CN111404743A - General evaluation system for cloud resource service capability - Google Patents

General evaluation system for cloud resource service capability Download PDF

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CN111404743A
CN111404743A CN202010174317.2A CN202010174317A CN111404743A CN 111404743 A CN111404743 A CN 111404743A CN 202010174317 A CN202010174317 A CN 202010174317A CN 111404743 A CN111404743 A CN 111404743A
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黄东
秦小林
刘竟成
陈震宇
林小光
杨涌
刘期烈
龙华
王小明
黄海松
唐飞
杨铮
于万钦
刘万平
吕健
刘苡村
唐奎
刘贲
王紫阳
冯涛
张伟
王万涛
徐涛
谭钧译
黄浩俭
陶汉君
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/04—Network management architectures or arrangements
    • H04L41/044—Network management architectures or arrangements comprising hierarchical management structures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • 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
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
    • 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
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
    • 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
    • H04L67/75—Indicating network or usage conditions on the user display

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Abstract

The invention provides a general evaluation system for cloud resource service capacity, which is realized by adopting a cloud resource service capacity evaluation criterion and establishing a cloud resource functional layer, and can realize refined cloud service capacity evaluation.

Description

General evaluation system for cloud resource service capability
Technical Field
The present invention relates to the field of communications technologies, and in particular, to service scheduling.
Background
Cloud computing is a service related to information technology, software and the internet, the computing resource sharing pool is called cloud, a plurality of computing resources are aggregated by the cloud computing, automatic management is achieved through software, and the resources can be rapidly provided only by few people. That is, the computing power as a commodity can be circulated on the internet, like water, electricity, and gas, can be conveniently used, and is low in price. The core concept of cloud computing is that internet is used as a center, fast and safe cloud computing services and data storage are provided on websites, and every person using the internet can use huge computing resources and data centers on the internet.
Cloud computing is a new innovation in the information era after the internet and computers, and is a great leap of the information era, the future era can be the cloud computing era, although the definition of the cloud computing is many at present, generally speaking, the cloud computing has many meanings, but in summary, the basic meanings of the cloud computing are consistent, namely, the cloud computing has strong expansibility and desirability, and can provide a brand new experience for users, and the core of the cloud computing is that many computer resources can be coordinated together, so that the users can obtain unlimited resources through the network, and meanwhile, the obtained resources are not limited by time and space.
In summary, the following steps: there is a need to provide an efficient evaluation method for cloud resource service capability.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: by adopting the cloud resource service capability evaluation criterion and establishing the cloud resource function layer, the cloud service capability evaluation can be refined.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
A. the method adopts a cloud resource service capability evaluation criterion, and specifically comprises the following steps: establishing a cloud resource service capability evaluation criterion, which comprises the following steps: performance efficiency, availability, continuity, reliability, user experience quality and followability; the performance efficiency is measured by full time domain link bandwidth efficiency C1 (link bandwidth accumulation value supported per watt and link bandwidth accumulation value on unit space), spatial distribution density C2 of network throughput, and degree of match C3 between network bandwidth and computation/storage capacity, as shown in fig. 1;
