personalized cache management for mobile computing environments

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Information Processing Letters 87 (2003) 221–228

www.elsevier.com/locate/ip

Personalized cache managementfor mobile computing environments

Ho-Sook Kim∗, Hwan-Seung Yong

Department of Computer Science and Engineering, Ewha Institute of Science and Technology, Seoul, Republic of Korea

Received 29 November 2001; received in revised form 28 December 2002

Communicated by A. El Abbadi

Keywords:Databases; Cache management; Mobile computing

1. Introduction tive role in maintaining cached items [3]. Based

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The mobile computing market is increasing rapidSeveral methods have been proposed that deafectively with the restrictions of the mobile compuing environments. In particular, many studies habeen done on caching methods. Caching frequentlycessed data on the mobile host side can reduce iactions between clients and servers [1,6].

Previous research on cache methods was desiaccording to temporal properties of data such ascess time or updating frequency [2,6]. These sties, however, have limitations because they overlusers’ mobility and the spatial attributes of geograpcal data. To support users’ mobility, Liu et al. [7] sugested a server-oriented cache management methat exploits information on the estimated directiand range of a mobile host. Each mobile client, hoever, freely and frequently connects with, or disconects from, the wireless network. Therefore, it is ufeasible for a server to keep track of all cached copof individual items. Clients should take a more a

* Corresponding author.E-mail address:[email protected] (H.-S. Kim).

0020-0190/$ – see front matter 2003 Published by Elsevier B.Vdoi:10.1016/S0020-0190(03)00352-1

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ture of data, Dunham et al. [5] envisioned geomrically distributed spatial replicas located at differesites throughout an entire geographic domain. Thmethods, however, assume that all spatial attributedata in a cell are identical except for their locatioRen et al. [8] proposed a semantic caching schecalled FAR, which considers the location, speeddirection of users. To support virtual walkthrough aplications in distributed virtual environments, Chet al. [4] proposed a caching algorithm that consers the importance of data such as the distance oobject from the viewer, as well as the size and oriention of an object. The density of a region and varioquery patterns, however, are not considered in theigorithms.

In this paper, we show that the movement omobile host determines the changes in the valuesemantics of data in the mobile host’s cache. We arthat nearby data is better suited to give answerusers’ queries in mobile environments. In addition,define spatial properties of data such as the locaand region. Using these spatial properties, we proptwo new cache replacement methods that efficie

222 H.-S. Kim, H.-S. Yong / Information Processing Letters 87 (2003) 221–228

support users’ mobility and spatial attributes of data.We also analyze a variety of factors affecting the

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cache hit ratio of a mobile host, and we evaluthe performance of cache replacement methods bon these factors. Finally, we propose a personalcache replacement selection algorithm with optimperformance in variable mobile environments.

2. Cache replacement methods using the spatialproperties of data

2.1. Basic assumptions and definitions of terms

We assume that a server broadcasts informatiothe spatial data included in its area and that all datits database have the same size.

Each datum has a spatial location and logical rain which it has importance. We define spatial rangethe region of a datum. Weather information, for exaple, is meaningful in a certain geographical area,traffic information is useful in a smaller area [3]. Aother example is the target area of mobile advertments, which are transmitted to wireless hosts sas PDAs and cellular phones, based on the chateristics of the wireless Internet such as personaltion and immediacy [9]. When stores wish to selocality-based advertisements to mobile hosts, thevertising cost determines the size of the region cered. A hamburger chain, for example, wants tovertise a discount coupon at lunchtime to mobile howithin 200 m of the store. In this paper, the regiona datum is represented by the minimum boundarytangle (MBR).

The properties of a mobile host comprise tcurrent position of the mobile host(Xh,Yh), the cacheregion (CR) and the cache size (CS). The CRrepresented as a circle whose radius isrCR. The cacheof a mobile host can store all the data located inCR. The CS represents the number of data that castored in the cache.

