CN104536894B - For the global optimization method based on maintenance cost of two layers of software aging phenomenon - Google Patents
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- 230000032683 aging Effects 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000012423 maintenance Methods 0.000 title claims abstract description 13
- 238000005457 optimization Methods 0.000 title claims abstract description 9
- 230000008929 regeneration Effects 0.000 claims abstract description 13
- 238000011069 regeneration method Methods 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims description 10
- 230000006399 behavior Effects 0.000 claims description 3
- 238000002474 experimental method Methods 0.000 description 9
- 238000012360 testing method Methods 0.000 description 9
- 230000001133 acceleration Effects 0.000 description 3
- 230000006378 damage Effects 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000003716 rejuvenation Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Abstract
The invention discloses the global optimization method based on maintenance cost for two layers of software aging phenomenon.Comprise the following steps：Monitor monitor application layer time interval (τ (i 1), τ i] in consumption internal memory XiWith operating system layer time interval (τ (i 1), τ i] in consumption internal memory Yi；Two layers of software aging analysis model is built based on renewal process；According to two layers of software aging analysis model, obtain optimum value, the optimum value of the fatigue threshold of operating system layer of the alarm threshold of operating system layer, and the alarm threshold of application layer optimum value；Obtain two layers of software system availability maximum.The present invention proposes the regeneration strategy method for two layers of software, can effectively improve overall availability.
Description
Technical field
The invention belongs to field of software performance test, more particularly to maintenance cost can be minimized, for two layers of software
The global optimization method based on maintenance cost of aging phenomenon.
Background technology
When software aging phenomenon is software long-play, software performance is caused due to the consumption of computer resource gradually
The phenomenon of decline.The consequence that this phenomenon is likely to result in is serious, and it not only influences the operation of common server software, and
Shadow may be caused to the software for requiring to use in the key area of high reliability, such as business, finance, science and technology and military affairs field
Ring, in the high software environment of security requirement, software aging phenomenon possibly even causes personnel's injury even fame loss.
For this phenomenon, scholar proposes a kind of method for being referred to as " software regeneration (Software Rejuvenation) ", that is,
By regularly restarting server software or whole computer system, the internal state of server is reinitialized, is released
The occupied system resource for being likely to result in aging is put so that the state and performance of software are restored, so as to avoid or reduce and be old
Change the serious hydraulic performance decline loss that even the software systems machine of delaying is caused caused.Among the application of software regeneration method, regeneration
The problem of determination of time interval is one extremely important, if regeneration intervals selection is long, may not avoid software
The harm that aging phenomenon is caused, if regeneration intervals selection is too short, initiative regeneration behavior sheet is brought as software systems
Expense may be excessive, causes the reduction of software efficiency of actual.Generally, software regeneration time interval can select one suitably
It is shorter than the value of software life expectancy, therefore, in prediction and the estimation always software aging field correlative study to software lifetime
A focus., can be fully abstract to the progress of actual software system for the probability distribution in Estimation Software life-span, utilize horse
The mathematical modelings such as Er Kefu models, Semi-Markov Process, stochastic reward net, stochastic Petri net are modeled to system, so that
Take out the feature of software lifetime；Real software systems can also be studied, are designed using the detection means of various performance parameters
Experiment, the various change data of acquisition system performance parameter, then utilize statistical method or artificial intelligence from real experiment
Energy algorithm etc., the life-span of description and forecasting software.However, carrying out test to real software system has a big obstacle, generally
In the case of server software be designed to uninterruptedly run offer service, even if there is aging phenomenon, the longevity of software
Life is also very long, is also just difficult to the sample that the software failure time is obtained by testing.For this phenomenon, scholar proposes a kind of title
For " accelerated ageing test (ADT) " and " accelerating lifetime testing (ALT) " technology, that is, it is theoretical using accelerating lifetime testing, it is logical
The lifetime data for taking software in the case of shorter accelerating lifetime testing is accelerated is crossed, software is true in the case of the non-acceleration of calculating
The real life-span.
