CN109379229B - Tenant-oriented micro-service cloud platform admission control method - Google Patents
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Abstract
According to the tenant-oriented micro-service cloud platform admission control method, on the basis of the calculated amount which can be provided by the physical machine resources of the known micro-service cloud platform and the request in the current time slice, unreasonable load is filtered at the gateway of the micro-service platform according to the signed service level protocol, then the state of the current micro-service cloud platform is judged, and the actual benefit of a service provider is improved under the overload condition to serve as an optimization target, so that the corresponding request is admitted and rejected. The invention considers the priority of the tenant level and the priority of the application level while considering the priority of the service, and maximizes the actual income of the service provider on the premise of meeting the SLA of the user.
Description
Technical Field
The invention relates to the field of cloud computing services, in particular to a tenant-oriented micro-service cloud platform admission control method.
Background
The cloud computing is a pay-per-use mode, a dynamic and easily-retractable computing service is provided by using a network, a resource pool for supporting the computing service is formed by maintaining a huge server cluster, and computing resources such as a CPU (central processing unit), an internal memory, a network, a hard disk and the like are provided for upper-layer applications. When the tenant uses the cloud computing service, only the required computing resource needs to be applied. The mode of service addition, use and delivery based on the internet of cloud computing comprises the following three layers of services, namely IaaS (infrastructure as a service): access to networking functions, computers (virtual or dedicated hardware), and data storage spaces is typically provided; paas (platform as a service): generally, a universal middleware product and an application program infrastructure are provided to help users to better build products; saas (software as a service): it is common to provide a sophisticated product whose operation and management are all under the responsibility of the service provider. The reliable, easily extensible, customized on demand, SLA-defined services can be provided to the user through any one of the three services.
As a PaaS cloud platform, the micro service cloud platform only provides an IaaS-level index or a single PaaS-level index, for example, only provides an availability index, in a current environment.
This is a risk to both the tenant and the cloud provider, due to inappropriate or less comprehensive indicators. On one hand, a user cannot obtain services which definitely guarantee the user to be concerned, and on the other hand, a cloud service provider cannot accurately evaluate the resource condition required by meeting the user requirements. Therefore, a more comprehensive set of Qos indexes at the PaaS level is required to establish the SLA.
Meanwhile, most of the current micro-service cloud platforms only consider the priority of the service, and are not distinguished between the tenant level and the application level, so that in the current increasingly complex cloud environment, in order to distinguish the benefit difference brought by the tenants with different levels and the applications with different levels, the benefit of an enterprise is improved as much as possible, and a tenant-oriented admission control method for guaranteeing the QoS of the tenants is urgently needed.
The admission control problem in cloud computing is always a hot problem of research, various optimal profit models and methods have appeared in the field of mathematical model optimization and the field of control theory throughout the research dynamic at home and abroad, however, the theoretical optimal profit does not represent the actual optimal profit, many invisible profits cannot be measured through the mathematical formula, and different strategies need to be executed according to the own preference of the cloud service provider, so that the practical profit of the service provider is maximized on the premise of satisfying the SLA of the user as much as possible, which is a problem to be considered.
Disclosure of Invention
Aiming at the problem that the actual income of a service provider cannot be maximized on the premise that the current micro-service cloud platform can not meet the user request, the invention provides a tenant-oriented micro-service cloud platform admission control method, which adopts the technical scheme that:
a tenant-oriented micro-service cloud platform admission control method comprises the following steps:
s10, a request of a client requests a certain service in a cloud platform through a gateway, and the gateway is responsible for collecting the request and acquiring the tenant level, the application level and the service level serviceLevel of the tenant to which the target service belongs;
s20, if the unit time is timeUnit and the time slice is T, the unit time number in the current time slice
timeNumber＝T/timeUnit
For each service in the current time slice, the gateway counts the request number pv of the service i in the current time sliceiMeanwhile, comparing the number of the requested time slices with the number of serviceMaxPcv timeNumber specified in SLA, if pviLess than serviceMaxPv timeNumber, then admit the service all requests in that time slice, if pviIf the number of the requests exceeds the service MaxPv, the requests exceeding the SLA specification are rejected; serviceMaxPv refers to the maximum concurrent access amount of a service;
s30, the gateway obtains the calculated amount S provided by physical resources per second in the cloud platform benchmark test environment, namely the number of times of floating point operation which can be carried out per second by the current machine, and meanwhile, for the service i, the average service response time measured by the service in the cloud platform benchmark test environment can be measured as serviceResponseTime when the service is deployediAnd throughPut of service through outiThen the amount of computation required per request of the service is vi＝S*serviceResponseTimei；
Counting the calculated quantity index v needed by each service according to the formula1,v2…vm；
And S40, under each time slice, the gateway acquires the calculated amount V which can be provided by the current time slice of the cloud platform physical machine resource. Judging the load state of the cloud platform, and if the platform is in a low load state, directly admitting all requests under the current time slice; if the platform is in a full load state, early warning is carried out, and all requests under the current time slice are admitted; if the current overload state is reached, an overload access control strategy based on a genetic algorithm is carried out, and a plurality of access control schemes are obtained through the genetic algorithm;
s50, after the genetic algorithm is finished, selecting a scheme with H% before benefit from a scheme set Z as an optimal set W, wherein the value of H can be changed according to the actual situation and is determined by a service provider, calculating the strategy score of each scheme in the optimal set W, and selecting the scheme with the highest strategy score as the final admission control scheme.