B. and establishing a cloud resource function layer as shown in figure 2.
In said step a, availability is measured by robustness C5 and accuracy C4 (accuracy of traffic scheduling and accuracy of calculation/storage); continuity is measured by burst margin C7 (describing the traffic burst strength, the maximum difference between the theoretical arrival time and the actual arrival time of a C8 packet when a traffic source bursts) and maximum burst length C6 (the maximum packet length that a traffic source can continuously transmit at a peak rate), sustainable rate C8 (the statistical average of the arrival time intervals of packets over a period of time is referred to as the average time interval of data transmission of the traffic source, wherein the inverse of the minimum average time interval is referred to as the sustainable packet rate C8, which is used to describe the average rate at which the traffic source transmits data over a period of time, i.e., the average rate of packet generation); reliability is measured by link redundancy C10, node redundancy C11, and data recovery capability C9; user experience quality through user behavior prediction C12 (through historical data analysis and judgment of the use requirements of the next user) and user prior matching C13 (degree of matching of the psychological expectation of the user to the system state and response to reality); the followability is measured by the consistency of application scenario and path selection C14, the consistency of application demand and traffic fractal prediction C15, and the response time margin C16 (the set of time intervals between an application request and a system response); the constraint relation among all judgment elements of the sub-criterion layer is as follows: the bandwidth efficiency of the full time domain link is affected by link redundancy; the spatial distribution density of network throughput is affected by node redundancy and link redundancy; the matching degree between the network bandwidth and the computing/storing capacity is influenced by the bandwidth efficiency of a full-time-domain link, the spatial distribution density of the network throughput and the burst tolerance; accuracy rate subject to maximum burst length, dataResilience and response time tolerance effects; robustness is affected by burst tolerance; the maximum burst length is affected by the bandwidth efficiency of the full time domain link; burst tolerance is affected by sustainable rate; data recovery capability is affected by burst tolerance; the prior matching of the user is influenced by the accuracy; the consistency of the application scene and the path selection is influenced by the spatial distribution density of the network throughput; the consistency of the application demand and the service fractal prediction is influenced by the user behavior prediction; the response time tolerance is affected by the maximum burst length, node redundancy and link redundancy; by using
Figure BDA0002410258620000031
A weight coefficient representing each sub-criterion, if any;
Figure BDA0002410258620000032
Figure BDA0002410258620000033
Figure BDA0002410258620000034
Figure BDA0002410258620000035
Figure BDA0002410258620000036
where C2' is the acceptable spatial distribution density of network throughput within tolerable interference, F (C10) is a mapping function between C10 and C16, F (C11) is a mapping function between C11 and C16, G (C6) is a mapping function between C6 and C16, G (C10) is a mapping function between C10 and C2, G (C11) is a mapping function between C11 and C2, F (C12) is a mapping function between C12 and C15, and F (C13) is a mapping function between C13 and C15; the weighting coefficients for each criterion are represented by phi (·), which is: i is1ω(B1)φ(B1)+I2ω(B2)φ(B2)+I3ω(B3)φ(B3)+I4ω(B4)φ(B4)+I5ω(B5)φ(B5)+I6Omega (B6) phi (B6) is an evaluation coefficient of cloud resource service capacity, phi (-) is a quantitative efficiency percentage of B1-B6, and omega (-) is an occurrence probability of B1-B6, and has
Figure BDA0002410258620000037
I＝{I1,I2,I3,I4,I5,I6Is a set of priority decision variables when
Figure BDA0002410258620000038
Figure BDA0002410258620000039
Figure BDA0002410258620000041
The higher the evaluation coefficient is, the stronger the cloud resource service capability is.
In the step B, the cloud resource function layer structure is divided into a physical resource layer, a logic resource layer and a virtual view layer, the physical resource layer is composed of a plurality of physical layer metadata and comprises storage metadata and data set metadata, the logic resource layer is composed of a plurality of logic layer metadata and comprises association type metadata and logic object metadata, the virtual view layer is composed of a plurality of view layer metadata, logical association exists between layers, the storage metadata comprises a system identification name, position information, a driver, a query language and security attributes, the data set metadata comprises a data set identification, mode mapping and an affiliated system, the logic object metadata comprises an object name, an attribute set 1, inter-object association and a callable data set, the attribute set 1 comprises transmission/calculation/storage performance attributes of the network, the association type metadata comprises full-time-domain link bandwidth efficiency, The method comprises the steps that spatial distribution density of network throughput, matching degree between network bandwidth and computing/storage capacity, different virtual view layers are defined for different user groups by a model of environment expression of an end user, the virtual view layers comprise view layer names, attribute sets 2 and attribute sets 3, the attribute sets 2 comprise list sets, view direction sets, view distance sets and visual area comparison sets, and the attribute sets 3 comprise user behavior prediction and user prior matching sets.
