CN116155835A - Cloud resource service quality assessment method and system based on queuing theory - Google Patents

Cloud resource service quality assessment method and system based on queuing theory Download PDF

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CN116155835A
CN116155835A CN202310071364.8A CN202310071364A CN116155835A CN 116155835 A CN116155835 A CN 116155835A CN 202310071364 A CN202310071364 A CN 202310071364A CN 116155835 A CN116155835 A CN 116155835A
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service
node
quality
nodes
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CN116155835B (en
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孟凡超
孟凡浩
朴学峰
初佃辉
卢阳
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Harbin Institute of Technology Weihai
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/80—Actions related to the user profile or the type of traffic
    • H04L47/805—QOS or priority aware
    • 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
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention provides a cloud resource service quality assessment method and a system based on queuing theory, which are used for determining the processing capacity and capacity of a service node relative to service; classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes; and aiming at different service nodes, adopting a corresponding queuing model to evaluate the service quality, and outputting an evaluation result. According to the cloud resource management method and the cloud resource management system, essential characteristics of cloud resource consumption can be described on a service level, and the service quality requirement of the terminal user is mapped into the consumption of the cloud resource, so that the renting cost of the cloud resource is reduced for a service provider on the premise that the service quality requirement of the user can be met.

Description

Cloud resource service quality assessment method and system based on queuing theory
Technical Field
The invention belongs to the field of cloud resource service quality evaluation, and particularly relates to a queuing theory-based cloud resource service quality evaluation method and a queuing theory-based cloud resource service quality evaluation system.
Background
With the rapid development of the internet and modern service industry, service systems have been widely deployed and applied in government, business, and educational fields. In order to ensure the performance of the system, the service provider needs to continuously increase the investment of the IT infrastructure, however, the problem of high operation cost, low resource utilization rate, difficult maintenance and the like exists in the expansion of the local data center of the enterprise. Report statistics show that the resource utilization of most enterprise data centers is below 15%, and still less than 5%. Because the load fluctuation of the service system is large, a large amount of residual resources exist in the data center in an idle period, and huge waste of energy consumption is caused.
The service quality, such as response time, reliability, etc., is the most concerned for the end user of the service system, while the main objective of the service provider is to meet the service quality requirement of the user with the lowest cost of renting cloud resources, so how to map the service quality requirement of the end user to the consumption of cloud resources, i.e. to build a quantitative relation model between the service quality requirement and the cloud resource consumption, is the premise and foundation for realizing the optimal supply of cloud resources.
Due to the characteristics of isomerism, dispersity, complexity, diversity and the like of cloud resources in the public cloud environment, the difficulty and the high cost of establishing a quantitative relation model between the service quality requirement and the cloud resource consumption are high, so that how to effectively solve the 'gap' between the service quality requirement and the cloud resource consumption is the problem to be solved by optimizing and supplying the cloud resources in a large-scale service system in the current public cloud environment.
Disclosure of Invention
The invention provides a cloud resource service quality assessment method based on queuing theory, which can measure the service capacity of cloud resources, establish a quantitative relation model between service quality requirements and the service capacity and user load of the cloud resources, thereby providing a theoretical basis for cloud resource optimization supply of a large-scale service system and reducing the lease cost of the cloud resources for service providers.
The cloud resource service quality assessment method comprises the following steps:
s1: calculating the processing capacity and capacity of the service node relative to the service;
s2: dividing the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
s3: aiming at different service nodes and service node clusters, different queuing models are adopted for service quality evaluation;
it should be further noted that, a service node refers to a virtual unit for deploying a service. Let s be a service, vu be a virtual unit, let
Figure BDA0004064887770000021
And->
Figure BDA0004064887770000022
(t=1, 2, …) is the time spent by the service s on the virtual unit vu and the consumed memory, which are random variables, respectively, and the time spent by the service node on each execution and the memory data can be obtained by adopting a performance test method, and the average value of the time spent by the service node on each execution and the memory data can be measured by adopting a mathematical statistics method.
The processing power refers to the average number of service nodes per unit time to fulfill user service requests. Set random variable
Figure BDA0004064887770000023
Mean>
Figure BDA0004064887770000024
Wherein (1)>
Figure BDA0004064887770000025
The average number of times the service s is executed on the virtual unit vu per unit time, and thus the processing power of the service s on the virtual unit vu can be defined as: />
Figure BDA0004064887770000026
Wherein ε vu The value of the processing capacity coefficient is related to the virtualization technology adopted by the virtual unit and the CPU architecture of the physical server.