2.2. The design of new cache replacement methodbased on spatial properties of data

Since a mobile host changes its location otime, the response to a query such as “Where isnearest subway station?” depends on the locatio

changes its location. Hence, to improve the cacheratio, we propose new cache replacement methodssupport users’ mobility and the spatial propertiesdata.

Definition 1 (Cache replacement based on the loction of data(CR_LOC)). CR_LOC is a cache replacment method based on the distance between thecation of a mobile host and the location of a datuWhen a mobile host replaces the cache, the datuthe farthest location from the mobile host is delefrom the cache.

Definition 2 (Distance from a mobile host to the regioof a datum). If a mobile host is inside the region ofdatum, the distance is 0. Otherwise, it is defined asshortest Euclidean distance from the current posiof the mobile host to the MBR of the region of thdatum.

Definition 3 (Cache replacement based on the regof data(CR_REG)). CR_REG is a cache replacemmethod based on the distance between the locationmobile host and the region of a datum. When a mohost replaces the cache, it selects a victim withlongest distance from the mobile host to the regiona datum.

Fig. 1 shows changes in the cache state accorto the movements of a mobile host to which CR_LOand CR_REG are applied when the CS is 4.

Figs. 1(a) and 1(d) show the initial states of tcache when H was located at (3,7). After H movedto (5,5), data 1, 2, 4 and 5 were stored in the ca(Figs. 1(b) and 1(e)). In Fig. 1(c), data 1, 3, 4 andremained in the cache after H moved to (7,3). Becausethe CS was 4, datum 2 had to be deleted to indatum 3. On the other hand, as in Fig. 1(f), data3, 4 and 5 remained in the cache, and datum 1deleted. Regarding the location of data, datum 1nearer than datum 2 to the location of the mobile h(7,3). Datum 2, however, which had a larger regthan datum 1, remained in the cache for a long timMoreover, datum 5 remained valuable althoughregion was small because it was on the path ofmobile host’s movement.

H.-S. Kim, H.-S. Yong / Information Processing Letters 87 (2003) 221–228 223

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Fig. 1. Changes in the cache state (H: mobile host; 1–5: data; cCR; rectangle: region of a datum; arrow: the moving path ofmobile host).

3. Various factors affecting the cache hit ratio of amobile host

We use the cache hit ratio as a criterion forefficiency of cache replacement methods. The cahit ratio is a percentage based on the number of quewhose target data is stored in the cache divided bytotal number of queries.

We selected four factors that affect the cacheratio during the performance of cache replacemmethods: the CS, the distribution of query targobjects, the density of a region and query patterns.define them below.

Definition 4 (Distribution of query target object(DTO)). The DTO is the geometrical distribution arof data required by queries performed at the currposition of the mobile host. The DTO is represenby a circle whose radius therDTO is determined bythe distance from the mobile host to the farthest daamong the required data.

The DTO means the spatial area of interesta mobile host. When we determinerDTO, we eitherapply a special value to each user or apply the svalue to all users located in a special server’s areathe former case, each mobile host analyzes the averDTO values of the historical queries of the user aapplies that value for cache management. If ususually access nearby data from the mobile host, trDTO is small. In the latter case, the server analythe historical queries, calculates the average va

environments where special patterns of queries usuoccur in accordance with the characteristics of dstored in the server.

Definition 5 (Density of a region(DR)). The DR isthe degree of density of data included in the acompared with the average density of data includethe total area managed by the server. We graded tlevels: sparse, average and dense.

To determine the DR, we divided a server’s ainto horizontal(I) × vertical (J ) blocks. When thenumber of data in the server isN , one block maycontainblock_Avg= N/(I × J ) objects on averageIf a block has more data than(1 + α) × block_Avg,we classify the block as a dense region. If a blockless than(1 − α) × block_Avg, the block is classifiedas sparse. Otherwise, it is an average region.

Definition 6 (Query pattern). A query pattern is aset of characteristics of users’ queries. We clasquery patterns as location-oriented queries or regoriented queries.

