CN111445318A - NVM-oriented edge cache auction method for differentiated services - Google Patents
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Abstract
The invention discloses an edge cache auction method for NVM-oriented differentiated services, which comprises the following steps: s1: constructing an edge cache model, S2: defining the interested content set of the user j as SjWherein, in the step (A),
defining the set of users interested in content i as omegai(ii) a S3: defining the content demand of the user as a user estimation value, and constructing a joint probability density distribution function of the user estimation value by using the user estimation value; s4: constructing a user income target function; s5: introducing the differentiated service factors and the NVM factors into an edge cache model, and constructing a mathematical objective function of the profit of the service provider, wherein ER is the profit of the service provider; s6: and dividing a system where the service provider is located into two stages, and solving the maximized benefit of the service provider. The invention is constructed by combining multiple factorsThe internet scene edge cache model achieves maximization of benefits of service providers while meeting different requirements of users and considering 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 NVM-oriented differentiated services.
Background
In the big data era, with the explosive growth of internet data, organizations have predicted that over 500 billion devices will be connected to the internet by 2020, and that internet data will 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 peak period of network data transmission of the big data background, because the cloud server bears huge load pressure, the traditional cloud computing technology is difficult to meet the QoS and QoE.
A number of studies have shown that edge caching can effectively address such problems. Edge caching still faces some challenges. First, the storage capacity of the edge devices and their embarrassment. Most of the existing edge devices are small in capacity, and although the capacity can be extended by using NVM (non-volatile memory/storage), the NVM has the disadvantages of asymmetric reading and writing and limited erasing times/lifetime. Second, diverse and varied user preferences will affect the effectiveness of the edge cache. The growing trend of internet data changes the distribution of user preference from a distribution following zigh's law to a stretched exponential distribution SED. Changes in user preferences can affect content placement in edge devices and can even exacerbate NVM wear, reducing NVM lifetime. Thirdly, in an actual internet application scenario, different internet users have different service level requirements, i.e., differentiated services. The content quality of service depends on the user's demand for content, and the SP needs to provide different levels of quality of service to users with different demands.
Reference [1] discloses an edge computing task unloading method and device based on a bidirectional auction mechanism, which are applied to a resource allocation server in an edge computing system, and include a resource allocation server, a plurality of user devices and a plurality of edge servers, so as to improve the utilization rate of computing resources of the edge servers. It models the edge server side's need to know the user device side, which is undesirable in internet scenarios, which do not take into account the privacy of the user. In addition, the method does not consider applying the NVM with wide storage prospect 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 an internet scene, considering the relation between CP-SP-users, the SP obtains the content from the CP and caches the content, and the user obtains the content from the SP. In the method, the user preference is assumed to be unknown, the CP-SP-user relation is modeled, the user is stimulated to speak the true words, and the most profitable content is cached in the SP so as to maximize the benefit of the SP. But it is not preferable in the internet application scenario that different users can deliver the same content to different users with the same service quality without considering the requirements of different users for different service levels of the same content among CP-SP-users. In addition, the SP in this method does not consider applying the NVM having a wide storage prospect to the edge server side, and does not consider the storage wear cost of the edge server side.
Therefore, a method for maximizing the service provider profit is obtained based on the research of an interconnection scenario model of the combined factors.
Disclosure of Invention
The invention provides an edge cache auction method for NVM-oriented differentiated services, aiming at overcoming the defects that in the prior art, the elements based on an Internet scene model are single and the income of a maximized service provider cannot be obtained.