Capacity refers to the maximum number of concurrent service requests that a service node can accept. Service s is in virtual unitThe capacity on vu can be defined as:
Figure BDA0004064887770000027
where D is vu For the maximum available memory of virtual unit vu,
Figure BDA0004064887770000028
is a random variable +.>
Figure BDA0004064887770000029
Average value of θ vu The capacity coefficient is a value related to the virtualization technology and the storage rack adopted by the virtual unit.
It should be further noted that the method further includes: the method comprises the steps of constructing a mapping relation between service system parameters and queuing system parameters, wherein the specific mapping relation among parameters in a service system is as follows:
{ load → average customer arrival rate, service node processing capability → average attendant processing rate, service node capacity → capacity of system (queue length+number of attendant), number of concurrent service requests → average queue length, response time → average stay rate, throughput → absolute throughput capability, reliability → relative throughput capability, error rate → loss rate }.
It should be further noted that, the classification method of service nodes in step S2 includes dividing service nodes into basic service nodes and combined service nodes according to the number of services included in the service nodes, where one basic service node includes only one service and one combined service node includes multiple services.
It should be further noted that step S3 further includes: aiming at basic service nodes, a single service window queuing model M/M/1/c with capacity constraint is established to evaluate service quality;
also for the combined service node, a multi-class customer single service window queuing model M/H with capacity constraint is established k 1/c to evaluate the quality of service;
a queuing network model is also built for the service node cluster to evaluate the quality of service.
It should be further noted that the manner of evaluating the quality of service based on M/1/c includes:
configuration of
Figure BDA0004064887770000031
A basic service node is represented, wherein s is a service, vu is a virtual unit, and the processing capacity and capacity of the service s on the virtual unit vu are respectively: mu and c;
basic service node
Figure BDA0004064887770000032
The response time of the service s deployment on the virtual unit vu under load λ is:
Figure BDA0004064887770000033
basic service node
Figure BDA0004064887770000034
I.e. the throughput of the service s deployed on the virtual unit vu under load λ is:
Figure BDA0004064887770000035
basic service node
Figure BDA0004064887770000041
I.e. the error rate of the service s deployment on the virtual unit vu under load λ is:
Figure BDA0004064887770000042
basic service node
Figure BDA0004064887770000043
I.e. the reliability of the service s deployed on the virtual unit vu under load λThe reliability is as follows:
Figure BDA0004064887770000044
/>
it is further noted that M/H based k The manner of evaluating the quality of service/1/c includes:
configuration of
Figure BDA0004064887770000045
Represents a combined service node, wherein s= { S 1 ,s 2 ,…,s k -service set, vu is a virtual unit;
service s i The processing capacity, capacity and load of the e S on the virtual unit vu are respectively: mu (mu) i ,c i And lambda (lambda) i The method comprises the steps of carrying out a first treatment on the surface of the Order the
Figure BDA0004064887770000046
The execution time of the virtual unit vu to all services obeys a k-order hyper-exponential distribution, and the distribution function is as follows:
Figure BDA0004064887770000047
capacity constrained multi-class customer single service window queuing model M/H k 1/c, which emphasizes on analyzing each type of customer performance index in the model;
model pair is distributed in load delta= { lambda% 1 ,λ 2 ,…,λ k Combined service node under }
Figure BDA0004064887770000048
Average quality of service of (a) and each service s i Evaluating the service quality of the E S;
wherein v= { s 0 ,s 1 ,…,s n ,s n+1 Is a collection of services for which i ∈V，λ i Representation s i Is a load of (2);
Figure BDA0004064887770000049
and->
Figure BDA00040648877700000410
Representing service nodes +.>
Figure BDA00040648877700000411
Average response time, throughput rate, error rate and reliability of all services in (a)>
Figure BDA00040648877700000412
And
Figure BDA00040648877700000413
representing services s respectively i At the service node->
Figure BDA00040648877700000414
Response time, throughput, error rate, and reliability.