Improved constantly with the complexity of system software and its running environment, the aging of system software is not simple only by answering
Caused with layer or operating system layer, any one layer of aging can all cause the collapse that whole service is serviced, and traditional test
Method is all limited only in one layer.The present invention builds two layers of software aging analysis model, and computing system aging threshold level utilizes biography
Wide variety of accelerating lifetime testing is theoretical in system industrial circle, disposes and implements the acceleration longevity for two layers of software aging phenomenon
Test experiments are ordered, the time interval of adjustment monitor monitoring internal memory change is calculated under each monitor monitoring time interval
The non-true lifetime for accelerating software and other specification, obtain the probability distribution of system software availability under normal circumstances.
Many experts and scholars are studied software aging, and for example Yun-Fei Jia et al. [2008] study web services
Aging phenomenon in device, they have built an experiment porch with an Apache Server and multiple client, run multigroup
Experiment, collects the relevant information of the system resource usage amount on server in every group of experiment, they observe software aging process
It is nonlinear and chaotic；Cotroneo et al. [2010] have studied linux kernel code, analyze workload parameters,
By be highlighted with the related subsystem of aging tendency, provide proof for the potential source of software aging.But they
Aging seldom for two layers of software is studied, but the availability of whole system not only relates only to application layer (operation
System layer) availability, it with it is above-mentioned both have close relationship, the failure of any one layer of software all can cause system to be not
It is available.
The content of the invention
Can minimize maintenance cost it is an object of the invention to provide a kind of, for two layers of software aging phenomenon based on
The global optimization method of maintenance cost.
The present invention is achieved by the following technical solutions：
For the global optimization method based on maintenance cost of two layers of software aging phenomenon, it is characterised in that including following
Several steps：
Step one：Record application layer can utilize internal memory A in initial healthtotalWith operating system layer in initial health
Internal memory O can be utilized during statetotal, monitor monitor application layer time interval (τ (i-1), τ i] in consumption internal memory Xi
With operating system layer time interval (τ (i-1), τ i] in consumption internal memory Yi, wherein τ is monitoring time interval, and i is prison
Survey total degree；
Step 2：Two layers of software aging analysis model is built based on renewal process：
Wherein, available internal memory in application layer when A (k τ) is monitor kth time monitoring, O (k τ) is monitor kth
Available internal memory in operating system layer during secondary monitoring；
Step 3：According to two layers of software aging analysis model, the alarm threshold O of operating system layer is obtainedredOptimum value,
The fatigue threshold O of operating system layerblueOptimum value, and application layer alarm threshold AredOptimum value；
The alarm threshold O of operating system layerredOptimum value be：
Wherein,For the alarm threshold O of operating system layerredOptimum value, the operating system before time (K+1) τ
The probability α of collapse is：
The fatigue threshold O of operating system layerblueOptimum value be：
Wherein,For the fatigue threshold O of operating system layerblueOptimum value,
The alarm threshold A of application layerredOptimum value be：
Wherein,For the alarm threshold A of application layerredOptimum value, before time (K+1) τ application layer collapse
Probability is：
Wherein, E (X) is XiDesired value, E (Y) is YiDesired value, cOpAnd cOrBe respectively operating system regenerate each time and by
The dynamic expense restarted, Δ and Δ ' it is respectively operating system regeneration and the time passively restarted, E (Δ) and E (Δ ') they are Δ respectively
With Δ ' desired value, cApAnd cArIt is the expense that application layer regenerates and passively restarted each time respectively, δ and δ ' are respectively application layer
Regeneration and the time passively restarted, E (δ) and E (δ ') are δ and δ ' desired value respectively.
Step 4：According to the alarm threshold O of operating system layerredOptimum value, the fatigue threshold O of operating system layerblue's
Optimum value, and application layer alarm threshold AredOptimum value, obtain two layers of software system availability maximum.