Preferably, the specific process of the step S40 for obtaining several admission control schemes through genetic algorithm is as follows:
s401, setting the number of all requests under the current time slice as m, and setting the genetic space formed by m services as { x }1,x2,x3…xm},xiE.0, 1, when equal to 0, the request is rejected, when equal to 1, the request is satisfied, and the total available calculation amount index of the platform is V, and the calculation amount in each request current time slice is { V1,v2…vm}；
S402, generating an initial population, namely randomly generating n 0,1 sequence strings with the length of m, namely { Z1,Z2,Z3…Zn}；
S403, Fitness Fitness calculation is carried out,
Fitness＝∑xd*pd(xd＝1)-∑xj*kj(xj＝0)
wherein p isdIs the benefit obtained when the d-th request is satisfied:
pd＝serviceUnitPrice*vd
the service UnitPoint is the service unit price and the price of unit calculated amount;
kjis the penalty to be deducted if the jth request is not satisfied:
kj＝serviceUnitPrice*vi*compensateRate
the compensateRate is the indemnity, and when the cloud service provider violates the SLA rule, the corresponding loss of the user needs to be compensated according to the indemnity;
s404, performing traditional selection, crossing and mutation strategies, and stopping iteration of the genetic algorithm when the maximum adaptive value of the genetic algorithm tends to be stable, wherein the final result set is Z.
Preferably, the low load state determination condition is:
V>∑viu%, U% is a threshold for judging low load and full load, and is set by a service provider;
the judgment conditions of the full load state are as follows:
V>∑viand V is<∑vi*U％；
The judgment conditions of the overload state are as follows: v<∑vi。
Preferably, the strategy score of the scheme is calculated as follows:
strategy score of its protocoliIs formed by adding the policy scores scoreService of each service.
scorei＝∑scoreService
scoreService＝tenantScore*R1％+applicationScore*R2％+scoreService*R3％
Wherein tentacle represents a tenant score, applicationScore represents an application score, serviceScore represents a service score, and R1, R2, and R3 are set constants. The settings of R1, R2, R3 represent different tendencies of different service providers, for example when R1> R2> R3, a representative service provider wishes to satisfy preferentially any request of a high priority tenant, then any request of the underlying application.
The invention also provides a service level agreement, which comprises service index commitment, charging standard and punishment standard;
service index commitment, namely when the service deploys the cloud platform, the service availability ratio, the service response time and the maximum access quantity of the service need to be committed;
and calculating a standard, and charging the service according to the calculated amount. The calculated amount per request of service i is S serviceResponseTimei。
The service price is related to the unit calculation volume price serviceUnitPoint, the service maximum concurrent access volume serviceMaxPv.
Penalty criteria: when the user is not provided with the service according to the provision of the SLA in a certain period of time, the corresponding loss cost of the user in the period of time needs to be compensated.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
on the basis of the calculation amount which can be provided by the known physical machine resources of the micro service cloud platform and the request in the current time slice, unreasonable load is filtered at the gateway of the micro service platform according to the signed SLA, then the state of the current micro service cloud platform is judged, and the actual benefit of a service provider is improved under the overload condition to serve as an optimization target, so that the corresponding request is admitted and rejected.
Drawings
Fig. 1 is a flowchart of the whole admission control process of the tenant-oriented microservice cloud platform admission control method provided by the present invention, and requests in a time slice are admitted and rejected by taking the time slice as a unit.
