CN106296239A - Small base station towards multiple network data providers caches auction strategy - Google Patents
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- CN106296239A CN106296239A CN201510261130.5A CN201510261130A CN106296239A CN 106296239 A CN106296239 A CN 106296239A CN 201510261130 A CN201510261130 A CN 201510261130A CN 106296239 A CN106296239 A CN 106296239A
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Abstract
A kind of small base station towards multiple network data providers caches auction strategy.A kind of auction mechanism easy to carry out of this policy depiction so that the cache resources of multiple small base stations can be distributed under the competition of multiple network data providers effectively.This strategy proposes, and the assigning process of data to base station can complete to utilize multi-unit auction mode repeatedly, auctions number of times and depends on the memory capacity of small base station, and bidding based on to user density, data access probability and the estimation of network delay of auctioning.Wherein a memory space of all small base stations is considered as the article of auction by auction every time, by every number according to being considered as suitor, will cache for all base station overabsorption portion different pieces of informations after each End of Auction.Algorithm preference based on article and the suitor figure of auction, changes the structure of preference figure by the price repeatedly raising different article, until there is a Perfect Matchings providing the method for salary distribution in preference figure.
Description
Technical field:
The present invention relates to a kind of caching auction strategy in wireless network resource distribution, be used for the spatial cache of multiple small base stations
Distribute to multiple network data provider.
Background technology:
The when that user in wireless network using that data are downloaded in request in mobile device access network, signal demand through access network with
And core net gets to the Internet, the data on the Internet then also pass through core net and access network just can be sent to user.
Owing to link is longer and according to the difference of offered load, the delay that transmission brings is the most different, so it is difficult to ensure that mobile subscriber's access
The experience of network data.
Because small base station is by universal at present, and can have stronger performance, reasonable solution is that access frequency is high
Data be stored in small base station as caching.If the network data that so certain user desires access to has been buffered in
In neighbouring small base station, then just can experience comparatively faster access speed.But the frequent phase of the area coverage of small base station
The most overlapping, how while optimizing caching mean hit rate, to avoid over-redundancy, become and hinder the biggest factor finding optimal solution.
Additionally, the existence of multiple network data providers result in the competition of cache resources, need a kind of strategy at present badly and make cache resources have
Distribute to these network data providers to effect.
Summary of the invention:
Current existing scheme all only focus on how to design allocative decision make substantial amounts of data buffer storage in limited memory space thus
Optimize caching mean hit rate, the most all do not account for the existence of multiple network data provider so that strategy itself is difficult to be suitable for
In real scene.Therefore currently invention addresses and how to formulate a kind of strategy, by effectively utilizing different network data providers
Between competition, optimize the mean hit rate of caching.
Consider that having multiple network data provider to need respective data caches, and network has some small base stations, each
Base station can store a certain amount of data.According to wireless carriers obtain statistical data and predict user density distribution,
The delay decrement that data stream degree and caching bring, network data provider can estimate certain number according to being buffered in certain base
The income can brought for user in standing.This invention proposes, and uses multi-unit auction strategy repeatedly can complete data and arrives base station
Assigning process, wherein auction all will cache for a different pieces of information of all base stations distribution every time, and bidding in auction process
It is based on above-mentioned provisional profit value.
This invention not only describes a kind of tactful cache resources making multiple small base station of auction easy to carry out can be at multiple nets
Effectively distributed between network metadata provider, moreover it is possible to ensure the little node B cache mean hit rate of wireless system to a certain extent.
Accompanying drawing illustrates:
Fig. 1 is the system model schematic diagram of the present invention, with three small base stations and two metadata providers as an example.
Detailed description of the invention:
The auction strategy that the present invention proposes comprises multi-unit auction repeatedly, and number of times depends on the memory capacity of small base station.The most
During article auction, a memory space of all small base stations is considered as the article of auction, and by every number according to being considered as suitor.
After multi-unit auction terminates every time, each small base station is by data different for overabsorption on the basis of upper once auction to portion
Cache.After the memory space of all of small base station all distributes and terminates, whole auction process also will terminate.
For each multi-unit auction, it is as follows for the algorithm flow that small base station distribution is data cached:
1, initialize: owing to, in each multi-unit auction, the quantity of article (memory space) is less than the number of bidders (data)
Amount, it is therefore desirable to increase virtual article and make number of articles reach number of bidders, and ensure in valuation matrix virtual object
The valuation of product is zero, and the bidders being assigned to virtual objects after End of Auction finally will not obtain any article.Hereafter, respectively
The price of individual article is all initialized to zero.
2, calculate valuation: according to current completed allocation result valuation and calculate by every number according to storage can obtain in certain base station
The additional benefit obtained, constitutes a valuation matrix.Wherein in order to calculate valuation, need user density distribution, data stream degree with
And the delay decrement these three data that caching brings.Certain number is this according to the valuation=storage of storage to certain base station to this base station
The popularity (accessed probability) of user's average density × this number evidence under additional coverage areas area × this region that video increases
× carry out caching and the delay decrement brought.
3, set up preference figure: article are considered as a group node, bidders is considered as another group node, be each according to each article
The profit (profit=valuation-price) that bidders is brought, each bidders node is required in the drawings to bringing maximum profit for it
Article node (one or several) line, this bigraph (bipartite graph) is desired preference figure.
4, find maximum match: from the beginning of certain in given preference figure does not mates article node, use BFS
(BFS) augmenting path is found, by the coupling limit in the augmenting path found being become non-matching limit and non-matching limit becomes
Cobordant, it is possible to obtain a bigger coupling.Repeat to find augmenting path until there is not augmenting path.
