CN105404583B - The quick detection of APK a kind of and the method for improving unit resource utilization rate - Google Patents
The quick detection of APK a kind of and the method for improving unit resource utilization rate Download PDFInfo
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Abstract
A kind of APK of the present invention quick detection and the method for raising unit resource utilization rate are that the computer of operation virtual machine determines that APK to be detected takes the minimum value Φ of every resources of virtual machine1And determine that current slot is not detected the ratio Φ of APK priority attributes sum and detected APK priority attributes sum2, and using Lagrangian weighting method to Φ1And Φ2It is weighted processing and forms Φ values；Then APK optimal distributing schemes to be detected are determined using genetic algorithm, i.e., by being realized in genetic algorithm by adding fitness P, the fitness P is the Φ sums of all virtual machines, is achieved in paying close attention to the optimal of whole detecting system.The advantage of the invention is that：The present invention is the method for raising APK detection rates and resource utilization based on APK attributes Yu resources of virtual machine relation, is distributed using this method with detecting that faster, resource utilization is more effective for speed during APK.
Description
Technical field
The invention belongs to field of information security technology, more particularly to a kind of Android mobile applications (Android
Application package, APK) quick detection and improve detecting system unit resource utilization rate method.
Background technology
With the high speed development of mobile Internet, the types of applications based on android system is largely developed.It is same with this
When, occur in that numerous spam applications, malicious application etc. so that the safety problem that mobile terminal faces also increases therewith, causes out
Originator and user benefit are impaired.Therefore need to carry out safety detection to Android application program.
Existing detection method is generally comprised：Static Detection, dynamic detection and Viral diagnosis.Static Detection is in APK
Detected before performing；Dynamic detection is detected when APK is run；Viral diagnosis is to carry out Virus Sample knowledge for APK
Do not kill virus.The ability of detection rates and consumption detecting system resource needed for different detection methods is not quite similar.
The APK detections of cloud computing are currently based on, its step is mainly：First, APK file to be measured is uploaded；Secondly, according to upper
APK precedence is passed, is sequentially allocated in specified virtual machine and is detected.In face of a large amount of different sizes, type, different inspections
During the APK to be measured of survey demand, due to not accounting for the detection resource of reasonable distribution system, there will necessarily be certain detection effect
Rate is low and the low problem of resource utilization.
Therefore need a kind of quick detection of Android mobile applications and improve detecting system unit resource utilization rate
Method solve the problem of prior art is present.
The content of the invention
The purpose of the present invention is in view of the shortcomings of the prior art, to propose a kind of quick inspection of Android mobile applications
The method surveyed and improve unit resource utilization rate, this method effectively improves APK detection speed and detection resource utilization.
Technical scheme of the present invention concern be not which APK to be detected is assigned to it is optimal in some virtual machine, and
It is how all APK to be detected to be assigned in different virtual machines so that detecting system quick detection in an optimal manner
APK。
The technical thought of technical scheme of the present invention is：It is every that the computer of operation virtual machine determines that APK to be detected takes
The minimum value Φ of platform resources of virtual machine1And determine that current slot is not detected APK priority attributes sum and detected APK
The ratio Φ of priority attribute sum2, and using Lagrangian weighting method to Φ1And Φ2It is weighted processing and forms Φ values；So
Determine APK optimal distributing schemes to be detected using genetic algorithm afterwards, i.e., by genetic algorithm by adding fitness P come real
Existing, the fitness P is the Φ sums of all virtual machines, is achieved in paying close attention to the optimal of whole detecting system；In addition, in this hair
In the bright genetic algorithm used, realized by operations such as intersection, selections and optimum allocation is realized to whole detecting system, so as to reach
Detection resource utilization is improved to realize quick detection APK purpose.
Realize the specific as follows of technique effect of the present invention：
The quick detection of APK a kind of and the method for improving unit resource utilization rate:
Include the computer of an operation virtual machine；
The computer of the operation virtual machine obtains APK to be detected in batches from detection queue；
The computer of the operation virtual machine obtains APK attributes to be detected and resources of virtual machine attribute；
The COMPUTER DETECTION of the operation virtual machine simultaneously determines APK property values to be detected and resources of virtual machine property value；
The computer of the operation virtual machine determines the weight of the corresponding resources of virtual machine of APK attribute, and the weight is used
In influence degree of the attribute to resources of virtual machine for identifying APK；
The computer of the operation virtual machine is set up and calculates APK property values to be detected and resources of virtual machine property value letter
Number；
The computer of the operation virtual machine determines optimal APK allocative decisions using genetic algorithm；
APK to be detected is distributed to virtual machine by the computer of the operation virtual machine according to optimal APK allocative decisions.
