CN111445318B - Edge cache auction method for differentiated service of NVM - Google Patents
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Abstract
The invention discloses a differentiated service for NVMThe edge buffer auction method of (2) includes the steps of: s1: constructing an edge cache model, and S2: defining the content set of interest of user j as S j Wherein, the method comprises the steps of, wherein,defining a set of users interested in content i as Ω i The method comprises the steps of carrying out a first treatment on the surface of the S3: defining the requirement of the user on the content as a user estimated value, and constructing a joint probability density distribution function of the user estimated value by utilizing the user estimated value; s4: constructing a user profit objective function; s5: introducing differentiated service factors and NVM factors into an edge cache model, constructing a mathematical objective function of the benefits of the service provider, wherein ER is the benefits of the service provider; s6: the system in which the service provider is located is divided into two stages, and the maximized benefits of the service provider are solved. The invention builds the internet scene edge cache model by combining multiple factors, thereby realizing the maximization of the benefit of the service provider while meeting different requirements of users and considering the NVM abrasion cost.
Description
Technical Field
The invention relates to the technical field of edge cache, in particular to an edge cache auction method for differentiated services of an NVM (non-volatile memory).
Background
In the big data age, with the explosive growth of internet data, organizations have predicted that more than 500 million devices will be connected to the internet by 2020, and that internet data will also reach 44ZB, with 70% of the data needing to be processed in edge devices. In addition, a large number of internet users frequently request/acquire content from the cloud, which puts a great load pressure on the servers of the network service provider SP. In the network data transmission peak period of the big data background, because the cloud server bears huge load pressure, the traditional cloud computing technology is difficult to meet QoS and QoE.
Extensive research has shown that edge caching can effectively solve such problems. Edge caching still faces some challenges. First, the storage capacity of the edge device and the facing distress. Most of the existing edge devices are small-capacity, and although the capacity can be expanded by using NVM (non-volatile memory/storage), NVM has the disadvantages of asymmetric reading and writing and limited erasing times/service life. Second, diverse and varied user preferences will affect the effectiveness of the edge cache. The growing trend of internet data changes the user preference profile from one that follows ziff's law to a stretched exponential profile SED. User preference changes can affect content placement in the edge device, even exacerbating NVM wear, reducing NVM life. Third, in an actual internet application scenario, different internet users have different service class requirements, i.e. differentiated services. Content quality of service depends on the user's demand for content, and SPs need to provide different levels of quality of service to users of different demands.
Reference [1] discloses a method and a device for unloading an edge computing task based on a bidirectional auction mechanism, which are applied to a resource allocation server in an edge computing system and comprise the resource allocation server, a plurality of user equipment and a plurality of edge servers, so as to improve the utilization rate of computing resources of the edge servers. But it models the need for the edge server side to be known to the user device side, which is not desirable in an internet scenario, which does not take into account the privacy of the user. In addition, the method does not consider the application of the NVM with wide storage prospects to the edge server side, and does not consider the storage wear cost of the edge server side.
Reference [2] discloses an optimal auction method based on an edge cache scenario. In the Internet scene, considering the relation among the CP-SP-users, the SP acquires the content from the CP and caches the content, and the users acquire the content from the SP. In this approach, it is assumed that the user preferences are unknown, the CP-SP-user relationship is modeled, and the user is motivated to "talk" and cache the most profitable content in the SP to maximize the SP's revenue. But among the three CP-SP-users, the same content is delivered to different users with the same quality of service without considering the requirements of different users for different service levels of the same content, which is not preferable in the internet application scenario. In addition, the SP in this method does not consider the application of NVM having a wide storage prospect to the edge server side, nor the storage wear cost of the edge server side.
Therefore, a method for maximizing the benefit of the service provider is obtained based on researching an interconnection scene model of joint factors.
Disclosure of Invention
The invention provides an edge cache auction method for differentiated services of an NVM (non-volatile memory) in order to overcome the defect that the prior art cannot obtain the maximum benefits of service providers because of single elements of an Internet scene model.