It should be further noted that, the way to build the queuing network model to evaluate the service quality includes:
configuration of
Figure BDA0004064887770000051
Represents a service node cluster, wherein s= { S 1 ,s 2 ,…,s k -a service set; VU= { VU 0 ,vu 1 ,…,vu m Is a set of virtual units, vu 0 Vu, a virtual unit of a control node j (j=1, …, m) is a virtual unit of a service node, m is the number of service nodes, and virtual units of different service nodes may be the same;
let the service set S be in the virtual unit vu j The load distribution on this is: delta j ＝{λ 1j ,λ 2j ,…,λ kj }，λ ij (i=1, 2,., k) is service s i In virtual unit vu j Load on, service cluster
Figure BDA00040648877700000514
Is a load distribution of delta = { lambda 1 ,λ 2 ,…,λ k }，/>
Figure BDA0004064887770000052
It should be further noted that the queuing network model is divided into two layers: a control layer and a service layer;
the control layer is a description of the service request process inside the control node, and the control node is used for controlling the service request process
Figure BDA0004064887770000053
Each service s in (a) i (i=1, 2, …, k) is modeled as a single service node +.>
Figure BDA0004064887770000054
The M/M/1/c model is adopted to evaluate the service quality;
the processing layer comprises m service nodes, if k=1, each service node is a basic service node, otherwise, each service node is a combined service node;
for basic service nodes, the M/M/1/c queuing model is adopted to evaluate the service quality;
for the combined service node, the M/H is adopted k A/1/c queuing model to evaluate its quality of service;
if consider a control node
Figure BDA0004064887770000055
And (3) quality of service: service s i (i=1, 2, …, k) at the service node +.>
Figure BDA0004064887770000056
The response time is as follows:
Figure BDA0004064887770000057
/>
service s i (i=1, 2, …, k) at the serving node
Figure BDA0004064887770000058
The throughput rate is as follows:
Figure BDA0004064887770000059
service s i (i=1, 2, …, k) at the serving node
Figure BDA00040648877700000510
The error rate is as follows:
Figure BDA00040648877700000511
service s i (i=1, 2, …, k) at the serving node
Figure BDA00040648877700000512
The reliability is as follows:
Figure BDA00040648877700000513
wherein,
Figure BDA0004064887770000061
and->
Figure BDA0004064887770000062
Respectively represent control nodes->
Figure BDA0004064887770000063
Average response time, throughput, error rate, and reliability of processing service requests;
Figure BDA0004064887770000064
the service s is a service without considering the quality of service of the control node i At the service node->
Figure BDA0004064887770000065
Response time, throughput, error rate, and reliability.
The invention also provides a cloud resource service quality evaluation system based on queuing theory, which comprises: the system comprises an evaluation determination module, a node classification module and a service quality evaluation module;
the evaluation determination module is used for determining the processing capacity and the capacity of the service node relative to the service;
the node classification module is used for classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
the service quality evaluation module is used for evaluating the service quality by adopting a corresponding queuing model aiming at different service nodes and outputting an evaluation result.
From the above technical scheme, the invention has the following advantages:
the cloud resource service quality assessment method based on queuing theory provided by the invention can be used for describing essential characteristics of cloud resource consumption on a service level, and providing concepts of processing capacity and capacity so as to realize high-level abstraction of cloud resource consumption.
In order to describe the problem of cloud resource quality assessment by adopting the queuing system, the invention establishes the mapping relation between the parameters in the service system and the parameters in the queuing system. Aiming at the characteristic of service node diversity, the service nodes are divided into basic service nodes and combined service nodes. Then according to the different types and scales of the service nodes, the invention provides an M/M/1/c queuing model and an M/H k And three service quality evaluation models, namely a 1/c service quality evaluation model and a queuing network model, are respectively used for evaluating the quality evaluation of the basic service node, the combined service node and the service node cluster, can meet the cloud resource service quality evaluation under different conditions, and have practical values.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for evaluating the quality of service of cloud resources based on queuing theory;
FIG. 2 is a mapping relationship between service system parameters and queuing system parameters of the present invention;
fig. 3 is a schematic diagram of a queuing network model for service node cluster quality of service assessment of the present invention.
Detailed Description
The cloud resource service quality assessment method based on queuing theory provided by the invention measures the service capacity of cloud resources. For the cloud resources or cloud services evaluated by the method, the cloud resources or cloud services are the general names of network technology, information technology, integration technology, management platform technology, application technology and the like based on cloud computing business model application, can form a resource pool, and are used as required, flexible and convenient. Among them, the background service of the cloud resource service requires a large amount of computing and storage resources, such as video websites, picture websites and more portals. Along with the high development and application of the internet industry, each article possibly has an own identification mark in the future, the identification mark needs to be transmitted to a background system for logic processing, data with different levels can be processed separately, and various industry data needs strong system rear shield support and can be realized only through cloud computing. The cloud resource service is mainly applied to medical cloud, cloud security, artificial intelligent service, cloud Internet of things, cloud games, cloud education, cloud conferences and other directions; among them, the artificial intelligence cloud Service is also commonly called AIaaS (AIas a Service, chinese is "AI as Service"). The service mode of the artificial intelligent platform is the mainstream at present, and particularly, the AIaaS platform can split several common AI services and provide independent or packaged services at the cloud. This service mode is similar to an AI theme mall: all developers can access one or more artificial intelligence services provided by the use platform through an API interface, and partial deep developers can also use an AI framework and AI infrastructure provided by the platform to deploy and operate and maintain self-proprietary cloud artificial intelligence services.
