Popularity and gain based caching scheme for information-centric networks

Transcription

1 Research Artcle Internatonal Journal of Advanced Computer Research, Vol 7(30) ISSN (Prnt): ISSN (Onlne): Popularty and gan based cachng scheme for nformaton-centrc networks Zhandong Fan, Qngtao Wu *, Mngchuan Zhang and Rujuan Zheng Informaton Engneerng College, Henan Unversty of Scence and Technology, Chna Receved: 09-February-2017; Revsed: 24-March-2017; Accepted: 27-March ACCENTS Abstract In nformaton-centrc networks (ICN), each node s equpped wth a cache and t can mprove content access and transmsson effcency. However, the bult-n cache capacty s small, and t can t completely store huge amounts of content transmtted. The exstng cachng schemes lack choces n content placement and balanced dstrbuton, thus, leadng to the problem of low cache ht rato and hgh user access tme delay. To solve the problem, ths paper proposes a popularty and gan based cachng scheme (). It refnes the content object from the content fle to the chunk, thus acheves fne-graned cache management. The strategy makes use of the feature of content chunk popularty, and takes the factors that affect the overall cachng gan nto consderaton, and realzes the placement and replacement of content chunks through comprehensve measure. The smulaton results show that compared wth other schemes, ths method can effectvely promote node s cache ht rato, reduce user request delay and further rase network servce qualty. Keywords Informaton-centrc networks, Content chunk, Popularty, Cachng gan. 1.Introducton Wth the rapd development of nternet, several of new applcatons have appeared. The tradtonal centered on host network archtecture has been dffcult to adapt to new applcaton requrements, whch faced wth a seres of challenges n moblty support, network scalablty and securty, etc. Although cachng technology n applcaton of content delvery networks (CDN) and peer to peer (P2P) [1] has allevated the pressure of applcaton requrements to a certan extent. The current cachng strateges are amed at a specfc applcaton and the technology wth shortcomngs of hgh complexty and costs and can t apply to all applcatons. In order to solve these problems, researchers have focused attenton on research of new Internet archtecture [2]. Informaton-centrc networks (ICN) emerges at the rght moment and has receved extensve research and development, among whch; the respectve schemes under ths background nclude the contentcentrc networkng (CCN)/ named-data networkng (NDN) [3], data-orented network archtecture (DONA) [4], NetInf [5], PDX [6], etc. In ICN, each node s equpped wth a bult-n cache, and ts core dea s that storng content through nternal routers of the network, then the user requests forward n the network. *Author for correspondence 71 The caches wll respond drectly f ht along the way, rather than from the content source (server) each tme [7]. Due to cache content closer to users, thus can reduce the consumpton of network bandwdth, server load and user access delay [8]. It s consderng these advantages, ths thought s extended to the entre network and all applcatons n the desgn of the new Internet. Cachng mechansm s the core of the ICN technology, the pros and cons of a cachng system drectly affect the effcency of ICN content dstrbuton, and a reasonable cachng polcy enables users easer to access, to content from the near cache nodes wthout from orgnal content server (OCS) every tme. The content placement and replacement strategy are two mportant aspects of cachng technology research, however, the major problems of current cachng polces nclude low space utlzaton rate, and low cache ht rato and large access latency. In order to solve the above problems, ths paper proposes a Scheme. The man contrbutons of ths paper are as follows. 1) Refnng the level of popularty to content chunk. The exstng cachng strateges are mostly based on content popularty, whch s not the same as

