CN107404523A - Cloud platform adaptive resource dispatches system and method - Google Patents

Cloud platform adaptive resource dispatches system and method Download PDF

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Publication number
CN107404523A
CN107404523A CN201710600363.2A CN201710600363A CN107404523A CN 107404523 A CN107404523 A CN 107404523A CN 201710600363 A CN201710600363 A CN 201710600363A CN 107404523 A CN107404523 A CN 107404523A
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resource
cloud
component
virtual machine
cloud platform
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梁鸿
孔言
孙晓
魏倩
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China University of Petroleum East China
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China University of Petroleum East China
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Priority to CN201710600363.2A priority Critical patent/CN107404523A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing

Abstract

The invention discloses a kind of cloud platform adaptive resource to dispatch system, including：Component registration, for cloud cluster management, store physical resource Information；Interactive component, for receiving user task request, analyze and judge that the user asks corresponding type of virtual machine, qos parameter and quantity demand；And whether the resource request of analysis task is reasonable；Monitor component, loaded for monitor supervision platform system load and cloud cluster, including the type of virtual machine, state and resource utilization, the state and resource utilization of physical node, and the network broadband and task response-time of cluster；Decision-making component, for loading situation of change according to cluster, using adaptive resource dispatching algorithm forecast resource requirements, and send resource scheduling request；Infrastructure component, for being adjusted according to resource scheduling request to infrastructure.The present invention realizes the self-adaption deployment of cloud platform, solves the occupancy of cloud resource and wastes problem, improves resource utilization.

Description

Cloud platform adaptive resource dispatches system and method
Technical field
The present invention relates to field of cloud calculation, more particularly to a kind of cloud platform adaptive resource scheduling system and method.
Background technology
Cloud computing relies on internet, according to personal hobby and needs, can be divided into both of which, be computation schema respectively And service mode.Certainly, the method for service of cloud computing has two characteristics, is exactly dynamic and elasticity.Its infrastructure innovated Deployment mode, resource use pattern, information processing and service mode, effectively to reduce informatization threshold, knot of adjusting economy Structure, improves resource utilization and Information Service Ability provides important foundation.Especially war is had become in current information-intensive society data Slightly in the case of resource, cloud computing can obtain knowledge and decision-making letter with value that is more economical, more efficient, being easier mining data Breath, the development to human economy and society produce revolutionary impact.Country " is adhering to scientific and technical innovation with realizing that industrialization is mutually tied Under the basic principle of conjunction ", it is proposed that promote cloud computing research and the pattern of application, specify the developing direction of emphasis and primarily appoint Business.
As people are increasingly becoming " doting on for this whole IT circles to portable and compatibility requirement increase, virtual machine Youngster ", it develops and application receives much concern.Machine is divided into each smaller group part by nineteen sixty-five, IBM R＆D works member, these Component can be independent management belong to the machine resources of oneself, so far, the concept of virtual machine is born.With researcher's Repetition test, practice, virtual machine technique gradual perfection.Until today, virtual machine technique has formed its unique architecture.Root According to the difference of Virtual Machine Worker system, virtual machine can substantially have following three types：Virtual machine based on Runtime Library, based on behaviour Make the virtual machine of system and hardware based virtual machine.The core of cloud computing is resource management, it is therefore an objective to makes full use of resource, carries High resource utilization, ensure that business is stable and perform.Due to specific tasks difference, it is necessary to which the resource characteristic of scheduling is different, resource Scheduling need to consider business characteristic, scheduling virtual machine resource, work very cumbersome.Therefore, how task based access control request is adaptive dynamic State adjustment cloud resource deployment, a technical problem for needing those skilled in the art urgently to solve at present.
The content of the invention
In order to solve the above problems, the present invention provides a kind of software self-adaption deployment framework based on cloud platform, Ke Yigen When loading change according to the visit capacity of the system operated on platform, adaptive adjustment and distribution resource, cloud platform is efficiently solved Resources idle and waste problem, improve resource utilization.
To achieve these goals, the present invention adopts the following technical scheme that：
A kind of cloud platform adaptive resource dispatches system, including：
Component registration, for cloud cluster management, record and manage physical resource Information；
Interactive component, for receiving user task request, analyze and judge the corresponding type of virtual machine of user's request, Qos parameter and quantity demand；And whether the resource request of analysis task is reasonable；
Monitor component, for monitoring the service condition of each resource in cloud cluster, including the type of virtual machine, state and money Source utilization rate, the state and resource utilization of physical node, and the network broadband and task response-time of cluster；
Decision-making component, the cluster for being sent according to decision-making component load situation of change, are dispatched and calculated using adaptive resource Method forecast resource requirements, and send resource scheduling request；
Infrastructure component, for being adjusted according to resource scheduling request to infrastructure.