A query based on the locational property of dasuch as “How far from here to the Shin-chun stationis called a location-oriented query. A query orientedthe regional property of data, such as “Which factorhave influence on the pollution of this stream?”, isregion-oriented query.

When we perform cache replacement methodsneed to calculate therCR while considering the CS anthe characteristics of the area where the mobile hoincluded:

rCR =√

Sarea× CS

Scount× π,

whereSareais the area covered by the server’s regiScount is the number of data stored in the servedatabase andπ is the ratio of the circumference ofcircle to its diameter. The value ofπ is 3.14.

Fig. 2 shows an example in which 20 bits of da(represented by points) are distributed in a servarea whose size is 100 units (10 units× 10 units).We calculated the proper radius of the CR in texample. The CS of the mobile host located at(5,5)


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CR_LOC and CR_REG methods proposed in this pa-per, as well as the CR_LRU method, which uses the

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Fig. 2. Distribution of data in a server.

was assumed to be 5. SinceSarea is 100 andScountis 20, thereforerCR � 2.82. The inner circle, its radiuis the rCR, indicates the area that the cache ofmobile host can cover. In Fig. 2, if (a) is the farthedatum required at the current location of the mobhost, all the data required by the queries is locatethe outer circle. In this case,rDTO is 4.

4. Performance study

To validate the efficiency of the proposed cacheplacement methods, we constructed a spatial datausing Informix Spatial DataBlade Module. We evaated the performance under various conditions thafect the cache hit ratio of a mobile host, including t

Table 1Parameter list for experiments

Notation Description

CS Size of the cache (percentage of server DBrCR Radius of the CRrDTO Radius of the DTOCR_type Cache replacement methods:

CR_LRU, CR_LOC, CR_REGPath_no Moving paths of a mobile hostQuery pattern Location-oriented queries: region-oriented

queries

Table 2
Densities of paths
e

conventional cache replacement method LRU.The parameters used in our experiments are li

in Table 1.Our experiments covered a rectangular area aro

Seodaemun-Gu, 5.5 km wide and 3.5 km long. We100 m as the unit for coordinates. Consequently,total server’s area was evenly subdivided into 1,9(55 × 35) square units. The data in the server wclassified into 9 types such as post offices, schoolsstores; the number of data was 1,020. To determthe densities of regions, we subdivided the servarea into 77 (11× 7) blocks with sides 500 m longWhen α = 0.3, there were 30 sparse region bloc(39%), 26 average region blocks (34%) and 21 deregion blocks (27%). Nine paths of a straight-line tywere selected as the moving paths of a mobile hThe density of a path was calculated by the avernumber of data in the blocks that intersected withpath. When we converted the value ofblock_Avgto1.0, the density of each path was adjusted, as showTable 2.

In the experiments, we performed two typesqueries: location-oriented queries referred to thecation of a target datum for queries within the DTand region-oriented queries referred to the regiona target datum for queries that overlapped withDTO.

Three different methods based on the type of dwere used to determine the nature of each region.first region, which consisted of public offices suchpost offices and police stations, was determinedthe MBR, including the jurisdiction area. The secoregion, which consisted of types such as schoand subway stations, were assumed to be inverproportional to the number of data of the same tyfor example, since the number of schools inserver’s area was 65, the school-type region wassquare of

√1925/65≈ 5 in length. The third region

which consisted of profit-making organizations su

7

Path_no 1 2 3 4 5 6 7 8 9

Density 0.59 0.68 0.91 0.95 1.02 1.09 1.15 1.31 1.3α = 0.3 sparse path average path dense path


Table 3Parameter values for the experiments

,

Exp. 1 Exp. 2 Exp.3 Exp. 4 Exp. 5

CS 5%, 10%, 15%, 15% 5%, 10% 5%, 10%, 5%, 10%,20%, 30% 15%, 20% 15%, 20%

rCR 5, 7, 9, 11, 13 9 5, 7 5, 7, 9, 11 5, 7, 9, 11rDTO 9 5, 7, 9, 11, 13, 15 5, 7, 9, 11 7 7

