Tuning QoD in Stream Processing Engines

Transcription

1 Proc. 21st Australasan Database Conference (ADC 2010), Brsbane, Australa Tunng QoD n Stream Processng Engnes Mohamed A. Sharaf 1 Panos K. Chrysanths 2 Alexandros Labrnds 2 1 ECE Department, Unversty of Toronto 2 CS Department, Unversty of Pttsburgh msharaf@eecg.toronto.edu {panos, labrnd}@cs.ptt.edu Abstract Qualty of Servce (QoS) and Qualty of Data (QoD) are the two major dmensons for evaluatng any query processng system. In the context of data stream management systems (DSMSs), mult-query schedulng has been exploted to mprove QoS. In ths paper, we are proposng to explot query schedulng to mprove QoD n DSMSs. Specfcally, we are presentng a new polcy for schedulng multple contnuous queres wth the objectve of maxmzng the freshness of the output data streams and hence the QoD of such outputs. The proposed Freshness-Aware Schedulng of Multple Contnuous Queres (FAS-MCQ) polcy decdes the executon order of contnuous queres based on each query s propertes (.e., cost and selectvty) as well the propertes of the nput update streams (.e., varablty of updates). Our expermental results have shown that FAS-MCQ can mprove QoD by up to 50% compared to exstng schedulng polces used n DSMSs. Fnally, we propose and evaluate a parametrzed verson of our FAS-MCQ scheduler that s able to balance the trade-off between freshness and response tme accordng to the applcaton s requrements. Keywords: Qualty of Servce (QoS), Qualty of Data (QoD), Data Freshness, Data Stream Management Systems, Contnuous Queres, Operator Schedulng. 1 Introducton Data streams processng s an emergng research area that s drven by the growng need for montorng applcatons. A montorng applcaton contnuously processes streams of data for nterestng, sgnfcant, or anomalous events, as defned by the users. Montorng applcatons have been used n mportant busness and scentfc nformaton systems, for example, montorng network performance, real-tme detecton of dsease outbreaks, trackng the stock market, performng envronmental montorng va sensor networks, provdng personalzed and customzed Web pages. For example, consder the Unversty of Pttsburgh s Realtme Outbreak of Dsease Survellance The frst author s supported n part by the Ontaro Mnstry of Research and Innovaton Postdoctoral Fellowshp. Ths work s also partally supported by NSF under project AQSIOS (IIS ) and Career award (IIS ). Copyrght c 2010, Australan Computer Socety, Inc. Ths paper appeared at the The twenty-frst Australasan Database Conference (ADC2010), Brsbane, Australa, January Conferences n Research and Practce n Informaton Technology (CRPIT), Vol. 104, Heng Tao Shen and Athman Bouguettaya, Ed. Reproducton for academc, not-for proft purposes permtted provded ths text s ncluded. System ( Such a system receves data from dfferent sources (e.g., hosptals, clncs, pharmaces, etc.) and ntegrates t together n order to detect correlatons or abnormal events. In the event of detectng a dsease outbreak, CDC and health departments are notfed to start moblzng ther resources. Effcent employment of montorng applcatons needs advanced data processng technques that can support the contnuous processng of rapd unbounded data streams. Such technques go beyond the capabltes of tradtonal store-then-query Data Base Management Systems. Ths need has led to a new data processng paradgm and created a new generaton of data processng systems, called Data Stream Management Systems (DSMS) that support the executon of contnuous queres on data streams (Terry et al. 1992). Aurora (Carney et al. 2002), STREAM (Motwan et al. 2003), TelegraphCQ (Chandrasekaran et al. 2003), Trbeca (Sullvan 1996), Ggascope (Cranor et al. 2003), Nagara (Chen et al. 2000) and Nle (Hammad et al. 2004) are examples of current prototype DSMSs. In such systems, each montorng applcaton regsters a set of contnuous queres (CQs), where a CQ s contnuously executed wth the arrval of new relevant data (Fgure 1). In the Realtme Outbreak of Dsease System (RODS) example, health offcals regster queres for trackng specfc ndcators of dsease outbreaks by montorng multple nput data