2 Zhandong Fan et al. content chunk popularty. Ths artcle refnes the level of popularty to the chunk and realzes fnegraned cachng management. 2) Based on comprehensve earnngs to fnd the best placement node. Comprehensvely measure factors that nfluence cachng gan, fndng the best node to cache the content chunk, and maxmzng the comprehensve ncome of nodes. 3) The substtuton method based on the comparson of the content chunk value. Comparng the value of the content chunk to be replaced wth the new arrval content chunk and determnng whether t s necessary to cache new content chunk. 2.Related works In ICN, f t s needed to establsh collaboraton doman and nteractve state nformaton of collaboraton between the nodes. ICN cachng strateges can be roughly dvded nto explct collaboraton and mplct cooperaton [9]. In explct collaboraton mechansm, a knd of common way s through the nteracton access mode between nodes n collaboraton doman, the cache state, etc. to mprove the cachng dversty n the cooperatve doman, but the strong dynamc nteracton of ICN cannot bear the huge costs. In mplct cooperatve cachng mechansm, nodes can acheve cache decson-makng wth lttle or less nteracton, at the same tme; have an advantage n terms of executon speed and complexty. The mplct cooperatve cachng mechansm can be dvded nto the followng two categores accordng to whether need to know accurate nformaton about content popularty beforehand. 2.1 Consderng potental content popularty cachng mechansm In [10] authors proposed an RCOne mechansm, whch stores content objects n a node randomly selected along the way transmsson path. ProbCache [11, 12] takes the node poston n the data transmsson path and cache capacty nto account whle makng probablty cachng decsons, makng the content wth greater probablty cached n the node closer to the requester. In [13] authors proposed a WAVE scheme whch provdes cachng advce for the subsequent nodes by settng the mark, and stores content chunks of fle n the exponental growth way along the transmsson path when the number of requests for a unt of the fle ncreases. Cachng decsons accordng to node and mportance n the communty have been suggested [14, 15]. Such decsons are relatvely smple, but lack of accurate 72 nformaton about content access frequency, and exst low speed of copyng the content to the edge, the excessve competton n the edge nodes, the dsorder competton of cache space between dfferent transmsson paths, etc. 2.2Cachng scheme based on content popularty In [16] authors proposed a cachng scheme based cachng age, whch calculates the cachng age of content accordng to the level of content popularty and the locaton of the storage nodes, the hgher the content popularty, the longer the cache resdes. A prob-pd scheme based on the probablty of popularty whch makes decson of probablty cachng accordng to content popularty and the dstance from the content source have been proposed [17]. A cachng mechansm based on ncome percepton, whch uses the flterng effect of request flow on the cache, and acheves collaboraton between nodes and cache dversty whle maxmzng sngle node earnngs have been proposed [18]. A probablstc storage based heurstc cachng method, the content wth hgher popularty and greater placement earnngs can have a greater cachng probablty have been proposed [19]. Badov et al. [20] proposed a congeston percepton and lookup mechansm, whch caches the more helpful content to allevate congeston and consders low bandwdth load of lnks preferentally when forwardng request messages. These schemes have a better performance n the cache ht rato and bandwdth savng, however, most of them, assumng content popularty s a known parameter, besdes, there s no clear descrpton nformaton that how to obtan and mantan content popularty by the router. In ICN cachng systems, the request arrval process s related to the cachng state of upstream nodes [21], so the arrval rate of content requests has a strong dynamc feature, and statc offlne popularty statstcs clearly can t wll meet the needs of cachng decsons. A drect method s to use a hash table to count the vsts of content; however, the storage cost of ths method s unbearable [22]. Some methods through the statstcs of coarse-graned content access frequency to reduce the complexty, the specfc methods nclude dvdng the content nto several levels n accordance wth content popularty [23, 24] and usng the content fle popularty nstead of content chunk popularty [25], etc. But users may show dfferent degrees of nterests towards the content chunks of same content fle; these methods gnore the dfferences whch nfluence the performance of the granular cache management.

