degree of each node in the embodiment of the present invention, according to real time load degree size, all nodes being sorted.In for each finegrained tasks Resources allocation, preferentially by peer distribution less for real time load degree to finegrained tasks, the resource utilization that can prevent the load imbalance of node from causing is low, thus can ensure the load balancing of each node.
Second aspect, embodiments provides a kind of cloud platform computational methods based on load balancing, as shown in Figure 2, comprising:
When receiving the service request of client, cloud platform obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to parallel processing cluster, to obtain multiple logic task;
Each logic task is resolved into multiple finegrained tasks;
Load-balancing method is utilized to be each finegrained tasks Resources allocation;
Analysis result is back to client according to the performance of each finegrained tasks by cloud platform.
Alternatively, load-balancing method adopts load-balancing method mentioned above to realize, and does not repeat them here.
Adopting the data format of different-format time mutual between different cloud platforms, causing transmitting between the platform of different structure, different data format together from there is certain obstacle during data.For solving the problem, in practical application, the present invention also carries out unitized management to received reporting information, comprising:
When receiving reporting information, this cloud platform carries out metadata description to described reporting information, to obtain the data of consolidation form;
The data of this consolidation form are stored.
In practical application, expandable mark language XML in the embodiment of the present invention, is adopted to carry out metadata description to described reporting information.Such as, when cloud storage area adopts distributed file system (HadoopDistributedFileSystem, when HDFS) building, adopt resource description and the administrative mechanism of XML, use XMLSchema file store meta data category under cloud platform and carry out metadata description.
The third aspect, for embodying the superiority of the cloud platform computational methods based on load balancing that the embodiment of the present invention provides, the embodiment of the present invention further provides a kind of cloud platform, realizes, as shown in Figure 3, comprising based on cloud platform computational methods mentioned above:
Data memory module, is connected with logic processing module, for the storage and management of reporting information;
Request of data analysis module, is connected with load balancing module with client, logic processing module respectively, for performing following steps: when receiving the service request of client, obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to parallel processing cluster, to obtain multiple logic task; And,
Data results is returned client;
Logic processing module, is connected with request of data analysis module, data memory module and load balancing module, for storing data according to multiple logic task visit data memory module to obtain, and will store data return data analysis request of data analysis module;
Load balancing module, for performing following steps:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In sequence S1, choose peer distribution to each finegrained tasks in sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
In practical application, the cloud platform that the embodiment of the present invention provides also comprises: data report analysis module, is connected with client and data storing platform, for when receiving reporting information, metadata description is carried out to described reporting information, to obtain the data of consolidation form; And the data of this consolidation form are stored.
The cloud platform provided in the embodiment of the present invention realizes based on cloud platform computational methods mentioned above, and thus can solve same technical problem, and obtain identical technique effect, this is no longer going to repeat them.
In sum, the load-balancing method that the embodiment of the present invention provides and cloud platform computational methods, cloud platform, decomposed and chronological order by finegrained tasks, by the successively Resourse Distribute to finegrained tasks, achieve the balance to each node load situation of cloud platform.And the time-sequencing of finegrained tasks, the task of ensure that can complete in official hour.Invention increases the utilance of resource, save the time of implementation span of task, and ensure that the load basis equalization of nodes, reach and can either execute the task safely and efficiently under cloud computing environment, the target of load basis equalization in system can be realized again.
In the present invention, term " multiple " refers to two or more, unless otherwise clear and definite restriction.
Although describe embodiments of the present invention by reference to the accompanying drawings, but those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, such amendment and modification all fall into by within claims limited range.
Claims (10)
1. a load-balancing method, is characterized in that, comprising:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
2. load-balancing method according to claim 1, it is characterized in that, described peer distribution of choosing in described sequence S1 is to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meet after each finegrained tasks completes the step of the demand of resource requirement, comprising:
Upgrade the real time load degree of each node.
3. load-balancing method according to claim 1, is characterized in that, adopts following formula to obtain real time load degree in the step of the real time load degree of each node of described acquisition:
In formula,
for real time load degree; δ
ifor weight coefficient, and 0≤δ
i≤ 1; E
ibe i-th calculating factor; N is calculating factor sum.
4., based on cloud platform computational methods for load balancing, it is characterized in that, comprising:
When receiving the service request of client, cloud platform obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to described parallel processing cluster, to obtain multiple logic task;
Each logic task is resolved into multiple finegrained tasks;
Load-balancing method is utilized to be each finegrained tasks Resources allocation;
Analysis result is back to client according to the performance of each finegrained tasks by cloud platform.
5. cloud platform computational methods according to claim 4, it is characterized in that, described load-balancing method comprises:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
6. cloud platform computational methods according to claim 4, it is characterized in that, described peer distribution of choosing in described sequence S1 is to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meet after each finegrained tasks completes the step of the demand of resource requirement, comprising:
Upgrade the real time load degree of each node.
7. cloud platform computational methods according to claim 4, is characterized in that, when receiving reporting information, this cloud platform carries out metadata description to described reporting information, to obtain the data of consolidation form;
The data of this consolidation form are stored.
8. cloud platform computational methods according to claim 7, is characterized in that, adopt expandable mark language XML to carry out metadata description to described reporting information.
9. a cloud platform, realizes based on the cloud platform computational methods described in claim 4 ~ 8 any one, it is characterized in that, comprising:
Data memory module, is connected with logic processing module, for the storage and management of reporting information;
Request of data analysis module, is connected with load balancing module with client, logic processing module respectively, for performing following steps: when receiving the service request of client, obtains corresponding service according to this service request;
This service decomposition is become separate multiple subtasks, and utilizes the plurality of subtask to form parallel processing cluster;
Logical analysis is carried out to described parallel processing cluster, to obtain multiple logic task; And,
Data results is returned client;
Logic processing module, is connected with request of data analysis module, data memory module and load balancing module, for storing data according to multiple logic task visit data memory module to obtain, and will store data return data analysis request of data analysis module;
Load balancing module, for performing following steps:
Obtain each finegrained tasks and complete resource requirement and Late Finish, and according to Late Finish, whole described finegrained tasks is sorted, to obtain sequence S;
Obtain the real time load degree of each node, and sort, to obtain sequence S1 according to the real time load degree size of each node;
In described sequence S1, choose peer distribution to each finegrained tasks in described sequence S, the node distributed needs real time load degree minimum, and meets the demand that each finegrained tasks completes resource requirement.
10. cloud platform according to claim 9, is characterized in that, also comprise: data report analysis module, be connected with client and data storing platform, for when receiving reporting information, metadata description is carried out to described reporting information, to obtain the data of consolidation form; And the data of this consolidation form are stored.
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