Further, the APK attributes include：Code is answered in the size of APK file, the size of DEX file, APK file
Resource file complexity and APK detection priority in miscellaneous degree, APK file median surface complexity, APK file, wherein,
The size of the APK file refers to that APK file takes the size of memory space in itself, and unit is Mb；
The size of the DEX file refers to the size of the entitled .dex files of extension in APK file, unit Mb；
Code complexity refers to the relation of the directories deep of code and number of files in dex files, code in the APK file
It is (1,100) that complexity, which sets scope, APK file directories deep for (1,10], more than 10 in terms of 10, APK number of files (1,
1000], more than 1000 in terms of 1000；
APK file median surface complexity refers to the interface numbers of APK in the process of running and control used in interface
Relation, it is (1,100) that interface complexity, which refers to setting scope, APK interfaces sum for (1,100], it is single more than 100 in terms of 100
Individual interface control sum (1,10], more than 10 in terms of 10；
Resource file complexity refers in APK the level of catalogue and following catalogue and text under res catalogues in the APK file
Relation between number of packages, it is (1,100) that the complexity of the resource file, which sets scope, directories deep for (1,5], every grade of maximum
Catalogue number (1,10], the maximum number of files of each catalogue (1,100]；
The APK detections priority refers to the APK detection time starteds the latest to be met, maximum number of required resource etc.
Condition, including but not limited to above-mentioned condition, can dynamically be adjusted according to actual conditions；
The resources of virtual machine attribute includes CPU indexes, memory index and thread index, wherein,
The CPU indexes refer to the ratio for the original usable resource of resource and CPU that CPU can currently be used；
The memory index refers to the ratio of internal memory current idle resource and the original usable resource of internal memory,
The thread index refers to the idle number of current hyperthread thread and original line number of passes.
Further, the APK property values include the size attribute value, the size attribute value of DEX file, APK of APK file
Resource file complexity attributes in code complexity attributes value, APK file median surface complexity attributes value, APK file in file
Value and APK detect priority attribute value, wherein,
The ratio between the size attribute value of APK file, specially APK actual sizes and detectable APK file maximum are multiplied by
100；
DEX actual size and detectable DEX file in the size attribute value of DEX file, specially APK in APK file
The ratio between maximum be multiplied by 100；
Code complexity attributes value in APK file, specially the directories deep of code is multiplied by detectable DEX in DEX file
After the maximum 30 of file divided by maximum directories deep 10 and it is multiplied by plus total number of files in DEX file after 70 divided by file is maximum
Number 1000 is obtained；
Total interface number of APK file median surface complexity attributes value, specially APK file is multiplied by single interface control most
Most divided by setting interface maximum number and the product of the maximum number of control in interface, then obtain APK file median surface multiplied by 100
Complexity attributes value；
Resource file complexity attributes value in APK file, specially in APK file under Res files physical directory depth with
Directories deep maximum under the product of the maximum number of physical directory maximum number and physical directory file divided by the Res files of setting with
The product of maximum number of files in catalogue number maximum and catalogue, then obtains resource file complexity category in PK files multiplied by with 100
Property value；
APK detects priority attribute value, and specially the time to be checked 10 is subtracted after setting detection time divided by 10 the latest the latest
It is multiplied by 100 determinations；
The resources of virtual machine property value includes CPU exponential quantities, memory index value and thread exponential quantity, wherein,
The CPU exponential quantities are specially the product that CPU core calculation is not used by rate with CPU frequency and current CPU；
The memory index value is specially the product that CPU memory sizes and current memory are not used by rate；
The thread exponential quantity is specially the product that Thread Count and current thread are not used by rate.
The weight determination mode of the corresponding resources of virtual machine of the APK attributes is：
Each APK has some attributes, and each virtual machine possesses limited resource, and APK attribute determines it in void
Allocation result in plan machine；
If influenceing the attribute of APK distribution as follows：
X1, X2..., Xt..., XT
The surplus resources species of each virtual machine is as follows：
R1, R2..., Rt..., RT
Because certain resource of virtual machine and APK some attributes are associated, so that the APK method of salary distribution is determined, it is described
The expression formula of association is：
RS=us1X1+us2X2+…+ustXt…+usTXT
Wherein,
S=1,2,3,4,5,6
T=1,2,3,4,5,6
ustRelation between resource s and attribute t is represented, 0≤u need to be met simultaneouslyst≤ 1 and us1+us2+…+ust=1 two bar
Part；RsRepresent that some APK is assigned to virtual machine S class resources shared after virtual machine.