3 Internatonal Journal of Advanced Computer Research, Vol 7(30) Dfferent wth the above works, ths paper comprehensvely measures the man factors whch nfluence the cachng gan by means of mplct cooperaton and fndng out the best placement node to cache content chunks through the calculaton of the server or nodes. At the same tme, the regular of rse and fall s consdered n cachng mechansm and usng the value of the content chunk comparson approach to decson new content chunk replacement. Besdes algorthm s smple and has a less cost of computng resources. 3. operaton mechansm In our system model, the network s made up of OCS and some routers equpped wth bult-n cache as well as some users. Among whch, only OCS can produce content, and store content permanently. Let G (V, E) be an undrected network wth V=v 1,...,v n nodes and E the set of lnks. For the sake of smplcty, we assume that v -1 s on the downstream sde of v, whch means v -1 s closer to the user. Ths artcle does not consder the stuaton of the flood routng, whch means the nodes choose the next-hop only accordng to routng protocols and not broadcast to all ports when receve nterest packet. 3.1The basc prncple of In order to make the cache n ICN more effcent, ths paper desgn's content chunk popularty and cachng gan based cachng scheme. The basc dea s based on the value of a content chunk n the cache space s changng dynamcally and decayng exponentally wth tme. At the same tme, n order to ensure that the hgh popularty content chunks have hgh cachng value, the value of content chunks ncreases exponentally wth the requested count, so the value of hgh popularty content chunks can be far hgher than the low popularty content chunks. The occuped space s marked as dle when the content chunks value below the threshold value set, however, the content chunks n the free space are not deleted mmedately, and wll be replaced only when new content chunks arrve, n ths way, the content chunks n the free space can contnue to provde servce for users. When the content chunks of the free space pop up agan, so that the value s hgher than the threshold set, and then cancel ts dle tag of the occuped space. Interest request packets wll forward n the form of labels, and f ht along the way, then the OCS or router wll packet and back to user along Interest path. If there are spare nodes on nterest path, then store the content chunk on the spare node closest to the user; f there are no spare nodes, and then select a 73 node whch can maxmze cachng gan as the best placement node. 3.2The mplementaton of cachng polcy Ths paper ams to desgn an n-network cachng scheme that can obtan hgh gan. Symbols are defned n Table 1. Table 1 Table of notaton Notaton Meanng o Content chunk v The dentfy of node w The average value of content chunks cached n v The occuped space of v S r f C d p D q The space of v expected to take up The free space of v The overall cache space of v The usage rato of v The dstance between v and user The total length of Interest path The rato of d and D Mv ( ) Cachng gan of v w0 m ( o) W ( o) Wt ( o ) The ntal value of content chunk The number of requests for o n v The value of o n v The value of o at tme t 3.2.1Cachng gan measurement In ICN, f the hgh popularty content chunks are stored n nodes closer to users, whch can reduce forwardng paths of nterest packets and data packets, thus can decrease users access tme effectvely and mprove the user s experence. However, not all content chunks wth hgh popularty can be stored on the node closest to the user due to the cache capacty constrants. Takng the nformaton of nodes whch ncludes the value of content chunks, the free space of nodes and the dstance between nodes and users and etc. nto account when searchng the node along the nterest path that can maxmum cachng gan f cache the content chunk. Defnng a measure ndcator M(v ) for a cachng gan of router nodes, the bgger value of M(v ) shows that placng the new content chunk on the node wll make overall nodes gan maxmal on the nterest path. The man factors nfluencng the value of M(v ) are: The smaller the average value w of the content cached by the v, ndcate that the hgher possblty content