The invention relates to a cloud resource service quality assessment method based on queuing theory, which utilizes the characteristic of service node diversity to divide service nodes into basic service nodes and combined service nodes, and establishes M/M/1/c queuing model and M/H k And three service quality evaluation models, namely a 1/c service quality evaluation model and a queuing network model, provide a theoretical basis for cloud resource optimization supply of a large-scale service system by utilizing parameters such as average response time, throughput rate, error rate, reliability and the like of the service, and reduce the lease cost of cloud resources for service providers. The 'gap' between the service quality requirement and the cloud resource consumption is further effectively solved, and the problem to be solved is that cloud resources are optimally supplied in a large-scale service system under the current public cloud environment.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for evaluating quality of service of cloud resources based on queuing theory in an embodiment is shown, where the method includes:
s1: determining processing capacity and capacity of the service node relative to the service;
the specific steps of step S1 are as follows:
s1.1: the method of performance test is adopted to obtain the time and memory data consumed by the service node for executing the service each time, and the average value of the time and memory data is measured by adopting the method of mathematical statistics.
Let s be a service, vu be a virtual unit, let
Figure BDA0004064887770000081
And->
Figure BDA0004064887770000082
(t=1, 2, …) is the time spent and the memory consumed by the service s for each execution on the virtual unit vu, respectively. Then, the performance test can be passed to obtain multiple groups of data and measure the average value of the data.
S1.2: the processing power of the service node relative to the service is calculated. And calculating the evaluation times of the service executed on the service node in unit time by collecting the performance test information, thereby calculating the processing capacity of the service node relative to the service. The processing power refers to the average number of service nodes per unit time to fulfill user service requests. Set random variable
Figure BDA0004064887770000083
Mean of (2)
Figure BDA0004064887770000084
Wherein (1)>
Figure BDA0004064887770000085
The average number of times the service s is executed on the virtual unit vu per unit time, and thus the processing power of the service s on the virtual unit vu can be defined as: />
Figure BDA0004064887770000091
Wherein ε vu To process the capacity coefficient, the coefficient may be estimated by analyzing the virtualization technique employed by the virtual unit and the test results of the physical server CPU architecture.
S1.3: the capacity of the service node relative to the service is calculated.
And performing performance test by setting different concurrent service request numbers, recording and analyzing the maximum concurrent service request number which can be accepted by the service node in the acceptable response time, so as to calculate the capacity of the service node relative to the service.
The number here refers to the maximum number of concurrent service requests that can be accepted by the service node. The capacity of a service s on a virtual unit vu can be defined as:
Figure BDA0004064887770000092
where D is vu For the maximum available memory of virtual unit vu,
Figure BDA0004064887770000093
is a random variable +.>
Figure BDA0004064887770000094
Average value of θ vu The capacity coefficient is determined by performing performance test on the virtualization technology and the storage architecture, wherein the value of the capacity coefficient is related to the virtualization technology and the storage architecture adopted by the virtual unit.
S2: classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
wherein the number of services contained in the service node is determined. If the service node only contains one service, the service node is a basic service node. If the service node only contains a plurality of services, the service node is a combined service node. If a plurality of basic service nodes and combined service nodes work together, and the load distribution of each service node can be different, and a cluster is formed, the cluster is called a service node cluster.
S3: and aiming at different service nodes, adopting a corresponding queuing model to evaluate the service quality, and outputting an evaluation result.
In this embodiment, an applicable qos evaluation model is established according to the classification result of S2. For basic service nodes, a single service window queuing model M/M/1/c with capacity constraint is established to evaluate the service quality. For the combined service node, a multi-class customer single service window queuing model M/H with capacity constraint is established k 1/c to evaluate its quality of service; for a service node cluster, a queuing network model is built to evaluate its quality of service.
The method comprises the following steps of:
according to the processing capacity mu and capacity c of the service S on the virtual unit vu obtained in the step S2, respectively solvingBasic service node
Figure BDA0004064887770000101
Response time, throughput, error rate, and reliability of (c).