The primary objective of the present invention is to solve the above technical problems, and the technical solution of the present invention is as follows:
an edge cache auction method of NVM-oriented differentiated services, comprising the following steps:
s1: constructing an edge cache model, wherein the edge cache model comprises the following steps: the system comprises m content publishers, a service provider and n users, wherein m and n are positive integers, the content publishers are used for publishing videos, and the users are used for requesting videos from the service provider;
s2: defining the interested content set of the user j as SjWherein, in the step (A),
defining the set of users interested in content i as omegaiWherein, in the step (A),
setting up
Wherein | SjL represents the number of contents that interest user j, | ΩiI represents the number of users interested in the content i;
s3: defining the content demand of the user as a user estimation value, and constructing a joint probability density distribution function of the user estimation value by using the user estimation value;
s4: constructing a user income target function;
s5: introducing the differentiated service factors and the NVM factors into an edge cache model, and constructing a mathematical objective function of the profit of the service provider, wherein ER is the profit of the service provider;
s6: and dividing a system where the service provider is located into two stages, and solving the maximized benefit of the service provider.
In this embodiment, the user estimation in step S3 includes: a virtual estimate and a real estimate, said virtual estimate being denoted as τ ═ τ1,τ2,...,τn]And the real estimation value is recorded as t ═ t1,t2,...,tn]τ and t are independent of each other, where tjIn the interval
Is denoted as fj(tj) The cumulative distribution function of which is denoted as Fj(tj) The joint evaluation interval for all users can be recorded as
The joint probability density distribution function can be written as
In the scheme, the evaluation value of the user is the demand of the user for the content.
In the scheme, the specific process of constructing the user income target function in the step S4 is as follows: the content publisher publishes content i to the service provider, which is at a fixed price per unit riPaying to the content distributor when the service provider assumes a transmission quality thetak(θkK) to K users, the transmission cost of the service provider is
Where h is a content transfer cost calculation function, assuming that user j receives the in-set S from the service providerjAnd the delivery quality is theta, and the unit satisfaction degree of the user j is recorded as theta tjThe user's profit can be recorded as
Wherein p isi(t) represents the ratio of the storage capacity of the content i in the service provider (0. ltoreq. p)i≤1)；
In this embodiment, the mathematical objective function of the profit of the service provider in step S5 is:
wherein the integral term
The sum of payments for the user,
Cost of content paid to content publishers for service providers,
For NVM wear cost in service provider,
A delivery cost for the service provider to deliver the content to the user; wherein xj(t) represents the payment amount of user j, pi(t) represents the ratio of the storage capacity of the content i in the service provider (0. ltoreq. p)i≤1)，riRepresenting the price per unit of content i, η is the NVM wear coefficient, η increases with NVM usage duration, initialize η1＝1，gi(t) represents the amount of change in the current to last storage capacity ratio of content i, ciRepresents the i unit abrasion cost of the content, | omegaiL represents the number of users interested in the content i, h (θ) is a delivery cost function of the SP service provider when the delivery quality is θ;
change g of next time content i is in proportion to current storage capacityi(t) is defined as:
gi(t)＝pi(x)-pi(t) (2)
where x is the distribution of the next user real estimate (user preferences most recently collected by the service provider), and t is the distribution of the user real estimate (reflecting previous user preferences) currently running by the service provider system.