The physical resource Information includes IP address, network broadband, main frame, MAC Address.
The data that the interactive component also obtains to monitor component are analyzed, and judge whether the request meets QoS about Beam, it is determined for compliance with the dispatching priority of the task of constraint.
The adaptive resource dispatching algorithm is to ask money of the record analysis to different request tasks based on System History Source demand is predicted.
Described be adjusted to infrastructure matches somebody with somebody confidence including the management of virtual machine dynamic, the switching manipulation of physical machine, modification Breath.
The adaptive resource dispatching algorithm is the cloud resource feature clustering algorithm based on Markov model.
Based on said system, present invention also offers a kind of cloud platform adaptive resource dispatching method, comprise the following steps：
Step 1：User task request is received, user task request is analyzed, judges corresponding virtual machine class Type, QoS demand and quantity demand, the task is assigned on suitable virtual machine and performed；
Step 2：Whether the load of monitor component monitoring platform system changes, if changing, by the load Change send to decision-making component；
Step 3：Decision-making component services institute according to the change of the load using adaptive resource dispatching algorithm computing system The cloud resource needed；
Step 4：Based on the resources, judge whether physical node performance exceedes the index of prediction, if exceeding, detection Whether the node virtual machine leaves unused, if idle, the virtual machine is destroyed and discharges resource；If the physical node performance is not Reach the index of prediction, finding optimal migration physical node by decision-making technique carries out virtual machine creating；Failing to find most Excellent physical node, remind keeper's lack of hardware resources.
The load of the plateform system is the resource usage amount of physical server.
The adaptive resource dispatching algorithm is the cloud resource feature clustering algorithm based on Markov model.
The adaptive resource dispatching algorithm is that different requests are appointed based on plateform system historical requests record dynamic analysis The resource requirement of business is predicted.
Beneficial effects of the present invention：
1st, the present invention performs resource deployment adjustment according to the load change monitored in real time, and what it is according to system concurrency number is not all It, which is distributed, meets the resource of the load and supports, the cloud platform resource effectively solved idle and wastes problem, greatly improves Resource utilization, the indirect saving for platform energy consumption provide possibility support.
2nd, the present invention is to be based on HadoopJava framework technologies, and system application is deployed in cloud platform.Cloud clothes can be embodied The efficient scalability of business, the improvement of cloud platform resource-adaptive scheduling is realized again, it is convenient to service at any time, improve the utilization of resources Rate.
Brief description of the drawings
Fig. 1 is the architecture of cluster resource management；
Mapping relations of the Fig. 2 between QoS demand and cloud resource；
Fig. 3 is that cloud platform adaptive resource of the present invention dispatches system diagram；
Fig. 4 is cloud platform adaptive resource scheduling flow figure of the present invention.
Embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provides further instruction to the application.It is unless another Indicate, all technologies and scientific terminology that the present invention uses have leads to the application person of an ordinary skill in the technical field The identical meanings understood.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
In the case where not conflicting, the feature in embodiment and embodiment in the application can be mutually combined.
As shown in figure 1, resource management architecture：The framework be divided into four layers they be respectively：Physical layer, dispatch layer, articulamentum, Application layer.The resource pool of physical layer i.e. cloud platform, that is, deploy the physical node of resources of virtual machine, and it includes in cluster Processor, hard disk, internal memory, the resource such as network bandwidth, provide basis for scheduling.What application layer represented is our application software Program, articulamentum are to provide the interface for being connected to cloud platform for software program by professional equipment.Dispatch layer be exactly then according to The task difference that family is submitted distributes corresponding resources of virtual machine, and specific strategy is pair to design by the analysis of historical data Different task distributes the prediction algorithm of respective resources.
QoS index is to weigh the standard for the service quality that Web service provider is provided, including the response time, expense, can By property, availability and confidence level.QoS index has certain specification to network bandwidth, network delay etc., to ensure the matter of service Amount.There are various mapping relations between the QoS demand and cloud resource of user, as shown in Figure 2.
Embodiment one
The scheduling of resource of cloud platform is exactly to submit task requests according to user, for the mistake of the virtual resource needed for user's distribution Journey.A kind of self-adapting dispatching system based on cloud platform is present embodiments provided, as shown in figure 3, including：
(1) component registration, for cloud cluster management, record and store physical resource Information；
Alternatively, component registration is specifically responsible for recording and managed and collects including IP address, network broadband, main frame, MAC Address etc. Physical resource Information in group.Information above is recorded in database, when having physical machine to add or removing, is responsible for modification number According to storehouse information.