The computer of the operation virtual machine is set up and calculates APK property values to be detected and resources of virtual machine attribute value function
Mode be：
By N number of APK (APKn| 1≤n≤N) it is assigned to M virtual machine (Vm| 1≤m≤M) on, i.e., N number of APK is divided into M
Cluster, each candidate cluster is designated as C 'm；It meets following constraints：
Resource sum needed for the APK being put into each virtual machine is not more than the resource that the virtual machine is actually possessed, and is：
Xt∈{X1, X2..., XTAnd ust≠0
Need to meet thereon：
Optimization aim one：The resource of every virtual machine is taken most possibly
Xt∈{X1, X2..., XTAnd ust≠ 0,
Wherein：M represents virtual machine number, and s represents resource category
Optimization aim two：For priority attribute, the higher APK of property value is preferentially distributed
Wherein：I represents not selected APK, the selected APK of j tables
Two above optimization aim is subjected to Lagrangian weighting, integrated objective function as follows can be obtained：
Φ=δ Φ1+（1-δ）Φ2, wherein, (0≤δ≤1).
The computer of the operation virtual machine determines concretely comprising the following steps for optimal APK allocative decisions using genetic algorithm：
Step one：Individual UVR exposure is generated
Using binary coding, a special coded identification "×" is introduced in addition and represents to forbid gene, forbids gene institute
Corresponding virtual machine is mismatched with the APK performances or virtual machine is in busy condition, it is impossible to which receive an assignment distribution；In initialization
When, forbid gene to be allocated coding "×", and all without change in genetic manipulation below, i.e., do not consider that distribution is corresponding
APK gives the virtual machine；Therefore, the gene on chromosome has " 0 ", " 1 ", three kinds of codings of "×", if the gene of μ rows τ row isThen significance of which is as follows：
Step 2：The generation of initial population
Because genetic algorithm is operated to colony, the data of initial population are determined；
Rapid three：Carry out fitness calculating
The quality of whole allocative decision is evaluated with the fitness size of all virtual machine sums in genetic algorithm, so that certainly
The size of fixed heredity chance；
The fitness is used(i represents the number of virtual machine) measures,More big selected probability
It is bigger；
Step 4：It is specifically chosen
Constitute follow-on individual using roulette method choice, any individual selected probability be equal to its adaptive value with
The ratio of all chromosome adaptive value sums, in this selection course, elite retention strategy is used, to ensure the optimal of acquisition
Individual is not destroyed by further genetic manipulation, so as to more likely converge to globally optimal solution；In operations such as intersection, variations
Afterwards, if the adaptive value of optimum individual is less than the adaptive value of optimum individual in previous generation in a new generation, previous generation's is optimal
Individual is replicated, and replaces the worst individual in a new generation；
Step 5：Cross selection
In order to ensure filial generation dyeing always meets constraints, make the feasible solution of child chromosome always problem, two fathers
A row or column in generation dyeing volume matrix is with certain crossover probability PcExchanged, need to be in father's chromosome P after the completion of intersection1、P2
With new chromosome C1、C2Between selected, adaptive value it is higher enter of future generation, be specially：
O1=max { fitne ss(P1), fitne ss(C1), Q2=max { fitness(P2), fitness(C2)}
Step 6：Variation
Mutation operator is that the genic value on some or certain some genes to individual is changed by a certain less probability
Become, it is also a kind of operating method for producing new individual.We use the method that position makes a variation in this model, i.e., from N rows, in M row
Randomly generate two random numbers p and q, then when following two condition is met, the gene that pth row q is arrangedIt is changed into 1 from 0
Or it is changed into 0 from 1, and following two conditions need to be met：Condition 1 is geneIt is not to forbid gene, condition 2 is
Above-mentioned two condition is met simultaneously just can enter row variation.
The advantage of the invention is that：The present invention is the raising APK detection rates based on APK attributes Yu resources of virtual machine relation
With the method for resource utilization, when detecting APK using this method, we are only needed to APK attributes to be detected and resources of virtual machine
Model attributes, calculate the relation function of APK attributes and resources of virtual machine attribute, then by genetic algorithm, calculate APK dispatching party
Case, distributes to virtual machine by APK according to scheduling scheme and is detected.Due to the issue in current APK and renewal speed faster, institute
To be distributed using this method with detecting that faster, resource utilization is more effective for speed during APK.