The present invention is directed to the global optimization method based on maintenance cost of two layers of software aging phenomenon, in addition to：
The method for obtaining two layers of software system availability maximum, including following steps,
1) state to application layer and operating system layer is divided, and works as Ared＜ A (K τ)≤AtotalWhen, at application layer
In safe condition；As 0 ＜ A (K τ)≤AredWhen, application layer is on the alert；As A (K τ)=0, application layer is in consumption
Most state；Work as Oblue＜ O (K τ)≤OtotalWhen, operating system layer is in a safe condition；Work as Ored＜ O (K τ)≤OblueWhen,
Operating system layer is in fatigue state；As 0 ＜ O (K τ)≤OredWhen, operating system layer is on the alert；When O (K τ)=
When 0, operating system layer is in spent condition；
2) try to achieve when application layer is on the alert or spent condition, and operating system layer is in the probability of fatigue state
pO1, and the now availability A of two layers of software system1For：
The Probability p being on the alert when operating system layerO2, and the now availability A of two layers of software system2For：
pO2=(1-pO1)(1-α)
When operating system layer reaches the Probability p of spent conditionO3, and the now availability A of two layers of software system3For：
pO3=(1-pO1)α
Wherein, tAM=β δ '+(1- β) δ is the average time for safeguarding an application layer,
3) the availability maximum for asking for two layers of software system is：
Availability=pO1·A1+pO2·A2+pO3·A3。
Beneficial effect：
The present invention from the memory refreshing Procedure Acquisition of two layers of software to some internal memory delta datas after can just construct two
The aging analysis model of layer software, can be in minimum with reference to the aging analysis model built and the definition of optimal threshold level
Change the threshold level for calculating the optimal aging of two layers of software on the basis of maintenance cost and regenerating, thus can be in very short time
Within effectively calculate the optimal availability size of whole system.The present invention application can be expanded to any one two
The system regions of layer software.
Brief description of the drawings
Fig. 1 is the execution schematic flow sheet of the present invention.
Fig. 2 is the renewal process model of two layers of software regeneration.
Fig. 3 is the application state based on A (t), O (t) and alarm threshold.
Embodiment
The present invention is described in further details below in conjunction with accompanying drawing.
The invention reside in the analysis model using system software update process and accelerated test are theoretical, two layers of software is tested
Lifetime data in the case of acceleration, derives two layers of aging can use under normal level by software aging phenomena impair
Property probability distribution so that draw minimize maintenance cost on the basis of system availability size.This purpose can be according to
Following steps realization, as shown in Figure 1：
The first step：Two layers of software aging analysis model is built based on renewal process.Software upgrading process refers to again initial
Change software inhouse state, discharge occupied memory source.
Second step：Define the threshold level of system mode.
3rd step：Analysis system availability.System availability refers to time that system software normally runs in whole system
Shared ratio in time.
The present invention enters to the web server software (application layer) of an e-commerce website and the operating system under it
Row system availability research.The process for maximizing system availability is described further with reference to other accompanying drawings.
1st, experiment porch is built, output parameter is obtained
1.1st, experiment porch is built：A module for carrying memory overflow bug is introduced in application layer, the module is in service
By random call during device normal process HTTP request, the probability of the module is called by control, memory overflow speed is brought it about
Differ, in operating system layer, a character device module is loaded, by write method to behaviour in the character device module
Make system application memory block not discharge, cause the memory overflow of operating system.When the module additionally introduced in application layer is adjusted
Used time, character device module is also called.
1.2nd, record application layer can utilize memory size A in initial healthtotalInitially it is being good for operating system layer
Memory size O can be utilized during health statetotal
1.3rd, running experiment, at the same with monitor monitor application layer time interval (τ (i-1), τ i] in consumption
Memory size XiWith monitor operating system layer time interval (τ (i-1), τ i] in consumption memory size Yi, then distinguish
It is saved in file tomcatMem.log and sysMeminfo.log, wherein τ is that monitor monitors time interval, and i is monitor
The total degree of monitoring.The run time entirely tested is a hour.