streams (e.g., prescrpton data from pharmaces). The arrval of new updates on the nput data streams trggers the executon of the regstered CQs. The output of such a frequent executon of a contnuous query s what we call an output data stream (see Fgure 1). As the amount of updates on the nput data streams ncreases and the number of regstered queres becomes large, advanced query processng technques are needed n order to effcently synchronze the results of the contnuous queres wth the avalable updates. Effcent schedulng of updates s one such query processng technque whch successfully mproves the Qualty of Data (QoD) provded by nteractve systems. QoD can be measured n dfferent dmensons such as accuracy, completeness, freshness etc. (Yeganeh et al. 2009). In ths paper, we focus on mprovng QoD n a DSMS n terms of freshness as we assume complete processng of stream data where the DSMS operates under a reasonable load wthout the need for employng load sheddng or approxmaton technques. As such, the DSMS provdes complete and accurate results that, however, mght be stale whch makes freshness the man QoD dmenson to consder for mprovement. Freshness s especally mportant, when we are nterested n an accurate vew of the current physcal 103

2 CRPIT Volume Database Technologes 2010 world, be t an outbreak of a dsease (as n the RODS system) or the detecton of traffc patterns and congestons n an urban settng durng a physcal dsaster. Such accurate vews must reflect all postve event sgnals (.e., updates) that satsfy the regstered CQs. Freshness, as well as schedulng polces for mprovng freshness, has been studed n contexts such as replcated databases (Cho & Garca-Molna 2000, 2003), Web databases (Labrnds & Roussopoulos 2001, Qu et al. 2006, Qu & Labrnds 2007), and dstrbuted caches (Olston & Wdom 2002). To the best of our knowledge, our work s the frst to study the problem of freshness n the context of data streams. In ths respect, our work can be regarded as complementary to the current work on the processng of contnuous queres, whch consders manly Qualty of Servce (QoS) metrcs lke response tme and throughput such as (Carney et al. 2003, Chandrasekaran et al. 2003, Babcock et al. 2003, Sutherland et al. 2005, Ba & Zanolo 2008, Sharaf et al. 2008). The contrbutons of ths paper are as follows: 1. We propose a polcy for Freshness-Aware Schedulng of Multple Contnuous Queres (FAS-MCQ). The proposed polcy, FAS-MCQ, has the followng salent features: It explots the varablty of the processng costs of dfferent contnuous queres regstered at the DSMS. It utlzes the dvergence n the arrval patterns and frequences of updates streamed from dfferent remote data sources. It consders the mpact of selectvty on the freshness of the output data stream. Revertng back to our RODS/event detecton example, our proposed polcy wll favor queres that lead to postve sgnals nstead of blndly processng queres that lead to negatve sgnals. 2. Beyond the basc FAS-MCQ polcy, we have also explored a weghted verson of our FAS-MCQ schedulng polcy that supports applcatons n whch queres have dfferent prortes. These prortes could reflect crtcalty, and hence ther mportance wth respect to QoD captured by freshness, or popularty, and thus be used to optmze the overall user satsfacton. 3. We study the trade-off between schedulng CQs wth the goal of mprovng QoD (usng FAS- MCQ) as opposed to schedulng to mprove QoS, whch s provded by Rate-based polces such as the ones proposed n (Sharaf et al. 2008, 2006, Urhan & Frankln 2001) 4. Fnally, we propose a parametrzed verson of our FAS-MCQ scheduler that s able to balance the trade-off between QoD and QoS accordng to the applcaton s requrements. In order to evaluate our proposed schedulng polces, we have mplemented a smulator of such DSMS scheduler and ran extensve experments. As our expermental results have shown, FAS-MCQ can mprove QoD by up to 55% compared to exstng schedulng polces used n DSMSs. FAS-MCQ acheves ths mprovement by decdng the executon order of contnuous queres based on ndvdual query propertes (.e., cost and selectvty) as well as propertes of the update streams (.e., varablty of updates). The rest of ths paper s organzed as follows. Secton 2 provdes the system model. In Secton 3, we Input Data Streams Fgure 1: queres Query Optmzer Query Scheduler Contnuous Query Q n Contnuous Query Q 1 Memory Manager Load Shedder Output Data Stream D n Output Data Stream D 1 A DSMS hostng multple contnuous defne our freshness-based QoD metrcs. Our proposed polces for mprovng freshness are presented n Secton 4. In Secton 5, we study the trade-off between QoD and QoS. Secton 6 descrbes our smulaton testbed, whereas Secton 7 dscusses our experments and results. Secton 8 surveys related work. We conclude n Secton 9. 2 System Model We assume a data stream management system (DSMS) where users regster multple contnuous queres over multple nput data streams (as shown n Fgure 1). In ths secton, we dscuss the detals of data stream processng n a DSMS, whereas n Secton 3, we defne our freshness-based QoD metrcs on data streams. In a DSMS, each nput data stream conssts of updates at remote data sources that are ether contnuously pushed to the DSMS or frequently pulled from the data sources. For example, sensor networks readngs are contnuously pushed to the DSMS, whereas updates to Web databases are frequently pulled usng Web crawlers. Each update u s assocated wth a tmestamp t. Ths tmestamp s ether assgned by the data source or by the DSMS. In the former case, the tmestamp reflects the tme when the update took place, whereas n the latter case, t represents the arrval tme of the update at the DSMS. In ths work, we assume sngle-stream queres where each query s defned over a sngle data stream. However, data streams can be shared by multple queres, n whch case each query wll operate on ts own copy of the data stream. Queres can also be shared among multple users, n whch case the results wll be shared among them. A sngle-stream query plan can be conceptualzed as a data flow dagram (Carney et al. 2002, Babcock et al. 2003),.e., as a sequence of nodes and edges, where the nodes are operators that process data and the edges represent the flow of data from one operator to another (as n Fgure 1). A query Q starts at a leaf node and ends at a root node (O r ). An edge from operator O 1 to operator O 2 means that the output of operator O 1 s an nput to operator O 2. Addtonally, each operator has ts own nput queue where data s buffered for processng. As a new update arrves at a query Q, t passes through the sequence of operators that compose Q. An update s processed untl t ether produces an output or untl t s dscarded by some predcate n 104

3 the query. An update produces an output only when t satsfes all the predcates n the query. In a query, each operator O x s assocated wth two values: processng tme or cost (c x ), and selectvty or productvty (s x ). As n tradtonal database systems, an operator wth selectvty s x n a DSMS produces s x tuples after processng one tuple for c x tme unts. s x s typcally less than or equal to 1 for operators lke flters. Selectvty expresses the behavor or power of a flter. Addtonally, for a query Q, we defne three parameters 1. maxmum cost (C ), 2. total selectvty or total productvty (S ), and 3. average cost (C avg ). For a query Q whch s composed of a stream of operators < O 1, O 2, O 3,..., O r >, the maxmum cost C, the total selectvty S and the average cost C avg are defned as follows: C = c 1 + c c r Proc. 21st Australasan Database Conference (ADC 2010), Brsbane, Australa If the tuple satsfes all the query s predcates, then D t = D +1. In ths case, the output data stream D s consdered stale durng the nterval [t, t ] as the new update occurred at tme t and t took untl tme t to append the update to the output data stream. If the tuple does not satsfy all the predcates, then D t = D. In ths case, the output data stream D s consdered fresh durng the nterval [t, t ] because the arrval of a new update has been dscarded by Q. Obvously, f there s no pendng update at the nput queue of D, then D would also be consdered fresh. Equvalently, f we vew a tuple that matches all the predcates of a query as a postve sgnal, then the current