In the scheme, the system where the service provider is located is divided into two stages, and the specific process of solving the maximized benefit of the service provider is as follows:
the service provider is in the initial operation phase of the system, the operation phase flag is marked as T, T is 0, and the setting is η1＝1,gi(t) 0, the objective function is simplified as:
defining a virtual estimate
Optimal content storage ratio p*(t) solving to obtain:
solving out the optimal payment amount of the user
Comprises the following steps:
the service provider delivers the data in different delivery qualities theta according to different valuations of the usersjDelivering to the user; defining a content delivery cost function as
The sum of the delivery costs of the service provider is obtained as
According to S4, the benefit of user j is solved as:
the system of the service provider operates at the middle and later stages, the operation stage marks that T is 1, and the current valuation of the user is T1The last collected user is estimated as t0Definition taking into account NVM wear and cost incurred by content replacement in service providers
η thereinwRepresenting the wear state coefficient of the NVM when T ═ w, let η0When 1, Delta is expressed in pi(t1) Not equal to 0 and pi(t0) Content set not equal to 0;
in order to reduce the NVM wear cost, the service provider needs to suppress the number of NVM wear times and reduce the number of unnecessary cache replacement times, i.e. maximize equation (2), which is solved as follows:
optimal user payment amount if content replacement occurs in the system of the service provider
Is composed of
Where 1 (-) is an indicator function, when j ∈ ΩΔWhen 1(·) is 1, otherwise 1(·) is 0, the user profit is updated to
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the method, the edge cache model of the internet scene is constructed by combining multiple factors, so that the profit of a service provider is maximized while different requirements of users are met and the NVM abrasion cost is considered.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
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 specifically described herein, and therefore the scope of the present invention is not limited by 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: the system comprises 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 to the service provider;
s2: defining the interested content set of the user j as SjWherein, in the step (A),
defining the set of users interested in content i as omegaiWherein, in the step (A),
setting up
Wherein, | SjL represents the number of contents that interest user j, | ΩiI represents the number of users interested in the content i;
it should be noted that different users have different requirements for the same content, and the same user also has different requirements for the same content. The more the service provider can make a profit, as analyzed from the service provider perspective, the greater the user's demand for content and the user obtains content from the service provider. For this purpose, the invention defines the user's demand for content as a user rating.
S3: defining the content demand of the user as a user estimation value, and constructing a joint probability density distribution function of the user estimation value by using the user estimation value;
the user estimation in step S3 includes: a virtual estimate and a real estimate, said virtual estimate being denoted as τ ═ τ1,τ2,...,τn]And the real estimation value is recorded as t ═ t1,t2,...,tn]τ and t are independent of each other, where tjIn the interval
Is denoted as fj(tj) The cumulative distribution function of which is denoted as Fj(tj) The joint evaluation interval for all users can be recorded as
The joint probability density distribution function can be written as
S4: constructing a user income target function;
the specific process of constructing the user income objective function in the step S4 is as follows: the content publisher publishes content i to the service provider, which is at a fixed price per unit riPaying to the content distributor when the service provider assumes a transmission quality thetak(θkK) to K users, the transmission cost of the service provider is
Where h is a content transfer cost calculation function, assuming that user j receives the in-set S from the service providerjAnd the delivery quality is theta, and the unit satisfaction degree of the user j is recorded as theta tjThe user's profit can be recorded as
Wherein p isi(t) represents the ratio of the content i in the storage capacity of SP (0. ltoreq. p)i≤1)；
S5: introducing the differentiated service factors and the NVM factors into an edge cache model, and constructing a mathematical objective function of the profit of the service provider, wherein ER is the profit of the service provider;
the mathematical objective function of the revenue of the service provider in step S5 is:
wherein the integral term
The sum of payments for the user,
Cost of content paid to content publishers for service providers,
For NVM wear cost in service provider,
A delivery cost for the service provider to deliver the content to the user; wherein xj(t) represents the payment amount of user j, pi(t) represents the ratio of the storage capacity of the content i in the service provider (0. ltoreq. p)i≤1)，riRepresenting the price per unit of content i, η is the NVM wear coefficient, η increases with NVM usage duration, initialize η1＝1，gi(t) represents the amount of change in the current to last storage capacity ratio of content i, ciRepresents the i unit abrasion cost of the content, | omegaiL represents the number of users interested in the content i, h (θ) is a delivery cost function of the SP service provider when the delivery quality is θ;
change g of next time content i is in proportion to current storage capacityi(t) is defined as:
gi(t)＝pi(x)-pi(t) (2)
where x is the distribution of the next user real estimate (user preferences most recently collected by the service provider), and t is the distribution of the user real estimate (reflecting previous user preferences) currently running by the service provider system.
S6: and dividing a system where the service provider is located into two stages, and solving the maximized benefit of the service provider.