The primary purpose of the invention is to solve the technical problems, and the technical scheme of the invention is as follows:
an edge cache auction method for a differentiated service of an NVM (non-volatile memory), comprising the following steps:
s1: constructing an edge cache model, wherein the edge cache model comprises the following steps: m content publishers, a service provider and n users, wherein m and n are positive integers, the content publishers are used for publishing videos to the service provider, and the users are used for requesting the videos from the service provider;
s2: defining the content set of interest of user j as S j Wherein, the method comprises the steps of, wherein,defining a set of users interested in content i as Ω i Wherein->Set->Wherein |S j The i represents the number of content that interest user j, |Ω i The i indicates the number of users interested in content i;
s3: defining the requirement of the user on the content as a user estimated value, and constructing a joint probability density distribution function of the user estimated value by utilizing the user estimated value;
s4: constructing a user profit objective function;
s5: introducing differentiated service factors and NVM factors into an edge cache model, constructing a mathematical objective function of the benefits of the service provider, wherein ER is the benefits of the service provider;
s6: the system in which the service provider is located is divided into two stages, and the maximized benefits of the service provider are solved.
In this embodiment, the user estimation in step S3 includes: virtual and real estimates, said virtual estimates being denoted τ= [ τ ] 1 ,τ 2 ,...,τ n ]The true estimate is noted as t= [ t ] 1 ,t 2 ,...,t n ]T and t are independent of each other, wherein t j In the intervalThe probability density distribution function on the upper is recorded as f j (t j ) The cumulative integral cloth function is marked as F j (t j ) The joint estimation interval of all users can be recorded as +.>The joint probability density distribution function can be described as +.>
In this scheme, the user's valuation is the user's demand for content.
In the scheme, the specific process of constructing the user benefit objective function in the step S4 is as follows: the content publisher publishes the content i to a service provider, which publishes the content i at a fixed price per unit r i Pay to content publisher when the service provider is transmitting quality θ k (θ k > 0, k=1,.,. K) transmitting content to K users, the service provider's transmission cost isWhere h is the content transmission cost calculation function, assuming user j receives the content from the service provider at set S j And the delivery quality thereof is theta, the unit satisfaction degree of the user j is theta t j The user benefit can then be recorded asWherein p is i (t) represents the duty ratio of content i in the storage capacity of the service provider (0.ltoreq.p) i ≤1)；
In this scenario, the mathematical objective function of the benefit of the service provider in step S5 is:
wherein the integral termPay sum for user, < >>Content costs paid to content publishers for service providers, +.>Wear cost for NVM in service provider, < > and +.>Delivery costs for delivering content to a user for a service provider; wherein x is j (t) represents the payment amount of user j, p i (t) represents the duty ratio of content i in the storage capacity of the service provider (0.ltoreq.p) i ≤1)，r i Representing the unit price of content i, eta is the NVM wear coefficient, eta increases with the NVM usage duration, and eta is initialized 1 ＝1，g i (t) represents the change amount of the current and last storage capacity ratio of the content i, c i Representing the content i unit wear cost, |Ω i I represents the number of users interested in content i, h (θ) is the SP service provider's delivery cost function at delivery quality θ;
the change amount g of the next-time-to-current storage capacity ratio of the content i i (t) is defined as:
g i (t)＝p i (x)-p i (t) (2)
where x is the distribution of the next user's actual valuations (the last collected user preferences by the service provider) and t is the distribution of the user's actual valuations (reflecting previous user preferences) that the current service provider system is running.