The specific calculation formula is as follows: basic service node
Figure BDA0004064887770000102
The response time of the service s deployment on the virtual unit vu under load λ is:
Figure BDA0004064887770000103
basic service node
Figure BDA0004064887770000104
I.e. the throughput of the service s deployed on the virtual unit vu under load λ is:
Figure BDA0004064887770000105
basic service node
Figure BDA0004064887770000106
I.e. the error rate of the service s deployment on the virtual unit vu under load λ is:
Figure BDA0004064887770000107
/>
basic service node
Figure BDA0004064887770000108
The reliability of the service s deployment on the virtual unit vu under load λ is:
Figure BDA0004064887770000109
M/H based k The 1/c queuing model construction steps are as follows:
according to the processing capacity, capacity and load of the service si e S on the virtual unit vu acquired in step S2, these are denoted as μi, ci and λi, respectively. Wherein,
Figure BDA00040648877700001010
representing a combined service node, s= { S1, S2, …, sk } is a service set, and vu is a virtual unit. Let->
Figure BDA0004064887770000111
The execution time of the virtual unit vu to all services obeys a k-order hyper-exponential distribution, and the distribution function is as follows:
Figure BDA0004064887770000112
each type of customer performance index in the model is then analyzed with emphasis. Model pair combined service node under load distribution delta= { λ1, λ2, …, λk })
Figure BDA0004064887770000113
And the quality of service for each service si e S. Wherein v= { s 0 ,s 1 ,…,s n ,s n+1 Is a collection of services for which i ∈V，λ i Representation s i Is a load of (a).
Figure BDA0004064887770000114
And->
Figure BDA0004064887770000115
Representing service nodes +.>
Figure BDA0004064887770000116
Average response time, throughput rate, error rate and reliability of all services in (a)>
Figure BDA0004064887770000117
Figure BDA0004064887770000118
And->
Figure BDA0004064887770000119
Representing services s respectively i At the service node->
Figure BDA00040648877700001110
Response time, throughput, error rate, and reliability.
As shown in fig. 2 and 3, the queuing-based network model construction steps are as follows:
step 3.3.1: queuing into a network model is divided into two layers: a control layer and a service layer.
Step 3.3.2: each service in the control node is modeled as a single service node, and the M/1/c model is used to evaluate its quality of service.
Step 3.3.3: the service nodes of the processing layer are evaluated.
Wherein,
Figure BDA00040648877700001111
represents a service node cluster, wherein s= { S 1 ,s 2 ,…,s k -a service set; VU= { VU 0 ,vu 1 ,…,vu m Is a set of virtual units, vu 0 Vu, a virtual unit of a control node j (j=1, …, m) is a virtual unit of a service node, m is the number of service nodes, and virtual units of different service nodes may be the same. Let the service set S be in the virtual unit vu j The load distribution on this is: Δj= { λ1j, λ2j, …, λkj }, λij (i=1, 2,., k) is service S i In virtual unit vu j Load on, service cluster->
Figure BDA0004064887770000121
The load profile of (a) = { λ1, λ2, …, λk }, +.>
Figure BDA0004064887770000122
The control layer is a description of the service request process inside the control node, and we will control the node
Figure BDA0004064887770000123
Each service s in (a) i (i=1, 2, …, k) is modeled as a single service node +.>
Figure BDA0004064887770000124
The M/M/1/c model is used to evaluate its quality of service.
The processing layer contains m service nodes, each of which is a basic service node if k=1, and otherwise a combined service node. The control layer is a description of the service request process inside the control node, and we will control the node
Figure BDA0004064887770000125
Each service s in (a) i (i=1, 2, …, k) is modeled as a single service node +.>
Figure BDA0004064887770000126
The M/M/1/c model is used to evaluate its quality of service. The processing layer contains m service nodes, each of which is a basic service node if k=1, and otherwise a combined service node. For basic service nodes, the M/M/1/c queuing model can be adopted to evaluate the service quality; the method comprises the steps of carrying out a first treatment on the surface of the For the combined service node, the M/H is adopted k The/1/c queuing model evaluates its quality of service.
Consider a control node
Figure BDA0004064887770000127
The specific calculation formula is as follows:
service s i (i=1, 2, …, k) at the serving node
Figure BDA0004064887770000128
The response time is as follows:
Figure BDA0004064887770000129
service s i (i=1, 2, …, k) at the serving node
Figure BDA00040648877700001210
The throughput rate is as follows:
Figure BDA0004064887770000131
service s i (i=1, 2, …, k) at the serving node
Figure BDA0004064887770000132
The error rate is as follows:
Figure BDA0004064887770000133
service s i (i=1, 2, …, k) at the serving node
Figure BDA0004064887770000134
The reliability is as follows:
Figure BDA0004064887770000135
wherein,
Figure BDA0004064887770000136
and->
Figure BDA0004064887770000137
Respectively represent control nodes->
Figure BDA0004064887770000138
Average response time, throughput rate, and error in processing service requestsRate and reliability; />
Figure BDA0004064887770000139
Figure BDA00040648877700001310
The service s is a service without considering the quality of service of the control node i At the service node->
Figure BDA00040648877700001311
Response time, throughput, error rate, and reliability.