1.4th, X is calculatediAnd YiDesired value E (X), E (Y).
2nd, the A that will be obtained in the 1st steptotal、Ototal、XiAnd YiIt is updated to
With
In, obtain building two layers of software aging analysis model based on renewal process, see Fig. 2.Wherein, K monitors for monitor
Number of times, K τ be monitor kth monitor when time, A (K τ) be monitor kth monitor when application layer in it is available
Memory size, available memory size in operating system layer when O (K τ) is the monitoring of monitor kth.
3rd, the optimal threshold level of computing system state.Define AredFor the alarm threshold of application layer, OblueFor operating system
The fatigue threshold of layer, OredFor the alarm threshold of operating system layer.By the E (X) obtained in the 1st step and E (Y) and previous scholars
The empirical value c that research aging is obtainedOp、cOr、Δ、Δ'、cAp、cAr, δ and δ ' calculate O respectively with the equation belowred、Ared
And OblueOptimum value, wherein cOpAnd cOrIt is the expense that operating system regenerates and passively restarted each time respectively, Δ and Δ ' be respectively
Operating system regenerates and time for passively restarting, E (Δ) and E (Δ ') be respectively Δ and Δ ' desired value, cApAnd cArIt is respectively
Application layer regenerates and the expense passively restarted each time, and δ and δ ' are respectively application layer regeneration and the time passively restarted, E (δ) and
E (δ ') is δ and δ ' desired value respectively.
3.1、OredThe determination of optimum value
WhereinIt is in time (K+
1) probability that operating system is collapsed before τ.
3.2、AredThe determination of optimum value
Wherein,It is in time (K+
1) probability that application layer is collapsed before τ.
3.3、OblueThe determination of optimum value
Wherein, E (N) meets following expression formula：
4th, system availability size is determined
4.1st, α, β, O are obtainedred、AredAnd OblueOptimum value after, definition work as Ared＜ A (K τ)≤AtotalShi Yingyong
Layer is in a safe condition；As 0 ＜ A (K τ)≤AredWhen application layer be on the alert；As A (K τ)=0, application layer is in
Spent condition, works as Oblue＜ O (K τ)≤OtotalWhen operating system layer it is in a safe condition；Work as Ored＜ O (K τ)≤OblueWhen
Operating system layer is in fatigue state；As 0 ＜ O (K τ)≤OredWhen operating system layer be on the alert；As O (K τ)=0
When operating system layer be in spent condition.As shown in Figure 3.
4.2nd, calculate application layer or operating system layer is in the availability size of each shape probability of state and system.
4.2.1, when application layer be in warning or spent condition, and operating system be in fatigue state when Probability pO1And
The availability size A of system1Respectively
4.2.2, the Probability p when operating system is on the alertO2And the availability size A of system2Respectively
pO2=(1-pO1)(1-α)
4.3.3, the Probability p when operating system reaches spent conditionO3And the availability size A of system3Respectively
pO3=(1-pO1)α
Wherein, tAM=β δ '+(1- β) δ is the average time for safeguarding an application layer.
4.4, obtain pO1、A1、pO2、A2、pO3、A3Afterwards, it is updated in following formula, the availability for obtaining system is maximum
Value.