Fig. 2 is a schematic view of an application scenario of the tenant-oriented micro service cloud platform admission control method provided by the present invention, where the admission control method is implemented at API GateWay.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and are used for illustration only, and should not be construed as limiting the patent. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1 to 2, a tenant-oriented micro service cloud platform admission control method includes the following steps:
s10, a request of a client requests a certain service in a cloud platform through a gateway, and the gateway is responsible for collecting the request and acquiring the tenant level, the application level and the service level serviceLevel of the tenant to which the target service belongs;
s20, if the unit time is timeUnit and the time slice is T, the unit time number in the current time slice
timeNumber＝T/timeUnit
For each service in the current time slice, the gateway counts the request number pv of the service i in the current time sliceiAt the same time, combine it withThe number of requested time slices specified in SLA (service request max pv) is compared with the number of timeunmbers if pviLess than serviceMaxPv timeNumber, then admit the service all requests in that time slice, if pviIf the number of the requests exceeds the service MaxPv, the requests exceeding the SLA specification are rejected; serviceMaxPv refers to the maximum concurrent access amount of a service;
s30, the gateway obtains the calculated amount S provided by physical resources per second in the cloud platform benchmark test environment, namely the number of times of floating point operation which can be carried out per second by the current machine, and meanwhile, for the service i, the average service response time measured by the service in the cloud platform benchmark test environment can be measured as serviceResponseTime when the service is deployediAnd throughPut of service through outiThen the amount of computation required per request of the service is vi＝S*serviceResponseTimei；
Counting the calculated quantity index v needed by each service according to the formula1,v2…vm；
And S40, under each time slice, the gateway acquires the calculated amount V which can be provided by the current time slice of the cloud platform physical machine resource. Judging the load state of the cloud platform, and if the platform is in a low load state, directly admitting all requests under the current time slice; if the platform is in a full load state, early warning is carried out, and all requests under the current time slice are admitted; if the current overload state is reached, an overload access control strategy based on a genetic algorithm is carried out, and a plurality of access control schemes are obtained through the genetic algorithm;
s50, after the genetic algorithm is finished, selecting a scheme with H% before benefit from a scheme set Z as an optimal set W, wherein the value of H can be changed according to the actual situation and is determined by a service provider, calculating the strategy score of each scheme in the optimal set W, and selecting the scheme with the highest strategy score as the final admission control scheme.
As a further preferred embodiment, the strategy score of the scheme is calculated as follows:
strategy score of its protocoliIs formed by adding the policy scores scoreService of each service.
scorei＝∑scoreService
scoreService＝tenantScore*R1％+applicationScore*R2％+serviceScore*R3％
Wherein tentacle represents a tenant score, applicationScore represents an application score, serviceScore represents a service score, and R1, R2, and R3 are set constants. The settings of R1, R2, R3 represent different tendencies of different service providers, for example when R1> R2> R3, a representative service provider wishes to satisfy preferentially any request of a high priority tenant, then any request of the underlying application.
Wherein, the low load state is judged under the following conditions: v>∑viU%, U% is a threshold for judging low load and full load, and is set by a service provider;
the judgment conditions of the full load state are as follows:
V>∑viand V is<∑vi*U％
The judgment conditions of the overload state are as follows: v<∑vi。
Example 2
This example is consistent with the above examples and is further defined in terms of the steps of the genetic algorithm to obtain several admission control schemes. The specific steps of the genetic algorithm for obtaining a plurality of admission control schemes are as follows:
s401, setting the number of all requests under the current time slice as m, and setting the genetic space formed by m services as { x }1,x2,x3…xm},xiE.0, 1, when equal to 0, the request is rejected, when equal to 1, the request is satisfied, and the total available calculation amount index of the platform is V, and the calculation amount in each request current time slice is { V1,v2…vm}；
S402, generating an initial population, namely randomly generating n 0,1 sequence strings with the length of m, namely { Z1,Z2,Z3…Zn}；
S403, Fitness Fitness calculation is carried out,
Fitness＝∑xd*pd(xd＝1)-∑xj*kj(xj＝0)
wherein p isdIs the benefit obtained when the d-th request is satisfied:
pd＝serviceUnitPrice*vd
the service UnitPoint is the service unit price and the price of unit calculated amount;
kjis the penalty to be deducted if the jth request is not satisfied:
kj＝serviceUnitPrice*vi*compensateRate
the compensateRate is the indemnity, and when the cloud service provider violates the SLA rule, the corresponding loss of the user needs to be compensated according to the indemnity;
s404, performing traditional selection, crossing and mutation strategies, and stopping iteration of the genetic algorithm when the maximum adaptive value of the genetic algorithm tends to be stable, wherein the final result set is Z.
The invention also provides a service level agreement, which comprises service index commitment, charging standard and punishment standard;
service index commitment, namely when the service deploys the cloud platform, the service availability ratio, the service response time and the maximum access quantity of the service need to be committed;
and calculating a standard, and charging the service according to the calculated amount. The calculated amount per request of service i is S serviceResponseTimei。
The service price is related to the unit calculation volume price serviceUnitPoint, the service maximum concurrent access volume serviceMaxPv.