(2) interactive component, for receiving user task request, analyze and judge that the user asks corresponding virtual machine class Type, qos parameter and quantity demand；And whether the resource request of analysis task is reasonable, and the data to monitor component acquisition Analyzed, judge whether the task requests meet QoS constraints.
Alternatively, the data that the interactive component obtains from monitor component include making for each resources such as hard disk, CPU, internal memory With situation, and task response-time and network traffics etc., judged according to above-mentioned data.
Judge whether the task requests meet QoS constraints, and be determined for compliance with the dispatching priority of the task of constraint.It is based on QoS priority packet resource scheduling algorithm --- Sufferage algorithms determine the priority of task, i.e., follow-up dispatching sequence, The principle of the Sufferage algorithms is that a task will be assigned to such a resource, if the task is not assigned to this Resource, it will the loss of maximum is suffered on the deadline, the algorithm is that each task specifies a Sufferage value, the value Secondary small deadline and the difference of minimum completion time of task are defined as, the big task of difference has high dispatching priority.
The algorithm is that the number of the computing resource needed for task based access control performs carries out priority packet, it is preferable that by resource Demand it is big as higher priority, with QosLevel vector representations, each component in vector is excellent as a Qos First level packet, because QosLevel vectors are a n-dimensional vectors, is divided into n groups, according to Qos priority by all gridding tasks It is scheduled.
Algorithm performs step is as follows：Obtain execution time of all tasks on All hosts and save it in matrix In E；M task is divided into by n QoS priority packet according to the executable situation of task；Matrix dividing E obtains submatrix Ei,m, I represents i-th of priority packet；For each packet, Sufferage algorithms are performed, determine the dispatching priority of each packet.
(3) monitor component, for monitoring cluster load, including the type of virtual machine, state and resource utilization, physics section The state and resource utilization of point, and the network broadband and task response-time of cluster.
Monitor component is basic module.Monitoring platform system load (the resource usage amount of physical server)；And monitoring The service condition of each resource such as hard disk, CPU, internal memory in cloud cluster, and task response-time and network traffics etc..Here monitor Cluster to include virtual machine, physical node and/or cluster overall.Its state (hollow and startup) is mainly monitored for virtual machine, Resource utilization and type of virtual machine.Its state (off/on/dormancy), CPU, internal memory etc. are mainly monitored for physical node Resource utilization.Then include network bandwidth, task requests speed, task for cluster state and complete all kinds of parameters etc..
(4) decision-making component, it is pre- using adaptive resource dispatching algorithm for loading situation of change according to plateform system cluster Resource requirement is surveyed, and sends resource scheduling request.
Decision-making component is nucleus module, and decision-making component can receive the Various types of data of monitor component, be sent out according to Surveillance center Load change (the resource usage amount of the physical server) situation sent, resource requirement is realized by adaptive resource dispatching algorithm Prediction, and resource scheduling request is sent, to realize working platform performance optimum level.What the scheduling of this module was calculated is according to software The resource requirement that a large amount of historical records analysis of the different requests of system comes to following different request tasks carries out the knot of reasonable prediction Fruit, it is using the set goal as driving behavior, by virtual resource self-adjusted block to different request tasks.
The adaptive resource dispatching algorithm used in the present embodiment is the cloud resource feature clustering based on Markov model Algorithm.There is strong nonlinearity between resource requirement and dynamic load change in cloud platform system, in order to its resource Changes in demand accurately predicted, can only be realized using the prediction mode that multi-model be combined with each other, because simply Forecast model method is more beneficial for linear relationship, and the method combined using multi-model can more accurately capture this non- In linear relationship data hide Behavior law, and then according to this rule come realize under different situations resource requirement it is pre- Survey.This implementation is needed by the cloud resource feature clustering algorithm based on Markov model to the resource of cloud platform software future Ask and be predicted.
The basic thought of Markov prediction is：According to the short-term trend of nearest some event of status predication of event Or the index of change, rather than the markov property of things is referred to as according to preneoplastic state, the property.It is multiple with markov property The things set of consecutive variations just constitutes Markov chain, and their evolution process is referred to as Markov process.Based on Ma Er Can the cloud resource prediction algorithm of husband's model be described below：
History resource using information is obtained as training data, the training data set to acquisition carries out cluster analysis, obtained Demand Forecast Model of the system to resource when obtaining different loads.The history resource using information real-time update, so as to obtain reality Shi Gengxin Demand Forecast Model.