Brief description of the drawings
The initial population body surface schematic diagram data that genetic algorithm is operated to colony in Fig. 1 technical schemes of the present invention；
A line (or row) in Fig. 2 technical schemes of the present invention in two parent chromosome matrixes of genetic algorithm is with one
Determine crossover probability PcBy exchange schematic diagram；
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, hereafter technical solution of the present invention is made into
One step is described in detail.It should be noted that in the case where not conflicting, feature in embodiments herein and embodiment can be with
Arbitrarily it is mutually combined.
The APK attributes that the present invention is referred to describe in detail as follows：
(1) for the size of APK file, X is used1Represent
The size of APK file refers to that file takes the size of memory space in itself, and unit is Mb.
Common, the composition of APK file includes：META-INF, the certificate signature of its essential record file；Res, its lead
It is used for the catalogue for depositing resource file；AndroidManifest.xml, it is mainly used in program global configuration file；
Classes.dex, it is mainly used in depositing Dalvik bytecodes；Resources.arsc, it is mainly used in after storage compiling
Binary Resources file.
Current detectable APK file size between (0,300) Mb, and major part APK size (5,50) Mb it
Between.Test result shows that file is bigger, and required detection resource is bigger.
X1Value be equal to APK actual sizes divided by detectable APK file maximum 300 multiplied by with 100, retain two small
Number, formula is as follows：
Example：APK actual size is 34.5Mb, is specifically calculated as follows：
X1=34.5 300 × 100=11.5 of ÷
(2) for the size of dex files in APK file, X is used2Represent
The size of Dex files refers to the size of the entitled .dex files of extension in APK file, unit Mb.
The size of Dex files between (0,30Mb), and major part dex files size between (1,10Mb),
.dex file is bigger, illustrates that size of code is bigger, service logic is more complicated.
X2Value be equal to Dex actual sizes divided by detectable Dex files in APK maximum 30 multiplied by with 100, retain two
Position decimal, formula is as follows：
Example：Dex actual size is 4.5Mb, is specifically calculated as follows：
X2=4.5 30 × 100=15 of ÷
(3) code complexity in APK file, uses X3Represent
Code complexity refers to the relation of the directories deep of code and number of files in dex files.
Code complexity set scope be (1,100), directories deep for (1,10], more than 10 in terms of 10, the file of class
Number (1,1000], more than 1000 in terms of 1000.
X3The depth value of the bag such as value be multiplied by 30 except 10 again plus the sum of class is multiplied by 70 and removes 1000, retain two-decimal, it is public
Formula is as follows：
Example：The depth of bag is 7, and the sum of class is 452, substitutes into formula specific as follows：
X3The ÷ 1000=62.64 of=7 × 30 ÷ 10+452 × 70
(4) APK file median surface complexity, uses X4Represent
Interface complexity refers to the relation of the interface numbers of APK in the process of running and control used in interface.
Interface complexity refer to setting scope be (1,100), interface sum for (1,100], it is single more than 100 in terms of 100
Interface control sum (1,10], more than 10 in terms of 10.
The interface number of the bag such as value be multiplied by the interface maximum number 100 of the most most of single interface control divided by setting, interface
The product of the maximum number 10 of middle control, multiplied by 100, retains two-decimal, formula is as follows：
Example：The interface number of bag is 36, in interface control at most be 8, substitute into formula specific as follows:
X4=36 × 8 ÷ (100 × 10) × 100=28.8
(5) resource file complexity in APK file, uses X5Represent
Resource file complexity is referred in APK under res catalogues between the level of catalogue and following catalogue and number of files
Relation.
It is (1,100) that the complexity of resource file, which sets scope, directories deep for (1,5], every grade of maximum catalogue number (1,
10], each catalogue maximum number of files (1,100].(according to circumstances sustained improvement)
Value be equal to APK in Res physical directories depth, physical directory maximum number, the product of the maximum number of physical directory file
Divided by the Res directories deep maximum 5 of setting, catalogue number maximum 10, catalogue maximum number of files 100 product, multiplied by with
100 retain two-decimal, and formula is as follows：
Example：Res physical directory depth is 3, and maximum catalogue number is 8, and maximum number of files is 30, is specifically calculated as follows：
X5=(3 × 8 × 20) ÷ (5 × 10 × 100) × 100=9.6
(6) APK detects priority, uses X6Represent
Priority refers to the APK detection time starteds the latest to be met, the condition such as maximum number of required resource, including
But it is not limited to above-mentioned condition.