defnton of freshness measures the amount of tme that passes before the sgnal becomes vsble to the end users. Formally, to defne freshness, we consder each output data stream D as an object and F(D, t) s the freshness of object D at tme t whch s defned as follows: { 1 f u It, σ(u) s false F(D, t) = 0 f u I t, σ(u) s true (1) S = s 1 s 2... s r C avg = c 1 + c 2 s 1 + c 3 s 2 s c r s r 1...s 1 The total selectvty measures the probablty that a new update wll satsfy all the query predcates, whle the average cost measures the expected tme for processng a new update untl t produces an output or untl t s dscarded. The average cost s computed as follows. An update starts gong through the chan of operators wth O 1, whch has a cost of c 1. Wth a probablty of s 1 (equal to the selectvty of operator O 1 ) the update wll not be fltered out, and, as such, contnue on to the next operator, O 2, whch has a cost of c 2. Movng along, wth a probablty of s 2 the update wll not be fltered out, and, as such, contnue on to the next operator, O 3, whch has a cost of c 3. Up untl now, on average, the cost wll be C avg = c 1 + c 2 s 1 + c 3 s 2 s 1. Ths s generalzed n the formula for C avg above as n (Urhan & Frankln 2001). The maxmum cost s a specal case of the average cost when the selectvty of each operator n the query s 1. 3 Freshness of Data Streams In ths secton, we descrbe our proposed metrc for measurng the qualty of output data streams. Our metrc s based on the freshness of data and s smlar to the ones prevously used n (Cho & Garca- Molna 2000, Labrnds & Roussopoulos 2001, Olston & Wdom 2002, Cho & Garca-Molna 2003, Labrnds & Roussopoulos 2004). However, t s adapted to consder the nature of contnuous queres and nput/output data streams. 3.1 Average Freshness for Sngle Streams In a DSMS, the output of each contnuous query Q s a data stream D. The arrval of new updates at the nput queue of Q mght lead to appendng a new tuple to D. Specfcally, let us assume that at tme t the length of D s D t and there s a sngle update at the nput queue, also wth tmestamp t. Further, assume that Q fnshes processng that update at tme t. At ths tme we dstngush two cases: where I t s the set of nput queues n Q at tme t and σ(u) s the result of applyng Q s predcates on update u. To measure the freshness of a data stream D over an entre dscrete observaton perod from tme T x to tme T y, we have that: T y 1 F(D) = F(D, t) (2) T y T x t=t x Fgure 2 shows an example of measurng the freshness of a data stream. Specfcally, the fgure shows two output data streams; (1) the deal stream, whch shows the tmes nstants when updates became avalable at the DSMS; and (2) the actual stream, whch shows the tme nstants when updates became avalable to the user. The delay between the tme an update s avalable at the system untl the tme t s propagated to the user s composed of two ntervals: (a) the nterval where the contnuous query s watng to be scheduled for executon; and (b) the nterval where the contnuous query s processng the update. The sum of these two ntervals represents the overall nterval when the output data stream devates from the deal one. That s, when the output data stream s stale compared to the physcal world. In the example llustrated n Fgure 2, the output data stream s stale for the ntervals t 1, t 2 and t 3. Hence, the staleness of the data stream s computed as: (t 1 +t 2 +t 3 )/(T y T x ), equvalently, the freshness of the data stream s computed as: ((T y T x ) (t 1 + t 2 + t 3 ))/(T y T x ). 3.2 Average Freshness for Multple Streams Havng measured the average freshness for sngle streams, we proceed to compute the average freshness over all the M data streams mantaned by the DSMS. If the freshness for each stream, D, s gven by F(D ) usng Equaton 2, then the average freshness over all data streams wll be: F = 1 M M F(D ) (3) =1 105