In the scheme, the system where the service provider is located is divided into two stages, and the concrete process for solving the maximum benefit of the service provider is as follows:
the initial operation stage of the system where the service provider is located, the operation stage mark is marked as T, T=0, and eta is set 1 ＝1,g i (t) =0, the objective function reduces to:
defining a virtual estimateOptimal content storage duty cycle p * And (t) solving to obtain:
solving for optimal user payment amountThe method comprises the following steps:
service provider uses different delivery quality theta according to different valuations of users j Delivering to the user; defining a content delivery cost function asThen the sum of service provider delivery costs is obtained as +.>According to S4, the profit of the user j is obtained by the solution:
the system where the service provider is located operates in the middle and later stages, the operation stage is marked with T=1, and the current valuation of the user is marked as T 1 The last collected user estimate is t 0 The definition takes into account NVM wear and costs incurred by content replacement in service providersWherein eta w Representing the wear state coefficient of NVM at t=w, let η be 0 =1, Δ is shown at p i (t 1 ) Not equal to 0 and p i (t 0 ) Content collection not equal to 0;
to reduce NVM wear costs, service providers need to suppress NVM wear times, reduce unnecessary cache replacement times, i.e., maximize equation (2), solving:
optimal user payment amount if content replacement occurs in the system where the service provider is locatedIs that
Wherein 1 (·) is an indication function, when j ε Ω Δ When 1 (·) =1, otherwise 1 (·) =0, the user benefit is updated to
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention builds the internet scene edge cache model by combining multiple factors, thereby realizing the maximization of the benefit of the service provider while meeting different requirements of users and considering the NVM abrasion cost.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Example 1
As shown in fig. 1, an edge cache auction method for NVM-oriented differentiated services includes the following steps:
s1: constructing an edge cache model, wherein the edge cache model comprises the following steps: m content publishers, which can be denoted as CP and service provider, and SP and n users, where m and n are positive integers, the content publishers are used for publishing videos to the service provider, and the users are used for requesting videos from the service provider;
s2: defining the content set of interest of user j as S j Wherein, the method comprises the steps of, wherein,defining a set of users interested in content i as Ω i Wherein->Set->Wherein, |S j The i represents the number of content that interest user j, |Ω i The i indicates the number of users interested in content i;
it should be noted that different users have different demands for the same content, and the same user also has different demands for the same content. From the service provider perspective, the greater the demand for content by the user and the more the user obtains the content from the service provider, the more the service provider can gain. To this end the invention defines the user's demand for content as a user estimate.
S3: defining the requirement of the user on the content as a user estimated value, and constructing a joint probability density distribution function of the user estimated value by utilizing the user estimated value;
the user estimation in step S3 includes: virtual and real estimates, said virtual estimates being denoted τ= [ τ ] 1 ,τ 2 ,...,τ n ]The true estimate is noted as t= [ t ] 1 ,t 2 ,...,t n ]T and t are independent of each other, wherein t j In the intervalThe probability density distribution function on the upper is recorded as f j (t j ) The cumulative integral cloth function is marked as F j (t j ) The joint estimation interval of all users can be recorded as +.>The joint probability density distribution function can be described as +.>
S4: constructing a user profit objective function;
step S4, constructing a userThe specific process of the income objective function is as follows: the content publisher publishes the content i to a service provider, which publishes the content i at a fixed price per unit r i Pay to content publisher when the service provider is transmitting quality θ k (θ k > 0, k=1,.,. K) transmitting content to K users, the service provider's transmission cost isWhere h is the content transmission cost calculation function, assuming user j receives the content from the service provider at set S j And the delivery quality thereof is theta, the unit satisfaction degree of the user j is theta t j The user benefit can be recorded as +.>Wherein p is i (t) represents the duty ratio of content i in SP storage capacity (0.ltoreq.p) i ≤1)；
S5: introducing differentiated service factors and NVM factors into an edge cache model, constructing a mathematical objective function of the benefits of the service provider, wherein ER is the benefits of the service provider;
the mathematical objective function of the benefit of the service provider in step S5 is:
wherein the integral termPay sum for user, < >>Content costs paid to content publishers for service providers, +.>Wear cost for NVM in service provider, < > and +.>Delivery costs for delivering content to a user for a service provider; wherein x is j (t) represents the payment amount of user j, p i (t) represents the duty ratio of content i in the storage capacity of the service provider (0.ltoreq.p) i ≤1)，r i Representing the unit price of content i, eta is the NVM wear coefficient, eta increases with the NVM usage duration, and eta is initialized 1 ＝1，g i (t) represents the change amount of the current and last storage capacity ratio of the content i, c i Representing the content i unit wear cost, |Ω i I represents the number of users interested in content i, h (θ) is the SP service provider's delivery cost function at delivery quality θ;
the change amount g of the next-time-to-current storage capacity ratio of the content i i (t) is defined as:
g i (t)＝p i (x)-p i (t) (2)
where x is the distribution of the next user's actual valuations (the last collected user preferences by the service provider) and t is the distribution of the user's actual valuations (reflecting previous user preferences) that the current service provider system is running.