The invention has the beneficial effects that: a general evaluation system for cloud resource service capability is realized by adopting a cloud resource service capability evaluation criterion and establishing a cloud resource function layer, and refined cloud service capability evaluation can be realized.
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FIG. 1 is a schematic diagram of evaluation criteria of cloud services
FIG. 2 cloud resource functional layer diagram
Detailed Description
The invention is explained in more detail below with reference to the figures and examples:
in order to achieve the purpose, the technical scheme of the invention is as follows:
the method comprises the following steps of firstly, adopting a cloud resource service capability evaluation criterion, specifically: establishing a cloud resource service capability evaluation criterion, which comprises the following steps: performance efficiency, availability, continuity, reliability, user experience quality and followability; the performance efficiency is measured by full-time domain link bandwidth effectiveness C1 (link bandwidth accumulated value supported by each watt and link bandwidth accumulated value on unit space), spatial distribution density C2 of network throughput and matching degree C3 between network bandwidth and computing/storing capacity, and the availability is measured by robustness C5 and accuracy C4 (accuracy of service scheduling and accuracy of computing/storing); continuity is measured by burst margin C7 (describing the traffic burst strength, the maximum difference between the theoretical arrival time and the actual arrival time of a C8 packet when a traffic source bursts) and maximum burst length C6 (the maximum packet length that a traffic source can continuously transmit at a peak rate), sustainable rate C8 (the statistical average of the arrival time intervals of packets over a period of time is referred to as the average time interval of data transmission of the traffic source, wherein the inverse of the minimum average time interval is referred to as the sustainable packet rate C8, which is used to describe the average rate at which the traffic source transmits data over a period of time, i.e., the average rate of packet generation); reliability is measured by link redundancy C10, node redundancy C11, and data recovery capability C9; user experience quality through user behavior prediction C12 (through historical data analysis and judgment of the use requirements of the next user) and user prior matching C13 (degree of matching of the psychological expectation of the user to the system state and response to reality); the following performance is consistent with the path selection through an application scene C14, and the application requirements are consistent with the service fractal predictionSex C15 and response time tolerance C16 (the set of time intervals between application requests and system responses) are measured; the constraint relation among all judgment elements of the sub-criterion layer is as follows: the bandwidth efficiency of the full time domain link is affected by link redundancy; the spatial distribution density of network throughput is affected by node redundancy and link redundancy; the matching degree between the network bandwidth and the computing/storing capacity is influenced by the bandwidth efficiency of a full-time-domain link, the spatial distribution density of the network throughput and the burst tolerance; the accuracy is affected by the maximum burst length, data recovery capability and response time tolerance; robustness is affected by burst tolerance; the maximum burst length is affected by the bandwidth efficiency of the full time domain link; burst tolerance is affected by sustainable rate; data recovery capability is affected by burst tolerance; the prior matching of the user is influenced by the accuracy; the consistency of the application scene and the path selection is influenced by the spatial distribution density of the network throughput; the consistency of the application demand and the service fractal prediction is influenced by the user behavior prediction; the response time tolerance is affected by the maximum burst length, node redundancy and link redundancy; by using
Figure BDA0002410258620000061
A weight coefficient representing each sub-criterion, if any;
Figure BDA0002410258620000062
Figure BDA0002410258620000063
Figure BDA0002410258620000064
Figure BDA0002410258620000065
where C2' is the acceptable spatial distribution density of network throughput within tolerable interference, F (C10) is a mapping function between C10 and C16, F (C11) is a mapping function between C11 and C16, G (C6) is a mapping function between C6 and C16, G (C10) is a mapping function between C10 and C2, G (C11) is a mapping function between C11 and C2, F (C12) is a mapping function between C12 and C15, and F (C13) is a mapping function between C13 and C15; using phi (-) to represent the weight coefficients of the criteriaThe method comprises the following steps: i is1ω(B1)φ(B1)+I2ω(B2)φ(B2)+I3ω(B3)φ(B3)+I4ω(B4)φ(B4)+I5ω(B5)φ(B5)+I6Omega (B6) phi (B6) is an evaluation coefficient of cloud resource service capacity, phi (-) is a quantitative efficiency percentage of B1-B6, and omega (-) is an occurrence probability of B1-B6, and has
Figure BDA0002410258620000066
I＝{I1,I2,I3,I4,I5,I6Is a set of priority decision variables when
Figure BDA0002410258620000067
Figure BDA0002410258620000068
Figure BDA0002410258620000069
Figure BDA00024102586200000610
The higher the evaluation coefficient is, the stronger the cloud resource service capability is.