The scope (1,100) set at present, the time detected the latest in units of hour, retains two small multiplied by with 100
Number, formula is as follows：
Example：The latest detection time be 8 hours 30 points, be specifically calculated as follows：
X6=((10-8) × 60-30) ÷ (10 × 60) × 100=15
The source attribute values for the virtual machine that the present invention is referred to are specific as follows：
(1) CPU indexes, with representing
CPU indexes refer to the ratio for the original usable resource of resource and CPU that CPU can currently be used, calculation formula
CPU core calculation × CPU frequency × (the currently used rates of 1-CPU).
(2) memory index, with representing
Memory index refers to the ratio of internal memory current idle resource and the original usable resource of internal memory, calculation formula CPU
Memory size × (the currently used rate of 1- internal memories).
(3) thread index, with representing
Thread index refers to the idle number of current hyperthread thread and original line number of passes, calculation formula Thread Count × (1-
The currently used rate of thread).
Attribute according to the APK of above-mentioned analysis obtains detecting the unit of account of resource needed for one, further according to detection resource
Corresponding attribute itself, passes through following algorithm a to value.
Each APK has some attributes, and each virtual machine possesses limited resource, and APK attribute determines it in void
Allocation result in plan machine；
If influenceing the attribute of APK distribution as follows：
X1, X2..., Xt..., XT
The surplus resources species of each virtual machine is as follows：
R1, R2..., Rt..., RT
Because certain resource of virtual machine may be associated with APK some attributes, so that the APK method of salary distribution is determined,
This association is expressed as follows by we with mathematical form：
Rs=us1X1+us2X2+…+ustXT…+usTXT
Wherein, the implication of each parameter is：
S=1,2 ..., S
T=1,2 ..., T
ustRelation between resource s and attribute t is represented, 0≤u need to be met simultaneouslyst≤ 1 and us1+us2+…+ust=1 two bar
Part；RsRepresent that some APK is assigned to virtual machine S class resources shared after virtual machine.
APK attributes are analyzed as follows with the concrete example that associates of resources of virtual machine：
Certain APK will carry out Static Detection, and APK each attribute description is as follows：
File size is 25.7Mb；
Wherein dex file sizes are 1.3Mb；
Maximum directories deep is 7 grades in dex files, and total files are 358；
Possessed interface number is 26, most in interface to be integrated with 9 controls；
Maximum directories deep is that most catalogue numbers under 4 grades, single catalogue is 7 in source file catalogue, under single catalogue
Most number of files is 84；
According to above-mentioned attribute description, attribute is substituted into Rs=us1X1+us2X2+…+ustXt…+usTXTFollowing public affairs can be obtained
Formula：
R1=u11X1+u12X2+u13X3+u14X4+u15X5
APK attributes and result of calculation：
X1=8.6, X2=4.3, X3=46, X4=23.4, X5=47
APK attributes and resources of virtual machine incidence relation：
u11=0.15, u12=0.35, u13=0.35, u14=0.05, u15=0.1
Each result above is substituted into formula and obtains following result：
R1=0.15 × 8.6+0.35 × 4.3+0.35 × 46+0.05 × 23.4+0.1 × 47=24.76.
Now by N number of APK { APKn| 1≤n≤N } it is assigned to M virtual machine { Vm| 1≤m≤M } on, i.e., N number of APK is divided into M
Individual cluster, each candidate cluster is designated as C 'm；It meets following constraints：
Resource sum needed for the APK being put into each virtual machine is not more than the resource that the virtual machine is actually possessed, and is：
Xi∈{X1, X2..., XTAnd ust≠0
Need to meet thereon：
Optimization aim one：The resource of every virtual machine is taken most possibly
Xt∈{X1, X2..., XTAnd ust≠ 0,
Wherein：M represents virtual machine number, and s represents resource category
Optimization aim two：For priority attribute, the higher APK of property value is preferentially distributed
Wherein：I represents not selected APK, the selected APK of j tables
Two above optimization aim is subjected to Lagrangian weighting, integrated objective function as follows can be obtained：
Φ=δ Φ1+(1-δ)Φ2, wherein, (0≤δ≤1).