S6: the system in which the service provider is located is divided into two stages, and the maximized benefits of the service provider are solved.
In the scheme, the system where the service provider is located is divided into two stages, and the concrete process for solving the maximum benefit of the service provider is as follows:
the initial operation stage of the system where the service provider is located, the operation stage mark is marked as T, T=0, and eta is set 1 ＝1,g i (t) =0, the objective function reduces to:
defining a virtual estimateOptimal content storage duty cycle p * And (t) solving to obtain:
solving for optimal user payment amountThe method comprises the following steps:
service provider uses different delivery quality theta according to different valuations of users j Delivering to the user; defining a content delivery cost function asThen the sum of service provider delivery costs is obtained as +.>According to S4, the profit of the user j is obtained by the solution:
the system where the service provider is located operates in the middle and later stages, the operation stage is marked with T=1, and the current valuation of the user is marked as T 1 The last collected user estimate is t 0 The definition takes into account NVM wear and costs incurred by content replacement in service providersWherein eta w Representing the wear state coefficient of NVM at t=w, let η be 0 =1, Δ is shown at p i (t 1 ) Not equal to 0 and p i (t 0 ) Content collection not equal to 0;
to reduce NVM wear costs, service providers need to suppress NVM wear times, reduce unnecessary cache replacement times, i.e., maximize equation (2), solving:
optimal user payment amount if content replacement occurs in the system where the service provider is locatedIs that
Wherein 1 (·) is an indication function, when j ε Ω Δ When 1 (·) =1, otherwise 1 (·) =0, the user benefit is updated to
The invention performs verification of the method through the following algorithm configuration, and the configuration parameters are shown in table 1:
TABLE 1
In order to conduct differentiated services for users, we have given high delivery quality (set θ j =2) gives low delivery quality (setting θ) for low-valued users j ＝1)。
Pairing algorithm10 4 Repeated simulation experiments prove that under three different user estimation distributions (uniform distribution Uni, exponential distribution Exp, uniform to exponential distribution Uni2 Exp), the simulation experiment shows that the simulation experiment is similar to document [2]]In comparison with our algorithm, we obtained a comparison document [2] in four important evaluation indexes (SP benefit ER, user benefit u, profit margin of user benefit, average wear number of NVM in SP)](noted as Baseline) better.
Reference to the literature
[1] An edge computing task unloading method and device based on a bi-directional auction mechanism, application number 201910789821.0.
[2]Cao,X.,Zhang,J.,Poor,H.V.:An optimal auction mechanism for mobile edge caching.In:2018IEEE 38th International Conference on Distributed Computing Systems(ICDCS).pp.388–399.IEEE(2018).