In the scheme, the system where the service provider is located is divided into two stages, and the specific process of solving the maximized benefit of the service provider is as follows:
the service provider is in the initial operation phase of the system, the operation phase flag is marked as T, T is 0, and the setting is η1＝1,gi(t) 0, the objective function is simplified as:
defining a virtual estimate
Optimal content storage ratio p*(t) solving to obtain:
solving out the optimal payment amount of the user
Comprises the following steps:
the service provider delivers the data in different delivery qualities theta according to different valuations of the usersjDelivering to the user; defining a content delivery cost function as
The sum of the delivery costs of the service provider is obtained as
According to S4, the benefit of user j is solved as:
the system of the service provider operates at the middle and later stages, the operation stage marks that T is 1, and the current valuation of the user is T1Last time of collectionThe users of the set estimate t0Definition taking into account NVM wear and cost incurred by content replacement in service providers
η thereinwRepresenting the wear state coefficient of the NVM when T ═ w, let η0When 1, Delta is expressed in pi(t1) Not equal to 0 and pi(t0) Content set not equal to 0;
in order to reduce the NVM wear cost, the service provider needs to suppress the number of NVM wear times and reduce the number of unnecessary cache replacement times, i.e. maximize equation (2), which is solved as follows:
optimal user payment amount if content replacement occurs in the system of the service provider
Is composed of
Where 1 (-) is an indicator function, when j ∈ ΩΔWhen 1(·) is 1, otherwise 1(·) is 0, the user profit is updated to
The invention carries out the verification of the method through the following algorithm configuration, and the configuration parameters are as shown in the table 1:
TABLE 1
To is coming toDifferentiated service to users, in experiments we give high delivery quality to highly valued users (set θ)j2), a low delivery quality is given to a user with a low evaluation value (set θ)j＝1)。
For algorithm 104The repeated simulation experiments verify that the experiments show that the user evaluation value distribution (uniform distribution Uni, exponential distribution Exp, uniform to exponential distribution Uni2Exp) is similar to the document [2]]Compared with the algorithm in (1), our algorithm obtains a ratio document [2] in four important evaluation indexes (SP profit ER, user profit u, profit margin of user profit, average wear number of NVM in SP)](Baseline) is better.
Reference to the literature
[1] An edge computing task unloading method and device based on a two-way auction mechanism is disclosed in application number 201910789821.0.
[2]Cao,X.,Zhang,J.,Poor,H.V.:An optimal auction mechanism for mobileedge caching.In:2018IEEE 38th International Conference on DistributedComputing Systems(ICDCS).pp.388–399.IEEE(2018).
[3]Guo L,Tan E,Chen S,et al.The stretched exponential distribution ofinternet media access patterns[C]//Proceedings of the twenty-seventh ACMsymposium on Principles of distributed computing.2008:283-294.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (6)
1. An edge cache auction method for NVM-oriented differentiated services, characterized by comprising the following steps:
s1: constructing an edge cache model, wherein the edge cache model comprises the following steps: the system comprises 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 to the service provider;
s2: defining the interested content set of the user j as SjWherein, in the step (A),
defining the set of users interested in content i as omegaiWherein, in the step (A),
setting up
Wherein, | SjL represents the number of contents that interest user j, | ΩiI represents the number of users interested in the content i;
s3: defining the content demand of the user as a user estimation value, and constructing a joint probability density distribution function of the user estimation value by using the user estimation value;
s4: constructing a user income target function;
s5: introducing differential service factors and NVM factors into an edge cache model, and constructing a mathematical objective function of the profit of a service provider, wherein ER is the profit of the service provider, and the NVM is a nonvolatile memory/storage;
s6: and dividing a system where the service provider is located into two stages, and solving the maximized benefit of the service provider.