CR_type CR_LRU, CR_REG CR_LOC, CR_LOC, CR_LOC,CR_LOC, CR_REG CR_REG CR_REGCR_REG

Path_no 3, 4, 6, 7, 8, 9 1, 3, 5, 8 2, 9 1∼ 9 1∼ 9

Query pattern 0:10 0:10 (a) 0:10 5:5 10:0, 7:3, 5:5Loc:Reg (b) 10:0 3:7, 0:10

as stores and hospitals, was determined by the weightfrom 5 to 15, and when therCR was 9. When therDTO
attribute; in this case we randomly assigned a weight
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was smaller than therCR, the CS of a mobile host wasies.ver,

CS

to each object.The parameter settings used in these experim

are summarized in Table 3.

4.1. Experiment 1: Cache hit ratio according toincreases in the CS using the CR_LOC, CR_REGCR_LRU methods

Our first set of experiments compared the perfmance of the CR_LOC, CR_REG and CR_LRU meods according to increases in the CS. Fig. 3 showsthe cache hit ratio increases as the CS increasesthermore, the CR_LOC and CR_REG methods copletely outperform the CR_LRU method.

Fig. 3 shows that the cache hit ratio of the CR_LRmethod increased regularly as the CS increasedthat the rate of increase of the cache hit ratiothe CR_REG method was reduced. This phenomeoccurs because when the CS of a mobile host is bigthan the total number of data located in the DTO,cache includes data less related to the current posiand a cache that includes such data has a reducedon the cache hit ratio.

4.2. Experiment 2: Cache hit ratio according to anincrease of therDTO using the CR_REG method

The DTO may be changed by characteristicsapplication programs. In experiment 2, we testedcache hit ratio as therDTO increased when regionoriented queries were performed. Fig. 4 showsresults of cache replacement when therDTO varied

t

larger than the number of data required by querConsequently, the cache hit ratio was high. Howewhen therDTO was larger than therCR, the hit ratiowas quickly reduced.

Fig. 3. Cache hit ratio of cache replacement methods as thevaries from 5 to 30%.

Fig. 4. Cache hit ratio as therDTO varies from 5 to 15 when therCRis 9.


4.3. Experiment 3: A comparison of the CR_LOC andCR_REG methods in relation to the cache hit ratio

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4.4. Experiment 4: Cache hit ratio according to anincrease in the cache requirement rate using the

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We compared the performance of two cacheplacement methods (CR_LOC and CR_REG)der more realistic environments. We selected tpaths with different densities (sparse and dense)two kinds of queries (region-oriented and locatiooriented). The CR_LOC method had a higher hittio than the CR_REG when the DR was high. Whthe DR was low, the CR_REG method had a beperformance. This result was caused by the CR_Rmethod handling more data for processing the careplacement than the CR_LOC method. Therefowhen a mobile host moves through a dense regionsize of the cache is less than the number of data inDTO of the mobile host. This situation causes frequcache replacements and lowers the cache’s efficieMoreover, Fig. 5 shows that the hit ratio is alsofected by query patterns even though they are unthe same DR. As in Fig. 5(a), when the query pattis region-oriented, the mobile host passing througdense region has nearly the same hit ratio whethuses the CR_REG or CR_LOC method. Neverthelas in Fig. 5(b), when queries performed are mailocation-oriented, a mobile host passing through deregions has a higher performance with the CR_Lmethod than with the CR_REG method. This methat for a higher cache hit ratio a mobile host needconsider query patterns when it passes through vardensities.

Fig. 5. Cache hit ratio for two different DRs under (a) rgion-oriented queries and (b) location-oriented queries.

CR_LOC and CR_REG methods

According to the results of experiments 1–3,know that the CS, DTO and DR all influence tcache hit ratio. Hence, we need a new paramthat simultaneously reflects these elements in ordegenerate a more efficient cache replacement metWe defined the cache requirement rate (CRR)tested the cache hit ratio while varying the CRRtwo cache replacement methods.