Based on above-mentioned analysis, optimal distributing scheme is found using genetic algorithm, following steps are specifically included：
Step one：Individual UVR exposure is generated
Using binary coding, a special coded identification "×" is introduced in addition and represents to forbid gene, forbids gene institute
Corresponding virtual machine is mismatched with the APK performances or virtual machine is in busy condition, it is impossible to which receive an assignment distribution.In initialization
When, forbid gene to be allocated coding "×", and all without change in genetic manipulation below, it is meant that do not consider distribution pair
The APK answered gives the virtual machine.Therefore, the gene on chromosome has " 0 ", " 1 ", three kinds of codings of "×", if the gene of row row
For then significance of which is as follows：
Step 2：The generation of initial population
As described in Figure 1, genetic algorithm is operated to colony, its data given initial population；
Step 3：Carry out fitness calculating
The quality of whole allocative decision is evaluated with the fitness size of all virtual machine sums in genetic algorithm, so that certainly
The size of fixed heredity chance；Fitness is used in this model(i represents the number of virtual machine) measures,More
Selected probability is bigger greatly；
Step 4：It is specifically chosen
Constitute follow-on individual using roulette method choice, any individual selected probability be equal to its adaptive value with
The ratio of all chromosome adaptive value sums；In this selection course, elite retention strategy is used, to ensure the optimal of acquisition
Individual is not destroyed by further genetic manipulation, so as to more likely converge to globally optimal solution；In operations such as intersection, variations
Afterwards, if the adaptive value of optimum individual is less than the adaptive value of optimum individual in previous generation in a new generation, previous generation's is optimal
Individual is replicated, and replaces the worst individual in a new generation；For example, set parent chromosome population as：
P={ A1, X2..., Ai... Aj... As}
And the new population obtained after intersecting, making a variation is：
Q={ B1, B2... Bm... Bn... Bs}
Wherein, AiAnd Aj,BmAnd BnIt is the optimal and worst individual in parent population and new population respectively, if adaptive value
fitness(Bm) ＜ fitness(Ai), then sub- population recruitment of future generation is as follows：
O={ B1, B2... Bm... Ai... Bs}
Step 5：Cross selection
As shown in Fig. 2 in order to ensure filial generation dyeing always meets constraints, making the feasible of child chromosome always problem
A line (or row) in solution, two parent chromosome matrixes is with certain crossover probability PcExchanged；
, need to be in father's chromosome P after the completion of intersection1、P2With new chromosome C1、C2Between selected, adaptive value is higher to be entered
Enter the next generation：
Q1=max { fitness(P1), fitness(C1), Q2=max { fitness(P2), fitness(C2)}
Step 6：Variation
Mutation operator is that the genic value on some or certain some genes to individual is changed by a certain less probability
Become, it is also a kind of operating method for producing new individual.We use the method that position makes a variation in this model, i.e., from N rows, in M row
Randomly generate two random numbers p and q, then when following two condition is met, the gene that pth row q is arrangedIt is changed into from 0
1 or it is changed into 0 from 1, and following two conditions need to be met：Condition 1 is geneIt is not to forbid gene, condition 2 is
Above-mentioned two condition is met simultaneously just can enter row variation.
The present invention is described in detail above, used herein principle and embodiment of the embodiment to the application
It is set forth, the explanation of above example is only intended to help and understands the present processes and its core concept；Simultaneously for
Those of ordinary skill in the art, according to the thought of the application, have change in specific embodiments and applications
Place, in summary, this specification content should not be construed as the limitation to the application.