Availability=pO1·A1+pO2·A2+pO3·A3。
Claims (2)
1. for the global optimization method based on maintenance cost of two layers of software aging phenomenon, it is characterised in that including following several
Individual step：
Step one：Record application layer can utilize internal memory A in initial healthtotalWith operating system layer in initial health
When can utilize internal memory Ototal, monitor monitor application layer time interval (τ (i-1), τ i] in consumption internal memory XiAnd behaviour
Make system layer time interval (τ (i-1), τ i] in consumption internal memory Yi, wherein τ is monitoring time interval, and i is total for monitoring
Number of times；
Step 2：Two layers of software aging analysis model is built based on renewal process：
Wherein, available internal memory in application layer when A (K τ) is the monitoring of monitor kth, O (K τ) is that monitor kth is supervised
Available internal memory in operating system layer during survey；
Step 3：According to two layers of software aging analysis model, the alarm threshold O of operating system layer is obtainedredOptimum value, operation
The fatigue threshold O of system layerblueOptimum value, and application layer alarm threshold AredOptimum value；
The alarm threshold O of operating system layerredOptimum value be：
Wherein,For the alarm threshold O of operating system layerredOptimum value, before time (K+1) τ operating system collapse
Probability α be：
The fatigue threshold O of operating system layerblueOptimum value be：
Wherein,For the fatigue threshold O of operating system layerblueOptimum value,
The alarm threshold A of application layerredOptimum value be：
Wherein,For the alarm threshold A of application layerredOptimum value, before time (K+1) τ application layer collapse probability
For：
Wherein, E (X) is XiDesired value, E (Y) is YiDesired value, cOpAnd cOrIt is that operating system regenerates and passively weighed each time respectively
The expense opened, Δ and Δ ' be respectively operating system regeneration and time for passively restarting, E (Δ) and E (Δ ') are respectively Δ and Δ '
Desired value, cApAnd cArIt is the expense that application layer regenerates and passively restarted each time respectively, δ and δ ' are respectively application layer regeneration
The time passively restarted, E (δ) and E (δ ') are δ and δ ' desired value respectively；
Step 4：According to the alarm threshold O of operating system layerredOptimum value, the fatigue threshold O of operating system layerblueIt is optimal
Value, and application layer alarm threshold AredOptimum value, obtain two layers of software system availability maximum.
2. the global optimization method based on maintenance cost according to claim 1 for two layers of software aging phenomenon, its
It is characterised by：The described method for obtaining two layers of software system availability maximum, including following steps,
1) state to application layer and operating system layer is divided, and works as Ared＜ A (K τ)≤AtotalWhen, application layer is in peace
Total state；As 0 ＜ A (K τ)≤AredWhen, application layer is on the alert；As A (K τ)=0, application layer, which is in, exhausts shape
State；Work as Oblue＜ O (K τ)≤OtotalWhen, operating system layer is in a safe condition；Work as Ored＜ O (K τ)≤OblueWhen, operation
System layer is in fatigue state；As 0 ＜ O (K τ)≤OredWhen, operating system layer is on the alert；As O (K τ)=0,
Operating system layer is in spent condition；
2) try to achieve when application layer is on the alert or spent condition, and operating system layer is in the Probability p of fatigue stateO1,
And the now availability A of two layers of software system1For：
The Probability p being on the alert when operating system layerO2, and the now availability A of two layers of software system2For：
pO2=(1-pO1)(1-α)
When operating system layer reaches the Probability p of spent conditionO3, and the now availability A of two layers of software system3For：
pO3=(1-pO1)α
Wherein, tAM=β δ '+(1- β) δ is the average time for safeguarding an application layer,
3) the availability maximum for asking for two layers of software system is：
Availability=pO1·A1+pO2·A2+pO3·A3。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102385550A (en) * | 2010-08-30 | 2012-03-21 | 北京理工大学 | Detection method for software vulnerability |
| CN103383659A (en) * | 2013-07-11 | 2013-11-06 | 哈尔滨工程大学 | Software accelerating life test method |
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| CN102385550A (en) * | 2010-08-30 | 2012-03-21 | 北京理工大学 | Detection method for software vulnerability |
| CN103383659A (en) * | 2013-07-11 | 2013-11-06 | 哈尔滨工程大学 | Software accelerating life test method |
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| Title |
|---|
| 基于软件老化累积损伤模型的应用服务器再生策略研究;赵天海等;《系统仿真学报》;20060831;第18卷;全文 * |
| 软件老化的加速寿命测试研究;靳瑜亮;《中国优秀硕士学位论文全文数据库信息科技辑》;20140415(第4期);全文 * |
| 软件老化过程建模、预测及软件再生策略研究;闫雪梅等;《北京理工大学学报》;20070731;第27卷(第7期);全文 * |
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