Definition 7 (Cache requirement rate(CRR)). TheCRR is a percentage derived by dividing the numof data in the DTO by the CS, as follows:

CRR= Number of data in DTO

CS× 100(%).

In Fig. 2, for example, if the number of data in thDTO is 8 and the CS is 5, the CRR is(8/5) × 100=160%; that means that 1.6 times the current CSrequired for a 100% cache hit ratio. In experimenwe compared the performance of the CR_LOC aCR_REG methods as the CRR varied under variCSs and different DRs. The results are shown in Fig

In Fig. 6, when the CRR value is small, thCR_REG method is better than the CR_LOC methand when the CRR value is large, the CR_LOmethod performs better. In addition, before the Cvalue reaches 40, the CR_REG method has a hihit ratio; after the CRR value reaches 40, the CR_Lmethod performs better. We define the cross p(CP) as the value of the CRR for whichever cacreplacement method needs to be changed.

Fig. 6. Cache hit ratio of cache replacement methods as thevaries from 15 to 129.


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Fig. 7. Change of CP values under different query patterns.

Definition 8 (The CP of the CRR). As the CRR in-creases, the more efficient cache replacement mechanges from the CR_REG method to the CR_Lmethod. We define the CP as the value of the CRRthe changeover time.

4.5. Experiment 5: Variations of the CP value baseon changes in query patterns

Our final set of experiments studied variationsthe CP value based on changes in query patterns. Fshows the results of the CPs affected by users’ qupatterns. When queries are mainly about the locaof data, the CP is low; the more the queries are abthe region of the data, the higher the CP is. The vaof the CP is determined by the characteristics of dstored in a database server and the CP is broadcfrom the server to each mobile host.

4.6. Personalized cache replacement selectionalgorithm

Our experiments show that a CS, DTO, DR aquery patterns affect the cache hit ratio. Therefoto select the optimum cache replacement metfor each mobile host, we propose an algorithm tconsiders these elements. We assume that mobileplay the main role in cache management and thatknow the CS, the DTO and users’ query patterIn addition, a server broadcasts the size of the tarea, the number and distribution of data stored inserver’s database and the standard CP values oserver area for each query pattern.

The scenario of the personalized cache replacemselection algorithm is as follows: when a mobile henters a specific server’s area (line 1), it hearssize of the total server area, the number of d

d

s

t

line1: Connect to a Server;line2: Get Server_region, Data_count, and Standard_CPs;line3: CalculaterCR;line4: Set base_CP;line5: While (Current_Position is included in Server’s area)line6: {Get Broadcasted Data;line7: Calculate Current_CRR;line8: IF (Current_CRR< base_CP);line9: Cache_Replacement_Method= CR_REG;line10: ELSE Cache_Replacement_Method= CR_LOC;

}}

Algorithm 1. Personalized cache replacement selection algorit

stored in the database and the standard CP va(line 2); it then calculates therCR using its own CSand DR (line 3) and determines the personalized bCP value that should be adapted by its own qupattern among standard CP values that have bbroadcasted (line 4); the server broadcasts the spdata information, and the mobile host hears its relalocation (line 6); for a mobile host to replace its cacit calculates the current CRR value using the CS,DTO and the DR at that position (line 7); if the curreCRR value is smaller than the base CP value, it shoselect the CR_REG method for cache replacem(line 9); otherwise, it should select the CR_LOC (li10). Algorithm 1 summarizes the process.

5. Conclusion

In this paper, we propose two new cache replament methods, the CR_LOC method and the CR_Rmethod, to efficiently support the mobility of useand the spatial properties of data for mobile computenvironments. We tested the relationship betweencache hit ratio and factors such as the CS of a mohost, the DTO, the variable density of the target regand query patterns. Finally, based on the results oexperiments, we propose a personalized cache repment selection algorithm with optimal efficiency feach mobile host in different conditions. In the futuwe will expand performance experiments to compour cache selection algorithm with other algorithmincluding FAR [8]. Moreover, we would like to stud


the directory architecture of a data server for broadcastwith respect to the spatial properties of data.

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