Claims (4)
1. the quick detection of APK a kind of and the method for improving unit resource utilization rate:
Include the computer of an operation virtual machine；
The computer of the operation virtual machine obtains APK to be detected in batches from detection queue；
The computer of the operation virtual machine obtains APK attributes to be detected and resources of virtual machine attribute；
The COMPUTER DETECTION of the operation virtual machine simultaneously determines APK property values to be detected and resources of virtual machine property value；
The computer of the operation virtual machine determines the weight of the corresponding resources of virtual machine of APK attribute, and the weight is used to mark
Know APK influence degree of the attribute to resources of virtual machine；
The computer of the operation virtual machine is set up and calculates APK property values to be detected and resources of virtual machine attribute value function, has
Body mode is：
By N number of APK { APKn| 1≤n≤N } it is assigned to M virtual machine { Vm| 1≤m≤M } on, i.e., N number of APK is divided into M cluster, often
Individual candidate cluster is designated as C'm；It meets following constraints：
Resource sum needed for the APK being put into each virtual machine is not more than the money that the virtual machine is actually possessed, and is：
Xt∈{X1,X2,…,XTAnd ust≠ 0,
Need to meet thereon：
Optimization aim one：The resource of every virtual machine is taken most possibly
Xt∈{X1,X2,…,XTAnd ust≠ 0,
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</munderover>
<msub>
<mi>&alpha;</mi>
<mi>t</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<msubsup>
<mi>R</mi>
<mi>s</mi>
<msub>
<mi>V</mi>
<mi>m</mi>
</msub>
</msubsup>
<mo>-</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mo>|</mo>
<mo>|</mo>
<msubsup>
<mi>C</mi>
<mi>m</mi>
<mo>&prime;</mo>
</msubsup>
<mo>|</mo>
<mo>|</mo>
</mrow>
</munderover>
<msubsup>
<mi>X</mi>
<mi>t</mi>
<mrow>
<msub>
<mi>APK</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</msubsup>
<mo>|</mo>
<mo>|</mo>
<mo>&RightArrow;</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
Wherein：M represents virtual machine number, and s represents resource category
Optimization aim two：For priority attribute, the higher APK of property value is preferentially distributed；
<mrow>
<msub>
<mi>&Phi;</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>i</mi>
</munder>
<msubsup>
<mi>X</mi>
<mi>p</mi>
<mrow>
<msub>
<mi>APK</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</msubsup>
</mrow>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>j</mi>
</munder>
<msubsup>
<mi>X</mi>
<mi>p</mi>
<mrow>
<msub>
<mi>APK</mi>
<mrow>
<mi>m</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&RightArrow;</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
Wherein：I represents not selected APK, the selected APK of j tables
Two above optimization aim is subjected to Lagrangian weighting, integrated objective function as follows can be obtained：
Φ=δ Φ1+(1-δ)Φ2Wherein, (0≤δ≤1)；
The computer of the operation virtual machine determines optimal APK allocative decisions using genetic algorithm, specific optimal APK allocative decisions
The step of be：
Step one：Individual UVR exposure is generated
Using binary coding, a special coded identification "×" is introduced in addition and represents to forbid gene, is forbidden corresponding to gene
Virtual machine and the APK performances mismatch or virtual machine is in busy condition, it is impossible to receive an assignment distribution；
In initialization, gene is forbidden to be allocated coding "×", and do not examined all without change in genetic manipulation below
The corresponding APK of distribution is considered to the virtual machine；Therefore, the gene on chromosome has " 0 ", " 1 ", three kinds of codings of "×", if theμRowτ
The gene of row isThen significance of which is as follows：
Step 2：The generation of initial population
Because genetic algorithm is operated to colony, the data of initial population are determined；
Rapid three：Carry out fitness calculating
The quality of whole allocative decision is evaluated with the fitness size of all virtual machine sums in genetic algorithm, so as to determine to lose
Pass the size of chance；
The fitness is usedMeasurement,More big selected probability is bigger, and wherein i represents virtual machine
Number；
Step 4：It is specifically chosen
Follow-on individual is constituted using roulette method choice, any individual selected probability is equal to its adaptive value with owning
The ratio of chromosome adaptive value sum, in this selection course, elite retention strategy is used, to ensure the optimum individual obtained
Do not destroyed by further genetic manipulation, so as to more likely converge to globally optimal solution；In operations such as intersection, variations
Afterwards, if the adaptive value of optimum individual is less than the adaptive value of optimum individual in previous generation, optimal of previous generation in a new generation
Body is replicated, and replaces the worst individual in a new generation；
Step 5：Cross selection
In order to ensure filial generation dyeing always meets constraints, make the feasible solution of child chromosome always problem, two parents dyes
A row or column in colour solid matrix is with certain crossover probability PcExchanged, need to be in father's chromosome P1, P2 and new after the completion of intersection
Selected between chromosome C1, C2, the higher entrance of adaptive value is of future generation, is specially：
O1=max { fitness(P1),fitness(C1)}
O2=max { fitness(P2),fitness(C2)}；
Step 6：Variation
From N rows, two random numbers p and q are randomly generated in M row, then when following two condition is met, pth row q is arranged
GeneIt is changed into 1 from 0 or is changed into 0 from 1, and following two conditions need to be met：Condition 1 is geneIt is not to forbid gene, bar
Part 2 isAbove-mentioned two condition is met simultaneously just can enter row variation；
APK to be detected is distributed to virtual machine by the computer of the operation virtual machine according to optimal APK allocative decisions.