4 CRPIT Volume Database Technologes 2010 Ideal output data stream Actual output data stream Fresh Stale t 1 t 2 t 3 T x T y Fgure 2: An example on measurng the freshness of a data stream 4 Freshness-Aware Schedulng of Multple Contnuous Queres In ths secton we descrbe our proposed polcy for Freshness-Aware Schedulng of Multple Contnuous Queres (FAS-MCQ). Current work on schedulng the executon of multple contnuous queres focuses manly on QoS metrcs such as response tme and throughput. Examples of such work appeared n (Chen et al. 2000, Shanmugasundaram et al. 2002, Carney et al. 2003, Chandrasekaran et al. 2003, Babcock et al. 2003, Sutherland et al. 2005, Sharaf et al. 2006, Ba & Zanolo 2008, Sharaf et al. 2008). The schedulng polces proposed n that body of work manly exploted the varablty n the output rate of dfferent CQs to mprove the provded QoS. Meanwhle, prevous work on synchronzng database updates exploted the varablty n the amount (frequency) of updates to mprove the provded QoD (Cho & Garca-Molna 2000, Olston & Wdom 2002, Cho & Garca-Molna 2003). In contrast to the work mentoned above, our proposal, FAS-MCQ, explots both the varablty n output rate as well as the amount of updates to mprove the QoD,.e., freshness, of output data streams. In our prevous work (Sharaf et al. 2005), we provde prelmnary work on mprovng the freshness of data streams. In the next sectons, we provde detaled analyss of our approach as well as extensons for handlng mult-class contnuous queres (Secton 4.4) and for effcent mplementaton (Secton 4.5). Addtonally, n Secton 5, we address the problem of balancng the trade-off between schedulng wth the goal of mprovng QoD vs. QoS. 4.1 Schedulng wthout Selectvty In order to explan the ntuton underlyng our FAS- MCQ scheduler, let us assume two queres Q 1 and Q 2, wth output data streams D 1 and D 2. Each query s composed of a set of operators, each operator has a certan cost, and the selectvty of each operator s one. Hence, we can calculate for each query Q ts maxmum cost C as shown n Secton 2. Moreover, assume that there are N 1 and N 2 pendng updates for queres Q 1 and Q 2 respectvely. Fnally, assume that the current wat tme for the update at the head of Q 1 s queue s W 1, smlarly, the current wat tme for the update at the head of Q 2 s queue s W 2. In order to determne whch of the two queres should be scheduled frst for executon, we compare two polces X and Y : Under polcy X, query Q 1 s executed before query Q 2, Under polcy Y, query Q 2 s executed before query Q 1. Under polcy X, where query Q 1 s executed before query Q 2, the total loss n freshness, L X, (.e., the perod of tme where Q 1 and Q 2 are stale) can be computed as follows: L X = L X,1 + L X,2 (4) where L X,1 and L X,2 are the staleness perods experenced by Q 1 and Q 2 respectvely. Snce Q 1 wll reman stale untl all ts pendng updates are processed, L X,1 s computed as follows: L X,1 = W 1 + (N 1 C 1 ) where W 1 s the current loss n freshness (.e., ncrease n staleness) and (N 1 C 1 ) s the tme requred to apply all the pendng updates. Smlarly, L X,2 s computed as follows: L X,2 = (W 2 + N 1 C 1 ) + (N 2 C 2 ) where W 2 s the current loss n freshness plus the extra amount of tme (N 1 C 1 ) where Q 2 wll be watng for Q 1 to fnsh executon. By substtuton n Equaton 4, we get L X = W 1 + (N 1 C 1 ) + (W 2 + N 1 C 1 ) + (N 2 C 2 ) (5) Smlarly, under polcy Y, where Q 2 s scheduled before Q 1, we have that the total loss n freshness, L Y wll be: L Y = (W 1 + N 2 C 2 ) + (N 1 C 1 ) + W 2 + (N 2 C 2 ) (6) In order for L X to be less than L Y, the followng nequalty must be satsfed: N 1 C 1 < N 2 C 2 (7) The left-hand sde of Inequalty 7 shows the total ncrease n staleness ncurred by Q 2 when Q 1 s executed frst. Smlarly, the rght-hand sde shows the total ncrease n staleness ncurred by Q 1 when Q 2 s executed frst. Hence, the nequalty mples that between the two queres, we start wth the one that has the lower N C value. Smlarly, n the general case, where there are more than 2 queres ready for executon, we start wth the one that has the lowest N C value snce t wll have the mnmum negatve mpact on the freshness of the other queres n the system. Fnally, t s worth mentonng that mnmzng the negatve mpact on the overall freshness was the same general crteron that we used n our pror work on schedulng updates over materalzed WebVews (Labrnds & Roussopoulos 2001). 106