So-called cluster analysis is exactly data acquisition system to be divided into the process of multiple small sets being made up of similar characteristics data, Data in so each small set have the similar features of height.(Fuzzy C is equal by present invention selection fuzzy clustering algorithm FCM It is worth clustering algorithm), cluster analysis is carried out to test result data collection to realize, and then obtain software and cloud is provided under different loads The demand model in source.
Define F={ f1,f2,...,fnRepresent the load sequence collection of system, here n take be less than more than 0 100,000 integer, Each fnAll it is a data point, they have k segmentation (clustering cluster) all in x gts：Cluster= (Cluster1, Cluster2..., ClusterK), and Markov model corresponding to each cluster：λ1, λ2..., λK。
Just by regarding the distance in high-dimensional feature space as optimization aim, that data point is categorized into K is poly- for clustering algorithm In class cluster.Herein from super tan as the mapping function for input space data being mapped to high-dimensional feature space.Surpass just The expression formula for cutting function is as follows：
K(fi,fj)=1-tanh (- | | fi-fj||2/α2),α>0
Specifically, the FCM clustering algorithms step is as follows：
Step 1：Random initializtion subordinated-degree matrix U is removed with [0,1] section, and it is met constraints
Step 2：Normalized is done to system load sequence sets F, as data input into clustering algorithm；
Step 3：K cluster centre Ci, i=1,2 ... are calculated, k, secondly, F is mapped by super tan by higher-dimension Each data point in F is calculated in space and carries out data clusters with K cluster centre distance, forms K clustering cluster；
Step 4：K clustering cluster is recirculated iterative calculation, then obtains one group of k new cluster centre Ci ', compares and sentences Disconnected cluster centre numerical value, which whether there is, to change, if unchanged, i.e.,:Ci=Ci ', terminate iteration, otherwise, return to Step3；
Step 5：Export cluster centre Ci and K group cluster cluster.
By obtaining the clustering cluster of corresponding data after the calculating of FCM algorithms, and each clustering cluster corresponds to Markov Model, the resource requirement forecast model for training us by the way that data and model are carried out with a series of weighted optimization and wanting to obtain. It is resource requirement needed for its calculating, so as to rational right according to the Demand Forecast Model when there is new task requests to submit The task requests carry out resource deployment.
Wherein, involved resource includes：Internal memory, network bandwidth, CPU etc., matching user's QoS demand description master Show calculating speed, handling capacity, delay etc. (as shown in Figure 2).Mainly wrapped in cloud data center resource management system Pattern containing two-level scheduler：In physical node on the scheduling of virtual resource and virtual resource task distribution.In task-virtual resource Task assignment procedure in, system is classified according to the QoS demand of user task to loading commissions first, it is preferable that according to Priority is grouped, and task is assigned on suitable virtual machine and performed.Then in the scheduling of resource process of virtual machine-main frame In, physical node needs to take into full account the resource requirement of virtual resource, so as to optimize the deployment of virtual machine, improves the utilization of resources Rate.
(5) infrastructure component, for being adjusted according to resource scheduling request to infrastructure.
Infrastructure component：The component mainly be responsible for infrastructure artificial adjustment operation, including to cluster, physical machine, Virtual machine etc. is managed, and concrete operations have establishment and release, the switching manipulation of physical machine, the modification configuration information of virtual machine Deng.The module belongs to the execution stage, and it can receive the resource scheduling request scheme of decision center transmission, and complete phase according to scheme It should operate.
Embodiment two
System is dispatched based on above-mentioned cloud platform adaptive resource, a kind of cloud platform adaptive resource is present embodiments provided and adjusts Degree method, it is characterised in that comprise the following steps：
Step 1：User task request is received, user task request is analyzed, judges corresponding virtual machine class Type, QoS demand and quantity demand, the task is assigned on suitable virtual machine and performed；
Step 2：Whether the load of monitor component monitoring platform system changes, if changing, by the load Change send to decision-making component；
Step 3：Decision-making component passes through adaptive resource dispatching algorithm, computing system service according to the change of the load Required cloud resource；
Step 4：Based on the resources, judge whether physical node performance exceedes the index of prediction, if exceeding, detection Whether the node virtual machine leaves unused, if idle, the virtual machine is destroyed and discharges resource；If the physical node performance is not Reach the index of prediction, finding optimal migration physical node by decision-making technique carries out virtual machine creating；Failing to find most Excellent physical node, remind keeper's lack of hardware resources.
Preferably, the load of the plateform system is the resource usage amount of physical server.
Preferably, the prediction algorithm is based on resource requirement of the System History request record analysis to different request tasks It is predicted.
Preferably, the prediction algorithm is the cloud resource feature clustering algorithm based on Markov model.