4 Zhandong Fan et al. chunks n the node wll be replaced and the greater the value of M(v ). The smaller the cache usage rate p of v, shows that the greater the free space of the node, whch can make full use of the cache space f cache the new content chunk on the node, and the greater the value of M(v ). The smaller the dstance rato q of v, shows that the closer dstance between the node and user, whch can reduce the user s access delay f cache the new content chunk on the node, and the greater the value of M(v ). Comprehensve calculate w, p, q and obtan M(v ). Step 1: calculatng the average value of the content chunks cached n v. 1 w n n j 1 Among them, ( o ) content chunks cached n of content chunks cached, and chunk j. j w ndcates the average value of v, n ndcates the number o j ndcates content Step 2: Computng the utlzaton rate of v. S r p C Among them, S ndcates the amount (not ncludng the content chunk n free space) of space occuped of v, r ndcates the amount of space that expected to be occuped, the value of r s equal to the number of Interest packets forwarded across v whch not yet return data packets. The maxmum r equals C S (the sze of the free space of v ), thus t can guarantee 0<P <1. Step 3: The dstance factor. d q D Among them, d ndcates the dstance (the number of hops) between v and user along the nterest path and D ndcates the total length of Interest path. The calculaton formula of v cachng gan ndex Mv ( ) w p q When v has free space, then OCS or router nodes choose the best cache node accordng to f n Interest packet, at ths tme, does not calculate the value of M(v ), so there s no meanngless stuaton that denomnator equals The calculaton of cache value If the Interest packet ht at OCS, then set an ntal value w 0 for the content chunk (the value s greater than the threshold), and f the requested content chunk comes from router node, then the ntal value of the content chunk s the value that cached n the node. Assumng that the dstance between v and OCS s 1, and v 1 located on the downstream sde of v, so the value of content chunk o when t returned v s calculated as follows. to 1 m ( o) 1 w ( o) w0 a m W 1( o) w( o) a 1 ( o) 1 Among them, m (o) ndcates the requested tmes of o n v, and n order to avod repeatng calculaton requests for the frst tme then mnus 1. a(a>1) s the base of the ndex, and the greater ts value shows that the faster the value ncreases of content chunk, we assume that a=2. The value growth rule of the content chunks n node s. W( o) w ( o) a Among them, W(o) s the value of o after request, w (o) s the value of o before request, and the content chunk o wll growth a(a>1) tmes after every request, and the greater a shows that the faster the content chunk value ncreases, we assume that a=2. The value attenuaton rule of content chunks n node s. t t0 W ( o) w ( o) b t Among them, W t (o) s the value of o at tme t, W (o) s the value before attenuaton, t 0 ndcates the last updated attenuaton tme, t-t 0 ndcates the dstance from the last update tme, and s a constant. b(0<b<1) s the base number, the smaller ts value shows that the faster attenuaton of the content chunk, we assume that b=0.5, τ=1. v v 74

5 Internatonal Journal of Advanced Computer Research, Vol 7(30) 3.3The man algorthm cachng polcy s based on sngle Interest packet request, ths paper wll gve the strategy mplementaton wth the process of Interest packet and data packet. When user sends a request, f the Interest packet does not ht and there s no PIT entry for content request at the node, then the 4 group ( v, d, f, M(v )) wll be recorded and forwarded to the next node. If the Interest packet ht at the node, then take out the set of 4 groups and judge whether there s any nodes wth f >0 along Interest path. Fndng out v that closest to the user wth f >0. If exst, then cachng o on the drectly n the process of data packet back to user, and at the same tme replacng the mnmum value of the content chunk; If not exst, then choose v wth the maxmum M(v ) as the best cache node along Interest path. When the data packet back to the node, comparng wth the content chunk wth the mnmum value, f the value of the content chunk not less than the mnmum value, then replacng the mnmum value content chunk; f the value of the content chunk less than the mnmum value whch shows the content chunk has low popularty, then gvng up cache the content chunk. If the Interest packet hts at OCS after forwardng, then OCS wll pack content chunk nto packet and back to user along Interest path. Frst of all, OCS wll assgn an ntal value 0 and the value of o wll ncrease wth the number of request tmes ncreases. Then, the OCS determnes the locaton of the content chunk o, the process s smlar to the above process of when Interest packet ht at the node, so we wll not repeat t here. strategy s only assocated wth the value of f and M(v ) of the node, so the node that Interest packet ht can judge ndependently and there s no addtonal nformaton nteracton between nodes. 