5 4.2 Schedulng wth Selectvty Assume the same settng as n the prevous secton, wth the only dfference beng that the total productvty of each query Q s S [0, 1], whch s computed as n Secton 2. The objectve when schedulng wth selectvty s the same as before: we want to mnmze the total staleness. Recall from Inequalty 7 that the objectve of mnmzng the total loss s equvalent to selectng for executon the query that mnmzes the loss n freshness ncurred by other queres n the system. In the presence of selectvty, we wll apply the same prncple above. Towards ths, we frst need to compute for each output data stream D ts staleness probablty (P ) gven the current status of the nput data stream. Ths s equvalent to computng the probablty that at least one of the pendng updates wll satsfy all of Q s predcates. If S s the total selectvty of Q, then (1 S ) N s the probablty that all pendng updates do not satsfy Q s predcates, and hence P = 1 (1 S ) N s the staleness probablty for Q. If out of two queres Q 1 and Q 2, Q 2 s executed before Q 1, then the expected loss n freshness ncurred by Q 1 due only to the mpact of processng Q 2 frst wll be: L Q1 = P 1 N 2 C avg 2 (8) where N 2 C avg 2 s the expected tme that Q 1 wll be watng for Q 2 to fnsh executon and P 1 s the probablty that D 1 s stale n the frst place. For example, n the extreme case of S 1 = 0, f Q 2 s executed before Q 1, t wll not ncrease the staleness of D 1 snce all the updates wll not satsfy Q 1. However, at S 1 = 1, f Q 2 s executed before Q 1, then the staleness of D 1 wll ncrease by N 2 C avg 2 wth probablty one. Smlarly, f Q 1 s executed before Q 2, then the expected loss n freshness ncurred by Q 2 only due to processng Q 1 frst s computed as: L Q2 = P 2 N 1 C avg 1 (9) In order for L Q2 to be less than L Q1, then the followng nequalty must be satsfed: N 1 C avg 1 P 1 < N 2C avg 2 (10) P 2 Thus, n our proposed polcy, each query Q s assgned a prorty value V whch s the product of ts staleness probablty and the nverse of the product of ts expected cost and the number of ts pendng updates. Formally, V = 1 (1 S ) N N C avg 4.3 The FAS-MCQ Polcy (11) Our proposed polcy for Freshness-Aware Schedulng for Multple Contnuous Queres (FAS-MCQ) uses the prorty functon of Equaton 11 to determne the schedulng order of dfferent queres. Under ths prorty functon FAS-MCQ behaves as follows: 1. If all queres have the same number of pendng tuples and the same selectvty, then FAS-MCQ selects for executon the query wth the lowest cost. 2. If all queres have the same cost and the same selectvty, then FAS-MCQ selects for executon the query wth less pendng tuples. Proc. 21st Australasan Database Conference (ADC 2010), Brsbane, Australa 3. If all queres have the same cost and the same number of pendng tuples, then FAS-MCQ selects for executon the query wth hgh staleness probablty. In case (1), FAS-MCQ behaves lke the Shortest Remanng Processng Tme polcy. In case (2), FAS- MCQ gves lower prorty to the query wth hgh frequency of updates. The ntuton s that when the frequency of updates s hgh, t wll take a long tme to establsh the freshness of the output data stream. Ths wll block other queres from executng and wll ncrease the staleness of ther output data streams. In case (3), FAS-MCQ gves lower prorty to queres wth low selectvty as there s a low probablty that the pendng updates wll survve the flterng of the query operators and thus be appended to the output data stream. 4.4 Weghted Freshness In many montorng applcatons, some queres are more mportant than others. That s especally obvous n emergency systems where a few contnuous queres can be more crtcal than others. For example, under the RODS system that montors for dsease outbreaks, t s crucal to montor for sgns of waterborne dseases n areas affected by Hurrcane Katrna (and thus consder the correspondng query more crucal than the rest), whereas n other areas of the world t may be more mportant to montor for sgns of the avan flu. In cases lke these, when the system s loaded, t s necessary to maxmze the freshness of these crtcal queres. Towards handlng mult-class CQs, we modfy our