Thus, the invention is based on the M/M/1/c queuing model, M/H k The three service quality evaluation models, namely the 1/c service quality evaluation model and the queuing network model, are respectively used for evaluating the quality evaluation of the basic service node, the combined service node and the service node cluster, can meet the cloud resource service quality evaluation under different conditions, and improve the cloud resource service management level and efficiency, thereby realizing timeliness and scientificity of the overall process supervision, management and control of the cloud resource service.
The following is an embodiment of a queuing theory-based cloud resource quality of service evaluation system provided by the embodiments of the present disclosure, where the system and the queuing theory-based cloud resource quality of service evaluation method of the foregoing embodiments belong to the same inventive concept, and details that are not described in detail in the queuing theory-based cloud resource quality of service evaluation system embodiment may refer to the foregoing queuing theory-based cloud resource quality of service evaluation method embodiment.
The system comprises: the system comprises an evaluation determination module, a node classification module and a service quality evaluation module;
the evaluation determination module is used for determining the processing capacity and the capacity of the service node relative to the service;
the node classification module is used for classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
the service quality evaluation module is used for evaluating the service quality by adopting a corresponding queuing model aiming at different service nodes and outputting an evaluation result.
The units and algorithm steps of each example described in the embodiments disclosed in the cloud resource service quality assessment method and system based on queuing theory can be implemented in electronic hardware, computer software or a combination of the two, and in order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functions in the above description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The present invention provides a flow chart of a queuing theory-based cloud resource quality of service assessment method, illustrating the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A cloud resource service quality assessment method based on queuing theory is characterized by comprising the following steps:
s1: determining processing capacity and capacity of the service node relative to the service;
s2: classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
s3: and aiming at different service nodes, adopting a corresponding queuing model to evaluate the service quality, and outputting an evaluation result.
2. The queuing theory-based cloud resource quality of service evaluation method according to claim 1, wherein in step S1, the service node is a virtual unit for deploying a service;
let s be a service, vu be a virtual unit, let
Figure FDA0004064887760000011
And->
Figure FDA0004064887760000012
The time spent and the memory consumed by the service s on each execution of the virtual unit vu are respectively;
acquiring time and memory data consumed by each execution of a service node by adopting a performance test method, and measuring the average value of the service node by adopting a mathematical statistics method;
the processing capacity of the service node is the average number of user service requests completed by the service node in unit time;
set random variable
Figure FDA0004064887760000013
Mean>
Figure FDA0004064887760000014
Wherein,
Figure FDA0004064887760000015
average number of times the service s is executed on the virtual unit vu per unit time;
the processing power of the service s on the virtual unit vu is defined as:
Figure FDA0004064887760000016
wherein ε vu The value of the processing capacity coefficient is related to the virtualization technology adopted by the virtual unit and the CPU architecture of the physical server;
the capacity of the service node is the maximum concurrent service request number which can be accepted by the service node;
the capacity of the service s on the virtual unit vu is defined as:
Figure FDA0004064887760000017
D vu maximum available memory for virtual unit vu, < ->
Figure FDA0004064887760000018
Is a random variable +.>
Figure FDA0004064887760000019
Average value of θ vu The capacity coefficient is a value related to the virtualization technology and the storage rack adopted by the virtual unit.
3. The queuing theory-based cloud resource quality of service assessment method of claim 1, wherein the method further comprises: the method comprises the steps of constructing a mapping relation between service system parameters and queuing system parameters, wherein the specific mapping relation among parameters in a service system is as follows:
{ load → average customer arrival rate, service node processing capability → average attendant processing rate, service node capacity → capacity of system (queue length+number of attendant), number of concurrent service requests → average queue length, response time → average stay rate, throughput → absolute throughput capability, reliability → relative throughput capability, error rate → loss rate }.
4. The method for evaluating the quality of service of cloud resources based on queuing theory according to claim 1, wherein the classification method of service nodes in step S2 includes dividing the service nodes into basic service nodes and combined service nodes according to the number of services included in the service nodes, wherein one basic service node includes only one service, and one combined service node includes a plurality of services.