The basic thought of Markov prediction is：According to the short-term trend of nearest some event of status predication of event Or the index of change, rather than the markov property of things is referred to as according to preneoplastic state, the property.It is multiple with markov property The things set of consecutive variations just constitutes Markov chain, and their evolution process is referred to as Markov process.Based on Ma Er Can the cloud resource prediction algorithm of husband's model be described below：
History resource using information is obtained as training data, the training data set to acquisition carries out cluster analysis, obtained Demand Forecast Model of the system to resource when obtaining different loads.The history resource using information real-time update, so as to obtain reality Shi Gengxin Demand Forecast Model.
So-called cluster analysis is exactly data acquisition system to be divided into the process of multiple small sets being made up of similar characteristics data, Data in so each small set have the similar features of height.(Fuzzy C is equal by present invention selection fuzzy clustering algorithm FCM It is worth clustering algorithm), cluster analysis is carried out to test result data collection to realize, and then obtain software and cloud is provided under different loads The demand model in source.
State is monitored when the present invention is to system operation, and according to system under monitoring data design different loads to resource The prediction of demand, finally with reference to resource dispatching strategy, realize to existing software in the self-adaption deployment of cloud platform, solve system When idle, the occupancy and waste problem of cloud resource, and greatly increase resource utilization.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.

Claims (10)

1. a kind of cloud platform adaptive resource dispatches system, it is characterised in that including：
Component registration, for cloud cluster management, record and manage physical resource Information；
Interactive component, for receiving user task request, analyze and judge that the user asks corresponding type of virtual machine, QoS Parameter and quantity demand；And whether the resource request of analysis task is reasonable；
Monitor component, for monitoring the service condition of each resource in cloud cluster, including the type of virtual machine, state and resource make With rate, the state and resource utilization of physical node, and the network broadband and task response-time of cluster；
Decision-making component, the cluster for being sent according to decision-making component loads situation of change, pre- using adaptive resource dispatching algorithm Resource requirement is surveyed, and sends resource scheduling request；
Infrastructure component, for being adjusted according to resource scheduling request to infrastructure.
A kind of 2. cloud platform adaptive resource scheduling system as claimed in claim 1, it is characterised in that the physical resource letter Breath includes IP address, network broadband, main frame, MAC Address.
3. a kind of cloud platform adaptive resource scheduling system as claimed in claim 1, it is characterised in that the interactive component is also The data obtained to monitor component are analyzed, and judge whether the request meets QoS constraints, are determined for compliance with the task of constraint Dispatching priority.
A kind of 4. cloud platform adaptive resource scheduling system as claimed in claim 1, it is characterised in that the adaptive resource Dispatching algorithm is predicted based on resource requirement of the System History request record analysis to different request tasks.
5. a kind of cloud platform adaptive resource scheduling system as claimed in claim 1, it is characterised in that described to infrastructure It is adjusted including the management of virtual machine dynamic, the switching manipulation of physical machine, modification configuration information.
A kind of 6. cloud platform adaptive resource scheduling system as claimed in claim 1, it is characterised in that the adaptive resource Dispatching algorithm is the cloud resource feature clustering algorithm based on Markov model.
A kind of 7. cloud platform adaptive resource dispatching method based on any one of the claim 1-6 systems, it is characterised in that Comprise the following steps：
Step 1：Receive user task request, to the user task request analyze, judge corresponding type of virtual machine, QoS demand and quantity demand, the task is assigned on suitable virtual machine and performed；
Step 2：Whether the load of monitor component monitoring platform system changes, if changing, by the change of the load Change and send to decision-making component；
Step 3：Decision-making component according to the change of the load, using adaptive resource dispatching algorithm computing system service needed for Cloud resource；
Step 4：Based on the resources, judge whether physical node performance exceedes the index of prediction, if exceeding, described in detection Whether node virtual machine leaves unused, if idle, the virtual machine is destroyed and discharges resource；If the physical node performance is not up to The index of prediction, optimal migration physical node is found by decision-making technique and carries out virtual machine creating；Failing to find optimal thing Node is managed, reminds keeper's lack of hardware resources.
8. a kind of cloud platform adaptive resource scheduling system as claimed in claim 7, it is characterised in that the plateform system Load the resource usage amount for physical server.
A kind of 9. cloud platform adaptive resource scheduling system as claimed in claim 7, it is characterised in that the adaptive resource Dispatching algorithm is the cloud resource feature clustering algorithm based on Markov model.
A kind of 10. cloud platform adaptive resource scheduling system as claimed in claim 7, it is characterised in that the adaptive money Source dispatching algorithm is predicted based on resource requirement of the plateform system historical requests record dynamic analysis to different request tasks.
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