75 v w to the content chunk o In order to llustrate the strategy mplementaton process more clearly, the pseudo code of algorthm s gven n Table 2. Table 2 algorthm 1. Intalzaton 2. f exst nodes wth f >0 along Interest path 3. Case1: exst 4. IF (the number of nodes s equal to 1) 5. cachng content chunk o n the node and replacng the content chunk wth the lowest value cached n free space; 6. ELSE 7. cachng content chunk o n the node closest to the user and replacng the content chunk wth the lowest value cached n free space; 8. End IF; 9. Case2: not exst 10. selectng the node wth the largest M(v ) as the best cache node; 11. IF (the value of o not less than the lowest value of content chunk n v ) 12. cachng o n the node and replacng the content chunk wth the lowest value; 13. ELSE 14. dscardng the content chunk o and gvng up cachng; 15. End IF; 4.Experment smulaton and analyss In order to verfy the performance of proposed n ths paper, we carred out the smulaton and performance evaluaton based on ndnsim [26] smulator and used Matlab software draw graphcs wth smulaton data. The man performance parameters consdered nclude cache ht rato, the hop count and content dversty rato. 4.1The setup and method of smulaton Topologcal structure s based on any ntenton structure, as shown n Fgure 1; the expermental network topology conssts of 36 nodes. Fgure 1 Smulaton topology At the same tme, we assume that the number of the content s 10000, each content has the same sze 10 Mbytes, and has been dvded nto k=10 chunks; Each router buffer space s 100 Mbytes, whch can cache 100 content chunks. In addton, each user wll

6 Content dversty rato Hop count Ht Rato Zhandong Fan et al. send 1000 requests for content to connect routers n the smulaton process, besdes the arrval of the requests accord wth Posson dstrbuton and the user requests for content accord wth Zpf dstrbuton [27], n whch, we assume 0.5 α 1 and α=0.7. We selected representatve strategy as performance comparson objects: one s the tradtonal leave copy everywhere () strategy, another s the strategy based on the betweenness strategy. Ths paper used multple performance parameters that compared wth and, whch ncludes cache ht rato, hop count and content dversty rato. In addton, we also studed the effect of varous network parameters on the performance, such as cache sze, the number of content and Zpf parameters. The man parameters and default values of ths paper are gven n Table Cache space(m) (a) Ht rato changes over the cache sze Table 3 Expermental parameters Parameter Default value Range Number of nodes 36 Number of users 1000 Number of content ~20000 Content sze (MB) An average of 10 Node cache sze (MB) ~200 Access pattern Zpf: = ~ The result of the experment The experment results are gven n ths secton, n order to observe the network performance s affected by some parameters, we only make one parameter change and other parameters reman unchanged, ts default value s shown n Table The nfluence of the cache sze In ths secton, we frst observed the effect of cache sze on system performance. Fgure 2 shows the system performance changes over the cache sze, among them, cache sze have been ncreased from 50MB to 200MB. As expected, from Fgure 2 (a) you can see that the cache ht rato of these three mechansms ncreases wth the ncrease of the cache sze, and n ths process, the performance of has been always better than and. Fgure 2 (b) shows that the hops user access to the content of these three mechansms decreases wth the ncrease of the cache sze. Because of the ht rato has mproved the hop count also fell naturally. However, the hop count of s always lower than and. As s shown n Fgure 2 (c) that the CDR of these three mechansms ncreases wth the ncrease of the cache sze, and s always hgher than and Cache space(m) (b) Hop count changes over the cache sze Cache space(m) (c) Content dversty rato changes over the cache sze Fgure 2 The nfluence of the cache sze 76

7 Hop count Ht Rato Ht Rato Content dversty rato Internatonal Journal of Advanced Computer Research, Vol 7(30) 4.2.2The nfluence of content quantty Fgure 3 shows the stuaton that system performance changes over content quantty, the number of content changes from 5000 to n a wde range. As shown n Fgure 3(a) that the cache ht rato of these three mechansms reduces sgnfcantly wth the ncrease of content amount. Ths s manly because the cache sze hasn t changed but the content quantty ncreased, thus, the cache wll become ncreasngly scare and the probablty of Interest packet be responded s reduced naturally. However, compared wth and, always shows an obvous advantage. In Fgure 3(b), as expected, the hop count of these three mechansms ncreases wth content amount ncreases, but s always lower than and. As shown n Fgure 3(c) that the CDR of these three mechansms decreases wth the content amount ncreases and s always better than and n varous cases. However, the relatve advantage of s gradually shrnkng wth the ncrease of content amount Number of content x 10 4 (c) Content dversty rato changes over content quantty Fgure 3 The nfluence of content quantty The nfluence of Zpf parameters (α) Now, everybody has a wdespread belef that user preferences (.e., access mode) for the content follows Zpf dstrbuton. Dfferent applcatons has dfferent dstrbuted parameters, the greater Zpf parameter ( ) the more concentrated the user preferences. In ths secton, we study the nfluence of user preference on cachng mechansm performance, more accurately; we d lke to know the cachng mechansm performance for dfferent applcatons. Fgure 4 shows the results of the experment, n whch, α vares from 0.5 to Number of content x 10 4 (a) Ht rato changes over content quantty Number of content x 10 4 (b) Hop count changes over content quantty Alpha (a) Ht rato changes over parameter α 77