2. a kind of APK as claimed in claim 1 quick detection and the method for improving unit resource utilization rate, its feature exist
In：The APK attributes include：In the size of APK file, the size of DEX file, APK file in code complexity, APK file
Resource file complexity and APK detection priority in interface complexity, APK file, wherein,
The size of the APK file refers to that APK file takes the size of memory space in itself, and unit is Mb；
The size of the DEX file refers to the size of the entitled .dex files of extension in APK file, unit Mb；
Code complexity refers to the relation of the directories deep of code and number of files in dex files in the APK file, and code is complicated
It is (1,100) that degree, which sets scope, APK file directories deep for (1,10], more than 10 in terms of 10, APK number of files (1,
1000], more than 1000 in terms of 1000；
APK file median surface complexity refers to the pass of the interface numbers of APK in the process of running and control used in interface
System, interface complexity refer to setting scope be (1,100), APK interfaces sum for (1,100], more than 100 in terms of 100, single boundary
Face control sum (1,10], more than 10 in terms of 10；
Resource file complexity refers in APK the level of catalogue and following catalogue and number of files under res catalogues in the APK file
Between relation, it is (1,100) that the complexity of the resource file, which sets scope, directories deep for (1,5], every grade of maximum catalogue
Number (1,10], the maximum number of files of each catalogue (1,100]；
APK detection priority refers to the APK detection time starteds the latest to be met, the bar such as maximum number of required resource
Part, including but not limited to above-mentioned condition, can dynamically be adjusted according to actual conditions；
The resources of virtual machine attribute includes CPU indexes, memory index and thread index, wherein,
The CPU indexes refer to the ratio for the original usable resource of resource and CPU that CPU can currently be used；
The memory index refers to the ratio of internal memory current idle resource and the original usable resource of internal memory,
The thread index refers to the idle number of current hyperthread thread and original line number of passes.
3. a kind of APK as claimed in claim 1 quick detection and the method for improving unit resource utilization rate, its feature exist
In：Code is complicated in size attribute value of the APK property values including APK file, the size attribute value of DEX file, APK file
Spend resource file complexity attributes value and APK detections in property value, APK file median surface complexity attributes value, APK file preferential
Level property value, wherein,
The ratio between the size attribute value of APK file, specially APK actual sizes and detectable APK file maximum are multiplied by 100；
DEX actual size and detectable DEX file be most in the size attribute value of DEX file, specially APK in APK file
The ratio between big value is multiplied by 100；
Code complexity attributes value in APK file, specially the directories deep of code is multiplied by detectable DEX file in DEX file
Maximum 30 after divided by maximum directories deep 10 and be multiplied by plus total number of files in DEX file after 70 divided by file maximum number
1000 obtain；
Total interface number of APK file median surface complexity attributes value, specially APK file is multiplied by the most most of single interface control
Divided by the product of the interface maximum number of setting and the maximum number of control in interface, then obtain APK file median surface multiplied by 100 complicated
Spend property value；
Resource file complexity attributes value in APK file, physical directory depth and reality under Res files specially in APK file
The product of the maximum number of catalogue maximum number and physical directory file divided by directories deep maximum and catalogue under the Res files of setting
The product of maximum number of files, then obtains resource file complexity attributes value in PK files multiplied by with 100 in number maximum and catalogue；
APK detect priority attribute value, be specially the latest the time to be checked 10 subtract the latest setting detection time divided by 10 after be multiplied by
100 determine；
The resources of virtual machine property value includes CPU exponential quantities, memory index value and thread exponential quantity, wherein,
The CPU exponential quantities are specially the product that CPU core calculation is not used by rate with CPU frequency and current CPU；
The memory index value is specially the product that CPU memory sizes and current memory are not used by rate；
The thread exponential quantity is specially the product that Thread Count and current thread are not used by rate.
4. a kind of APK as claimed in claim 1 quick detection and the method for improving unit resource utilization rate, its feature exist
In：The weight determination mode of the corresponding resources of virtual machine of the APK attributes is：
Each APK has some attributes, and each virtual machine possesses limited resource, and APK attribute determines it in virtual machine
In allocation result；
If influenceing the attribute of APK distribution as follows：
X1,X2,…,Xt,…,XT
The surplus resources species of each virtual machine is as follows：
R1,R2,…,Rs,…,RS
Because certain resource of virtual machine and APK some attributes are associated, so that the APK method of salary distribution is determined, the association
Expression formula be：
Rs=us1X1+us2X2+…+ustXt+…+usTXT
Wherein,
S=1,2,3,4,5,6
T=1,2,3,4,5,6
ustRelation between resource s and attribute t is represented, 0≤u need to be met simultaneouslyst≤ 1 and us1+us2+…+ust=1 two condition；
RsRepresent that some APK is assigned to virtual machine S class resources shared after virtual machine.
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