Penalty criteria: when the user is not provided with the service according to the provision of the SLA in a certain period of time, the corresponding loss cost of the user in the period of time needs to be compensated.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (3)
1. A tenant-oriented micro-service cloud platform admission control method is characterized by comprising the following steps:
s10, a request of a client requests a certain service in a cloud platform through a gateway, and the gateway is responsible for collecting the request and acquiring the tenant level, the application level and the service level serviceLevel of the tenant to which the target service belongs;
s20, if the unit time is timeUnit and the time slice is T, the unit time number in the current time slice
timeNumber＝T/timeUnit
For each service in the current time slice, the gateway counts the request number pv of the service i in the current time sliceiMeanwhile, comparing the number of the requested time slices with the number of serviceMaxPcv timeNumber specified in SLA, if pviLess than serviceMaxPv timeNumber, then admit the service all requests in that time slice, if pviIf the number of the requests exceeds the service MaxPv, the requests exceeding the SLA specification are rejected; serviceMaxPv refers to the maximum concurrent access amount of a service;
s30, the gateway obtains the calculated amount S provided by physical resources per second in the cloud platform benchmark test environment, namely the number of times of floating point operation which can be carried out per second by the current machine, and meanwhile, for the service i, the average service response time measured by the service in the cloud platform benchmark test environment can be measured as serviceResponseTime when the service is deployediAnd throughPut of service through outiThen the amount of computation required per request of the service is vi＝S*serviceResponseTimei；
Counting the calculated quantity index v needed by each service according to the formula1,v2…vm；
S40, under each time slice, the gateway obtains the calculated amount V which can be provided by the current time slice of the cloud platform physical machine resource; judging the load state of the cloud platform, and if the platform is in a low load state, directly admitting all requests under the current time slice; if the platform is in a full load state, early warning is carried out, and all requests under the current time slice are admitted; if the current overload state is reached, an overload access control strategy based on a genetic algorithm is carried out, and a plurality of access control schemes are obtained through the genetic algorithm;
s50, after the genetic algorithm is finished, selecting a scheme with H% of benefit from the scheme set Z as an optimal set W, calculating the strategy score of each scheme in the optimal set W, and selecting the scheme with the highest strategy score as a final admission control scheme;
the specific process of the step S40 for obtaining several admission control schemes through genetic algorithm is as follows:
s401, setting the number of all requests under the current time slice as m, and setting the genetic space formed by m services as { x }1,x2,x3…xm},xiE.0, 1, when equal to 0, the request is rejected, when equal to 1, the request is satisfied, and the total available calculation amount index of the platform is V, and the calculation amount in each request current time slice is { V1,v2…vm}；
S402, generating an initial population, namely randomly generating n 0,1 sequence strings with the length of m, namely { Z1,Z2,Z3…Zn}；
S403, Fitness Fitness calculation is carried out,
Fitness＝∑xd*pd(xd＝1)-∑xj*kj(xj＝0)
wherein p isdIs the benefit obtained when the d-th request is satisfied:
pd＝serviceUnitPrice*vd
the serviceUnitPoint is the service unit price, namely the price of unit calculated amount;
kjis the penalty to be deducted if the jth request is not satisfied:
kj＝serviceUnitPrice*vi*compensateRate
the compensateRate is the indemnity, and when the cloud service provider violates the SLA rule, the corresponding loss of the user needs to be compensated according to the indemnity;
s404, performing traditional selection, crossing and mutation strategies, and stopping iteration of the genetic algorithm when the maximum adaptive value of the genetic algorithm tends to be stable, wherein the final result set is Z.
2. The tenant-oriented micro service cloud platform admission control method according to claim 1, wherein the low load state is determined by:
V>∑viu%, U% is a threshold for judging low load and full load, and is set by a service provider;
the judgment conditions of the full load state are as follows:
V>∑viand V is<∑vi*U％；
The judgment conditions of the overload state are as follows: v<∑vi。
3. The tenant-oriented micro service cloud platform admission control method according to claim 1, wherein the strategy score of the scheme is calculated as follows:
strategy score of its protocoliThe policy score scoreService for each service is added to:
scorei＝∑scoreService
scoreService＝tenantScore*R1％+applicationScore*R2％+serviceScore*R3％
wherein tentacle represents a tenant score, applicationScore represents an application score, serviceScore represents a service score, and R1, R2, and R3 are set constants.
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| CN104135535A (en) * | 2014-08-14 | 2014-11-05 | 苏州大学 | Tenant adjusting method and system oriented to cloud computing |
| CN106095569A (en) * | 2016-06-01 | 2016-11-09 | 中山大学 | A kind of cloud workflow engine scheduling of resource based on SLA and control method |
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