2. The method of claim 1, wherein the step S3 of evaluating the user value comprises: a virtual estimate and a real estimate, said virtual estimate being denoted as τ ═ τ1,τ2,...,τn]And the real estimation value is recorded as t ═ t1,t2,...,tn]τ and t are independent of each other, where tjIn the interval
Is denoted as fj(tj) The cumulative distribution function of which is denoted as Fj(tj) The joint evaluation interval for all users can be recorded as
The joint probability density distribution function can be written as
3. The method of claim 2, wherein the user's valuation is the user's demand for content.
4. The method for auction edge cache of NVM-oriented differentiated services according to claim 3, wherein the specific process of constructing the user profit objective function in step S4 is as follows: the content publisher publishes content i to the service provider, which is at a fixed price per unit riPaying to the content distributor when the service provider assumes a transmission quality thetak(θkK) to K users, the transmission cost of the service provider is
Where h is a content transfer cost calculation function, assuming that user j receives the in-set S from the service providerjAnd the delivery quality is theta, and the unit satisfaction degree of the user j is recorded as theta tjThe user's profit can be recorded as
Wherein p isi(t) represents the ratio of the storage capacity of the content i in the service provider (0. ltoreq. p)i≤1)。
5. The method for auction edge cache of NVM-oriented differential services according to claim 4, wherein the mathematical objective function of the revenue of the service provider in step S5 is:
wherein the integral term
The sum of payments for the user,
Cost of content paid to content publishers for service providers,
For NVM wear cost in service provider,
A delivery cost for the service provider to deliver the content to the user; wherein xj(t) represents the payment amount of user j, pi(t) represents the ratio of the storage capacity of the content i in the service provider (0. ltoreq. p)i≤1)，riRepresenting the price per unit of content i, η is the NVM wear coefficient, η increases with NVM usage duration, initialize η1＝1，gi(t) represents the amount of change in the current to last storage capacity ratio of content i, ciRepresents the i unit abrasion cost of the content, | omegaiL represents the number of users interested in the content i, h (θ) is a delivery cost function of the service provider when the delivery quality is θ;
change of content i in next ratio to current storage capacityVariable gi(t) is defined as follows:
gi(t)＝pi(x)-pi(t) (2)
wherein, x is the distribution of the real estimation value of the user at the next time, and t is the distribution of the real estimation value of the user currently running by the current service provider system.
6. The method according to claim 5, wherein the system of the service provider is divided into two stages, and the specific process of solving the maximum profit of the service provider is as follows:
the service provider is in the initial operation phase of the system, the operation phase flag is marked as T, T is 0, and the setting is η1＝1,gi(t) 0, the objective function is simplified as:
defining a virtual estimate
Optimal content storage ratio p*(t) solving to obtain:
solving out the optimal payment amount of the user
Comprises the following steps:
the service provider delivers the data in different delivery qualities theta according to different valuations of the usersjDelivering to the user; defining a content delivery cost function as
The sum of the delivery costs of the service provider is obtained as
According to S4, the benefit of user j is solved as:
the system of the service provider operates at the middle and later stages, the operation stage marks that T is 1, and the current valuation of the user is T1The last collected user is estimated as t0Definition taking into account NVM wear and cost incurred by content replacement in service providers
η thereinwRepresenting the wear state coefficient of the NVM when T ═ w, let η0When 1, Delta is expressed in pi(t1) Not equal to 0 and pi(t0) Content set not equal to 0;
in order to reduce the NVM wear cost, the service provider needs to suppress the number of NVM wear times and reduce the number of unnecessary cache replacement times, i.e. maximize equation (2), which is solved as follows:
optimal user payment amount if content replacement occurs in the system of the service provider
Is composed of
Where 1 (-) is an indicator function, when j ∈ ΩΔWhen 1(·) is 1, otherwise 1(·) is 0, the user profit is updated to
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| US20160292724A1 (en) * | 2015-04-06 | 2016-10-06 | Jed HOROVITZ | Systems and methods for maximizing marketplace transactions |
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