5. The queuing theory-based cloud resource quality of service assessment method according to claim 1, wherein step S3 further comprises: aiming at basic service nodes, a single service window queuing model M/M/1/c with capacity constraint is established to evaluate service quality;
also for the combined service node, a multi-class customer single service window queuing model M/H with capacity constraint is established k 1/c to evaluate the quality of service;
a queuing network model is also built for the service node cluster to evaluate the quality of service.
6. The queuing theory based cloud resource quality of service assessment method of claim 5 wherein the means for assessing quality of service based on M/1/c comprises:
configuration of
Figure FDA0004064887760000021
A basic service node is represented, wherein s is a service, vu is a virtual unit, and the processing capacity and capacity of the service s on the virtual unit vu are respectively: mu (mu)And c;
basic service node
Figure FDA0004064887760000022
The response time of the service s deployment on the virtual unit vu under load λ is:
Figure FDA0004064887760000023
basic service node
Figure FDA0004064887760000024
I.e. the throughput of the service s deployed on the virtual unit vu under load λ is:
Figure FDA0004064887760000031
basic service node
Figure FDA0004064887760000032
I.e. the error rate of the service s deployment on the virtual unit vu under load λ is:
Figure FDA0004064887760000033
basic service node
Figure FDA0004064887760000034
The reliability of the service s deployment on the virtual unit vu under load λ is:
Figure FDA0004064887760000035
7. according toThe queuing theory-based cloud resource quality of service assessment method as claimed in claim 5, wherein M/H based k The manner of evaluating the quality of service/1/c includes:
configuration of
Figure FDA0004064887760000036
Represents a combined service node, wherein s= { S 1 ,s 2 ,…,s k -service set, vu is a virtual unit;
service s i The processing capacity, capacity and load of the e S on the virtual unit vu are respectively: mu (mu) i ,c i And lambda (lambda) i The method comprises the steps of carrying out a first treatment on the surface of the Order the
Figure FDA0004064887760000037
The execution time of the virtual unit vu to all services obeys a k-order hyper-exponential distribution, and the distribution function is as follows:
Figure FDA0004064887760000038
capacity constrained multi-class customer single service window queuing model M/H k 1/c, which emphasizes on analyzing each type of customer performance index in the model;
model pair is distributed in load delta= { lambda% 1 ,λ 2 ,…,λ k Combined service node under }
Figure FDA0004064887760000039
Average quality of service of (a) and each service s i Evaluating the service quality of the E S;
wherein v= { s 0 ,s 1 ,…,s n ,s n+1 Is a collection of services for which i ∈V，λ i Representation s i Is a load of (2);
Figure FDA00040648877600000310
and->
Figure FDA00040648877600000311
Representing service nodes +.>
Figure FDA00040648877600000312
Average response time, throughput rate, error rate and reliability of all services in (a)>
Figure FDA0004064887760000041
Figure FDA0004064887760000042
And->
Figure FDA0004064887760000043
Representing services s respectively i At the service node->
Figure FDA0004064887760000044
Response time, throughput, error rate, and reliability.
8. The queuing theory-based cloud resource quality of service assessment method of claim 5 wherein the manner in which the queuing network model is built to assess quality of service comprises:
configuration of
Figure FDA0004064887760000045
Represents a service node cluster, wherein s= { S 1 ,s 2 ,…, k -a service set; VU= { VU 0 ,vu 1 ,…, m Is a set of virtual units, vu 0 Vu, a virtual unit of a control node j (j=1, …, m) is a virtual unit of a service node, m is the number of service nodes, and virtual units of different service nodes may be the same;
let the service set S be in the virtual unit vu j The load distribution on this is: delta j ＝{λ 1j ,λ 2j ,…,λ kj }，λ ij (i=1, 2,., k) is service s i In virtual unit vu j Load on, service cluster
Figure FDA0004064887760000046
Is a load distribution of delta = { lambda 1 ,λ 2 ,…,λ k }，
Figure FDA0004064887760000047
9. The queuing theory based cloud resource quality of service assessment method of claim 8 wherein,
the queuing network model is divided into two layers: a control layer and a service layer;
the control layer is a description of the service request process inside the control node, and the control node is used for controlling the service request process
Figure FDA0004064887760000048
Each service s in (a) i (i=1, 2, …, k) is modeled as a single service node +.>
Figure FDA0004064887760000049
The M/M/1/c model is adopted to evaluate the service quality;
the processing layer comprises m service nodes, if k=1, each service node is a basic service node, otherwise, each service node is a combined service node;
for basic service nodes, the M/M/1/c queuing model is adopted to evaluate the service quality;
for the combined service node, the M/H is adopted k A/1/c queuing model to evaluate its quality of service;
if consider a control node
Figure FDA00040648877600000410
And (3) quality of service: service s i (i=1, 2, …, k) at service node +.>
Figure FDA00040648877600000411
The response time is as follows:
Figure FDA0004064887760000051
service s i (i=1, 2, …, k) at the serving node
Figure FDA0004064887760000052
The throughput rate is as follows:
Figure FDA0004064887760000053
service s i (i=1, 2, …, k) at the serving node
Figure FDA0004064887760000054
The error rate is as follows:
Figure FDA0004064887760000055
service s i (i=1, 2, …, k) at the serving node
Figure FDA0004064887760000056
The reliability is as follows:
Figure FDA0004064887760000057
wherein,
Figure FDA0004064887760000058
and->
Figure FDA0004064887760000059
Respectively represent control nodes->
Figure FDA00040648877600000510
Average response time, throughput, error rate, and reliability of processing service requests;
Figure FDA00040648877600000511
the service s is a service without considering the quality of service of the control node i At the service node->
Figure FDA00040648877600000512
Response time, throughput, error rate, and reliability.