8 Content dversty rato Hop count Zhandong Fan et al Ths s because these three mechansms take advantage of the local tme of content n place or replacement strategy, whch s further proved that the mportance of usng local tme n desgnng cachng mechansm. Fgure 4(b) shows that the hop count of these three mechansms decreases wth the ncrease of α, but s better than and. Fgure 4(c) shows that the CDR of and decreases slowly wth the ncrease of α, whle the CDR of almost kept unchanged, but s always hgher than and. The above expermental results show that performance s better than and under varous expermental condtons whch nclude cache ht rato, hop count and content dversty rato Alpha (b) Hop count changes over parameter α Alpha (c) Content dversty changes over parameter α Fgure 4 The nfluence of Zpf parameter (α) Fgure 4(a) clearly shows that the cache ht rato of these three mechansms ncreases wth the ncrease of α, the performance mproves wth the strengthen of local tme. 5.Concluson In ths paper, we propose a content chunk popularty and gan based cachng scheme. It refnes content object to the chunk, and realzes fne-graned cachng decsons. The strategy makes full use of content chunk popularty, and takes the access frequency of content chunk as a measure of popularty. The popularty of a content chunk ncreases exponentally wth the requested count, at the same tme, t decays exponentally over tme. also takes node s overall cachng gan nto account, the new arrval content chunk s cached n the node whch can maxmze the overall cachng gan. The smulaton results show that the scheme can mprove the cache ht rato of routng nodes and reduce users requests tme delay. In the future, we wll study the correlaton between dfferent content chunks of a same content object, and apply t to the to mprove the performance of our algorthm. Acknowledgment Ths work s partally supported by the Natonal Natural Scence Foundaton of Chna (NSFC) under Grants no. U , no. U , and no , n part by the Program for Scence & Technology Innovaton Talents n the Unversty of Henan Provnce under Grants no. 16HASTIT035 and no. 14HASTIT045, n part by Program for Scence & Technology Innovatve Research Team n Unversty of Henan Provnce under Grant no. 14IRTSTHN021, and n part by Henan Scence and Technology Innovaton Outstandng Talent under Grant no Conflcts of nterest The authors have no conflcts of nterest to declare. 78

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10 Zhandong Fan et al. ZhanDong Fan receved the bachelor degree n computer scence and technology from Henan Unversty of Scence and Technology, Luoyang, Henan provnce, Chna, n 2014, and he s currently pursung the master s degree n the computer applcaton technology, Henan Unversty of Scence and Technology, Luoyang, Henan provnce, Chna. Hs research nterests nclude cloud computng, Internet of thngs and nformaton-centrc Network. Emal: @qq.com Qngtao Wu was born n Jangsu Provnce, PRC n March Qngtao Wu studed n East Chna Unversty of Scence and Technology (Shangha, PRC) from March 2003 to March 2006, majored n computer applcaton and earned a Doctor of Engneerng Degree n three years tme. He works as a Professor n Henan Unversty of Scence and Technology. Hs research nterests nclude component technology, computer securty and future Internet securty. Mngchuan Zhang was born n Henan Provnce, PRC on May Mngchuan Zhang studed at Bejng Unversty of Posts and Telecommuncatons (Bejng, PRC) from September 2011 to July 2014, majored n Communcaton and nformaton system and earned a Doctor of Engneerng Degree n three years tme. He works as an Assocate Professor at Henan Unversty of Scence and Technology. Hs research nterests nclude bonspred networks, Internet of Thngs, future Internet and computer securty. Rujuan Zheng was born n Henan Provnce, PRC n March Rujuan Zheng studed n Harbn Engneerng Unversty Technology (Harbn, PRC) from March 2005 to March 2008, majored n computer applcaton and earned a Doctor of Engneerng Degree n three years tme. She works as an Assocate Professor at Henan Unversty of Scence and Technology from March Her research nterests nclude bo-nspred networks, Internet of Thngs, and computer securty. 80