5, judging whether maximum match is Perfect Matchings, the most each node is matched another node: 1) if Perfect Matchings,
Then jump to the 6th step.2) if not Perfect Matchings, then the node being traversed in the last BFS constitutes one
Individual limited collection.Promote the item price that article node in this set is corresponding, until suitor changes oneself line in preference figure
Till.If finding all items price minima non-zero, then it is unified in the price of all items and deducts this value.Jump to the 2nd
Step revalues and sets up preference figure, until finding Perfect Matchings.
6, maximum match this moment i.e. illustrates allocation result, and price then represents concluded price, and algorithm terminates.
The proof of this Algorithm Convergence: definition algorithm price of m-th article when running to certain moment isAnd define this
Carving the potential profit of the article of its preference faced by the n-th suitor isTo all ofWithIt is total that summation is potential society
Welfare, if but Perfect Matchings do not exist, then this welfare value can because be too high to obtain.It is being all items every time
The when that price deducting minimum non-zero value, eachReduce andIncrease this nonzero value, due to number of articles and suitor's quantity phase
Constant with therefore social total benefit.When the article being limited concentration increase price every time, the point of each limited concentrationIncrease
AndReducing same value, the article number of nodes yet with limited concentration is more than suitor's quantity, causes social total benefit to decline.
Finally having the social total benefit in the preference figure of Perfect Matchings must be a nonnegative value being not more than initial social total benefit value,
As long as therefore when the valuation of article is expressed as limited location decimal, by the iteration of finite number of time, initial social total benefit can be dropped
Low can restrain to final social total benefit value, i.e. algorithm.
Claims (3)
1. the small base station towards multiple network data providers caches auction strategy, is used for the caching of multiple small base stations
The different pieces of information of multiple network data provider is distributed to as caching in space, it is characterised in that: consider small base station and cover model
The competition between overlapping and heterogeneous networks metadata provider between enclosing, gives a kind of multistep solution theoretical based on multi-unit auction
Certainly scheme.
Small base station towards multiple network data providers the most according to claim 1 caches auction strategy, and its feature exists
In described multi-unit auction process, the every a memory space of all small base stations is accordingly to be regarded as the article of auction, and by every number
According to being accordingly to be regarded as suitor.
3. caching auction strategy according to the small base station towards multiple network data providers described in claim 1 and 2, it is special
Levy and be that the algorithm of each multi-unit auction process is as follows:
1) initialize: increase virtual objects and make article and number of bidders equal, and ensure in valuation matrix virtual objects
Valuation be zero；The price of each article is all initialized to zero；
2) valuation is calculated: calculate the additional benefit storing data into small base station according to current allocation result, constitute valuation matrix；
3) setting up preference figure: article and bidders are respectively seen as a group node, each bidders node is required in the drawings to energy
The article node line of maximum profit is brought for it；
4) maximum match is found: constantly look for augmenting path and obtain bigger coupling, until there is not augmenting path；
5) judge whether maximum match is Perfect Matchings: if Perfect Matchings, then jump to the 6th) step；If not Perfect Matchings,
The item price that then in one limited collection of lifting, article node is corresponding, until preference figure changes structure, jumps to the 2nd) step；
6) maximum match i.e. illustrates allocation result, and price then represents concluded price, and algorithm terminates.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107590711A (en) * | 2017-07-07 | 2018-01-16 | 南京理工大学 | Based on the wireless cache policy that binding algorithm and more wheel two way auctions are theoretical |
| CN110135994A (en) * | 2019-05-21 | 2019-08-16 | 中国科学技术大学 | The transaction processing method of time-sensitive data |
| CN110602173A (en) * | 2019-08-20 | 2019-12-20 | 广东工业大学 | Content cache migration method facing mobile block chain |
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| US20110029347A1 (en) * | 2009-07-31 | 2011-02-03 | Kozat Ulas C | Method for wireless network virtualization through sequential auctions and conjectural pricing |
| CN103220757A (en) * | 2013-04-19 | 2013-07-24 | 重庆邮电大学 | Optimal relay selection method based on two-way auction model |
| CN104469847A (en) * | 2014-10-28 | 2015-03-25 | 南京大学 | Method for balancing base station loads based on auction algorithm |
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| US20060167785A1 (en) * | 2005-01-27 | 2006-07-27 | Mullany Francis J | Bidding a price for goods and/or services in an auction of wireless communication access requests within a marketplace |
| US20110029347A1 (en) * | 2009-07-31 | 2011-02-03 | Kozat Ulas C | Method for wireless network virtualization through sequential auctions and conjectural pricing |
| CN101945369A (en) * | 2010-09-08 | 2011-01-12 | 北京航空航天大学 | Auction and satisfaction model-based dynamic frequency spectrum allocation method |
| CN103220757A (en) * | 2013-04-19 | 2013-07-24 | 重庆邮电大学 | Optimal relay selection method based on two-way auction model |
| CN104469847A (en) * | 2014-10-28 | 2015-03-25 | 南京大学 | Method for balancing base station loads based on auction algorithm |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107590711A (en) * | 2017-07-07 | 2018-01-16 | 南京理工大学 | Based on the wireless cache policy that binding algorithm and more wheel two way auctions are theoretical |
| CN110135994A (en) * | 2019-05-21 | 2019-08-16 | 中国科学技术大学 | The transaction processing method of time-sensitive data |
| CN110135994B (en) * | 2019-05-21 | 2023-06-16 | 中国科学技术大学 | Transaction processing method for time sensitive data |
| CN110602173A (en) * | 2019-08-20 | 2019-12-20 | 广东工业大学 | Content cache migration method facing mobile block chain |
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Application publication date: 20170104 |