[3]Guo L,Tan E,Chen S,et al.The stretched exponential distribution of internet media access patterns[C]//Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing.2008:283-294.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (2)
1. The edge cache auction method for the differentiated service of the NVM is characterized by comprising the following steps:
s1: constructing an edge cache model, wherein the edge cache model comprises the following steps: m content publishers, a service provider and n users, wherein m and n are positive integers, the content publishers are used for publishing videos to the service provider, and the users are used for requesting videos from the service provider;
s2: defining the content set of interest of user j as S j Wherein, the method comprises the steps of, wherein,defining a set of users interested in content i as Ω i Wherein->Set->Wherein, |S j The i represents the number of content that interest user j, |Ω i The i indicates the number of users interested in content i;
s3: defining the requirement of the user on the content as a user estimated value, and constructing a joint probability density distribution function of the user estimated value by utilizing the user estimated value;
the user estimation in step S3 includes: virtual and real estimates, said virtual estimates being denoted τ= [ τ ] 1 ,τ 2 ,...,τ n ]The true estimate is noted as t= [ t ] 1 ,t 2 ,...,t n ]T and t are independent of each other, wherein t j In the intervalThe probability density distribution function on the upper is recorded as f j (t j ) The cumulative integral cloth function is marked as F j (t j ) The joint estimation interval of all users can be recorded as +.>The joint probability density distribution function can be described as +.>
The estimation of the user is that the user needs the content;
s4: constructing a user profit objective function;
the specific process of constructing the user benefit objective function in the step S4 is as follows: the content publisher publishes the content i to a service provider, which publishes the content i at a fixed price per unit r i Pay to content publisher when the service provider is transmitting quality θ k (θ k > 0, k=1,.,. K) transmitting content to K users, the service provider's transmission cost isWhere h is the content transmission cost calculation function, assuming user j receives the content from the service provider at set S j And the delivery quality thereof is theta, the unit satisfaction degree of the user j is theta t j The user benefit can be recorded as +.>
S5: introducing differentiated service factors and NVM factors into an edge cache model, constructing a mathematical objective function of the benefits of a service provider, wherein ER is the benefits of the service provider, and the NVM is nonvolatile memory/storage;
the mathematical objective function of the benefit of the service provider in step S5 is:
wherein the integral termPay sum for user, < >>Content costs paid to content publishers for service providers, +.>Cost of wear for NVM in service provider、/>Delivery costs for delivering content to a user for a service provider; wherein x is j (t) represents the payment amount of user j, pi (t) represents the duty ratio of content i in the storage capacity of the service provider (0.ltoreq.p) i ≤1)，r i Representing the unit price of content i, eta is the NVM wear coefficient, eta increases with the NVM usage duration, and eta is initialized 1 ＝1，g i (t) represents the change amount of the current and last storage capacity ratio of the content i, c i Representing the content i unit wear cost, |Ω i I represents the number of users interested in content i, h (θ) is the delivery cost function of the service provider at delivery quality θ;
the change amount g of the next-time-to-current storage capacity ratio of the content i i The definition of (t) is as follows:
g i (t)＝p i (x)-p i (t) (2)
wherein x is the distribution of the actual estimates of the next time the user is operating, and t is the distribution of the actual estimates of the current service provider system;
s6: the system in which the service provider is located is divided into two stages, and the maximized benefits of the service provider are solved.
2. The method for auction of edge cache for NVM-oriented differentiated services according to claim 1, wherein the system in which the service provider is located is divided into two stages, and the specific process for solving the maximized profit of the service provider is:
the initial operation stage of the system where the service provider is located, the operation stage mark is marked as T, T=0, and eta is set 1 ＝1,g i (t) =0, the objective function reduces to:
defining a virtual estimateOptimal content storage duty cycle p * And (t) solving to obtain:
solving for optimal user payment amountThe method comprises the following steps:
service provider uses different delivery quality theta according to different valuations of users j Delivering to the user; defining a content delivery cost function asThen the sum of service provider delivery costs is obtained as +.>According to S4, the profit of the user j is obtained by the solution:
the system where the service provider is located operates in the middle and later stages, the operation stage is marked with T=1, and the current valuation of the user is marked as T 1 The last collected user estimate is t 0 The definition takes into account NVM wear and costs incurred by content replacement in service providersWherein eta w Representing wear of NVM at t=wState coefficient, let eta 0 =1, Δ is shown at p i (t 1 ) Not equal to 0 and p i (t 0 ) Content collection not equal to 0;
to reduce NVM wear costs, service providers need to suppress NVM wear times, reduce unnecessary cache replacement times, i.e., maximize equation (2), solving:
optimal user payment amount if content replacement occurs in the system where the service provider is locatedIs that
Wherein 1 (·) is an indication function, when j ε Ω Δ When 1 (·) =1, otherwise 1 (·) =0, the user benefit is updated to
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| CN109901922A (en) * | 2019-03-05 | 2019-06-18 | 北京工业大学 | A kind of container cloud resource method for optimizing scheduling of oriented multilayer service |
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