10. A cloud resource service quality assessment system based on queuing theory, which is characterized in that the system adopts the cloud resource service quality assessment method based on queuing theory according to any one of claims 1 to 9;
the system comprises: the system comprises an evaluation determination module, a node classification module and a service quality evaluation module;
the evaluation determination module is used for determining the processing capacity and the capacity of the service node relative to the service;
the node classification module is used for classifying the service nodes into basic service nodes and combined service nodes according to the service quantity contained in the service nodes;
the service quality evaluation module is used for evaluating the service quality by adopting a corresponding queuing model aiming at different service nodes and outputting an evaluation result.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104038392A (en) * 2014-07-04 2014-09-10 云南电网公司 Method for evaluating service quality of cloud computing resources
CN104123189A (en) * 2014-06-30 2014-10-29 复旦大学 Web multilayer application dynamic resource adjustment method based on IaaS layer application perception
US20150039764A1 (en) * 2013-07-31 2015-02-05 Anton Beloglazov System, Method and Computer Program Product for Energy-Efficient and Service Level Agreement (SLA)-Based Management of Data Centers for Cloud Computing
CN105630575A (en) * 2015-12-23 2016-06-01 一兰云联科技股份有限公司 Performance evaluation method aiming at KVM virtualization server
US20160233682A1 (en) * 2013-09-30 2016-08-11 Jackseario Antonio Dionisio DO ROSARIO Power Quality of Service Optimization for Microgrids
CN107172710A (en) * 2017-04-23 2017-09-15 西安电子科技大学 A kind of resource allocation and service access control method based on virtual subnet
CN110753083A (en) * 2019-09-06 2020-02-04 江苏中云科技有限公司 Cloud service resource uniform distribution system for multiple service providers
CN113515351A (en) * 2021-09-07 2021-10-19 华南理工大学 Resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150039764A1 (en) * 2013-07-31 2015-02-05 Anton Beloglazov System, Method and Computer Program Product for Energy-Efficient and Service Level Agreement (SLA)-Based Management of Data Centers for Cloud Computing
US20160233682A1 (en) * 2013-09-30 2016-08-11 Jackseario Antonio Dionisio DO ROSARIO Power Quality of Service Optimization for Microgrids
CN104123189A (en) * 2014-06-30 2014-10-29 复旦大学 Web multilayer application dynamic resource adjustment method based on IaaS layer application perception
CN104038392A (en) * 2014-07-04 2014-09-10 云南电网公司 Method for evaluating service quality of cloud computing resources
CN105630575A (en) * 2015-12-23 2016-06-01 一兰云联科技股份有限公司 Performance evaluation method aiming at KVM virtualization server
CN107172710A (en) * 2017-04-23 2017-09-15 西安电子科技大学 A kind of resource allocation and service access control method based on virtual subnet
CN110753083A (en) * 2019-09-06 2020-02-04 江苏中云科技有限公司 Cloud service resource uniform distribution system for multiple service providers
CN113515351A (en) * 2021-09-07 2021-10-19 华南理工大学 Resource scheduling implementation method based on energy consumption and QoS (quality of service) cooperative optimization

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
G. YAN, F. MENG , D. CHU: "An Evaluation Methodology of Cloud Service Quality Based on Queuing Model", 2017 IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC), 15 December 2017 (2017-12-15), pages 925 - 930, XP033351126, DOI: 10.1109/ISPA/IUCC.2017.00142 *

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