Secondly, the cloud resource function layer structure is divided into a physical resource layer, a logic resource layer and a virtual view layer, wherein the physical resource layer is composed of a plurality of physical layer metadata including storage metadata and data set metadata, the logic resource layer is composed of a plurality of logic layer metadata including association type metadata and logic object metadata, the virtual view layer is composed of a plurality of view layer metadata, logical association exists among layers, the storage metadata includes system identification names, position information, drivers, query languages and security attributes, the data set metadata includes data set identifications, mode mapping and affiliated systems, the logic object metadata includes object names, attribute sets 1, inter-object association and callable data sets, the attribute sets 1 include transmission/calculation/storage performance attributes of the network, the association type metadata includes full-time-domain link bandwidth efficiency, The method comprises the steps that spatial distribution density of network throughput, matching degree between network bandwidth and computing/storage capacity, different virtual view layers are defined for different user groups by a model of environment expression of an end user, the virtual view layers comprise view layer names, attribute sets 2 and attribute sets 3, the attribute sets 2 comprise list sets, view direction sets, view distance sets and visual area comparison sets, and the attribute sets 3 comprise user behavior prediction and user prior matching sets.
The invention provides a general evaluation system for cloud resource service capacity, which is realized by adopting a cloud resource service capacity evaluation criterion and establishing a cloud resource functional layer, and can realize refined cloud service capacity evaluation.

Claims (10)

1. A general evaluation system for cloud resource service capability is realized by adopting a cloud resource service capability evaluation criterion and establishing a cloud resource function layer, and can realize refined cloud service capability evaluation, and the evaluation system specifically comprises the following steps: establishing a cloud resource service capability evaluation criterion, which comprises the following steps: performance efficiency, availability, continuity, reliability, user experience quality and followability; the performance efficiency is measured by the full time domain link bandwidth efficiency C1 (link bandwidth accumulation value supported per watt and link bandwidth accumulation value per unit space), the spatial distribution density of network throughput C2, and the degree of match between network bandwidth and computing/storage capacity C3.
2. A method according to claim 1, characterized in that: availability is measured by robustness C5 and accuracy C4 (accuracy of traffic scheduling and accuracy of computation/storage); continuity is measured by burst margin C7 (describing the traffic burst strength, the maximum difference between the theoretical arrival time and the actual arrival time of a C8 packet when the traffic source bursts) and maximum burst length C6 (the maximum packet length that the traffic source can continuously transmit at the peak rate), sustainable data rate C8 (the statistical average of the arrival time intervals of packets over a period of time is referred to as the average time interval of the traffic source data transmission, wherein the inverse of the minimum average time interval is referred to as sustainable data rate C8, which describes the average rate at which the traffic source transmits data over a period of time, i.e., the average rate of packet generation).
3. A method according to claim 1, characterized in that: reliability is measured by link redundancy C10, node redundancy C11, and data recovery capability C9; user experience quality through user behavior prediction C12 (through historical data analysis and judgment of the use requirements of the next user) and user prior matching C13 (degree of matching of the psychological expectation of the user to the system state and response to reality); the followability is measured by the application scenario and path selection consistency C14, the application demand and traffic fractal prediction consistency C15, and the response time margin C16 (the set of time intervals between the application request and the system response).
4. A method according to claim 1, characterized in that: the constraint relation among all judgment elements of the sub-criterion layer is as follows: the bandwidth efficiency of the full time domain link is affected by link redundancy; the spatial distribution density of network throughput is affected by node redundancy and link redundancy; the matching degree between the network bandwidth and the computing/storing capacity is influenced by the bandwidth efficiency of a full-time-domain link, the spatial distribution density of the network throughput and the burst tolerance; the accuracy is affected by the maximum burst length, data recovery capability and response time tolerance; robustness is affected by burst tolerance; the maximum burst length is affected by the bandwidth efficiency of the full time domain link; burst tolerance is affected by sustainable rate; data recovery capability is affected by burst tolerance; the prior matching of the user is influenced by the accuracy; the consistency of the application scene and the path selection is influenced by the spatial distribution density of the network throughput; the consistency of the application demand and the service fractal prediction is influenced by the user behavior prediction; the response time tolerance is affected by the maximum burst length, node redundancy and link redundancy.
5. A method according to claim 1, characterized in that: by using
Figure FDA0002410258610000021
A weight coefficient representing each sub-criterion, if any;
Figure FDA0002410258610000022
Figure FDA0002410258610000023
Figure FDA0002410258610000024
Figure FDA0002410258610000025
where C2' is the acceptable spatial distribution density of network throughput within tolerable interference, F (C10) is the mapping function between C10 and C16, F (C11) is the mapping function between C11 and C16, G (C6) is the mapping function between C6 and C16, G (C10) is the mapping function between C10 and C2, G (C11) is the mapping function between C11 and C2, F (C12) is the mapping function between C12 and C15, and F (C13) is the mapping function between C13 and C15.
6. A method according to claim 1, characterized in that: the weighting coefficients for each criterion are represented by phi (·), which is: i is1ω(B1)φ(B1)+I2ω(B2)φ(B2)+I3ω(B3)φ(B3)+I4ω(B4)φ(B4)+I5ω(B5)φ(B5)+I6Omega (B6) phi (B6) is an evaluation coefficient of cloud resource service capacity, phi (-) is a quantitative efficiency percentage of B1-B6, and omega (-) is an occurrence probability of B1-B6, and has
Figure FDA0002410258610000026
I1,I2,I3,I4,I5,I6For the B1-B6 priority decision variables, respectively
Figure FDA0002410258610000027
Figure FDA0002410258610000028
Figure FDA0002410258610000031
Figure FDA0002410258610000032
The higher the evaluation coefficient is, the stronger the cloud resource service capability is.
7. The cloud resource function layer structure is divided into a physical resource layer, a logic resource layer and a virtual view layer, wherein the physical resource layer is composed of a plurality of physical layer metadata and comprises storage metadata and data set metadata, the logic resource layer is composed of a plurality of logic layer metadata and comprises association metadata and logic object metadata, the virtual view layer is composed of a plurality of view layer metadata, and logic association exists among the layers.
8. The method of claim 7, wherein: the storage metadata comprises a system identification name, position information, a driver, a query language and security attributes, the data set metadata comprises a data set identification, mode mapping and a system set, the mode mapping is a mapping rule of a physical resource layer and a logic resource layer about physical resources and virtual resources, and the system set is a device attribution set or an application attribution set corresponding to a single transmission device, a computing device and a storage device respectively.
9. The method of claim 7, wherein: the logic object metadata comprises an object name, an attribute set 1, an inter-object association and a callable data set, wherein the attribute set 1 comprises transmission/calculation/storage performance attributes of the network, and the association type metadata comprises full time domain link bandwidth efficiency, spatial distribution density of network throughput and matching degree between network bandwidth and calculation/storage capacity.
10. The method of claim 7, wherein: the virtual view layer defines different virtual view layers for different user groups by a model expressed by the environment of an end user, wherein the virtual view layers comprise view layer names, an attribute set 2 and an attribute set 3, the attribute set 2 comprises a list set, a view direction set, a view distance set and a visual area comparison set, and the attribute set 3 comprises a user behavior prediction set and a user prior matching set.
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Application publication date: 20200710