proposed FAS-MCQ polcy to ncrease the freshness of data streams whch have hgher levels of mportance. Specfcally, we assgn each contnuous query Q a weght α. Ths assgned weght represents the mportance of the query and t takes values n the range (0.0, 1.0] where the weght 1.0 s assgned to the most mportant query. Hence, the objectve of our polcy would be to maxmze the overall weghted freshness. A prorty functon that allows us to maxmze the weghted freshness can be easly deduced from Equatons 8 and 9. Recall that Equaton 8 measures the expected loss n freshness experenced by Q 1 due to executng Q 2 frst, thus, the expected loss n weghted freshness experenced by Q 1 s measured as: WL Q1 = α 1 P 1 N 2 C avg 2 Smlarly, the expected loss n weghted freshness experenced by Q 2, when Q 1 s executed frst, s measured as: WL Q2 = α 2 P 2 N 1 C avg 1 In order for WL Q2 to be less than WL Q1, the followng nequalty must be satsfed: N 1 C avg 1 P 1 α 1 < N 2C avg 2 P 2 α 2 Then, the prorty assgned to each query s computed as: V = α (1 (1 S ) N ) N C avg (12) The weghts of the queres can be explctly or mplctly defned, dependng on the applcaton. For example, n the case of an applcaton that ncludes queres that are crtcal, the crtcal queres can be explctly assgned hgher weghts than the rest of the queres. In applcatons where explct crtcalty/mportance nformaton s not gven, an mplct 107

6 CRPIT Volume Database Technologes 2010 measure of mportance can be derved. For example, the popularty of each query (.e., the number of users that regstered that query) can be used as the weght. In such an applcaton, the weghted FAS- MCQ polcy wll provde hgh levels of overall user satsfacton n terms of QoD (freshness). Fnally, t s worth mentonng that the weght gven to a query can be dynamc; for example, t can change dependng on the tme of day or the day of the week (e.g., for traffc management queres). 4.5 Implementng the FAS-MCQ Scheduler The FAS-MCQ Scheduler s nvoked at every schedulng pont and uses the current values for N and S to compute the prorty of each query Q, accordng to Equaton 11. In our mplementaton of the FAS- MCQ polcy, a schedulng pont s reached when a query fnshes executon. In order to keep the schedulng overhead low when computng prortes, we use a Calendar Queue (Brown 1988) for prorty management. Calendar queues have been wdely used for mplementng prorty-based schedulng algorthms n hgh-speed networks as well as n the Aurora DSMS (Carney et al. 2003). A calendar queue s an O(1) prorty queue, based on the dea of Bucket Sort. Specfcally, the calendar queue s structured as buckets where each bucket corresponds to a class of prortes. To nsert an element n the calendar queue, a hash functon s used to map ts prorty to the correspondng bucket. To retreve elements from the calendar queue, buckets are traversed n order. A calendar queue allows us to avod re-computng the prortes of queres that receved no new updates between consecutve schedulng ponts. Addtonally, for queres wth new updates, the amortzed cost of updatng the prorty s of O(1). 5 Schedulng for QoD vs. Schedulng for QoS In ths secton we dscuss the dfference n behavor between schedulng wth the goal of mprovng QoD as opposed to schedulng wth the goal of mprovng QoS (.e., when the objectve s to mnmze the average response tme). We also present a parametrzed verson of our FAS-MCQ scheduler that balances the trade-off between both the QoD and QoS metrcs. 5.1 Schedulng for QoS In tradtonal DBMSs, the response tme of a query s defned as the amount of tme from the tme the query arrves at the system untl the tme when the last tuple of the result s produced. The defnton of response tme above captures the behavor of DBMSs whch respond to the arrval of queres. In contrast, DSMSs respond to the arrval of new data. Hence, n a DSMS, t s more approprate to defne response tme from the perspectve of data (nstead of queres). Therefore, we defne tuple response tme as follows: Defnton 1 Tuple response tme, T, for tuple s T = D A, where A s the tuple arrval tme and D s the tuple departure tme. Accordngly, the average response tme for N tuples s: 1 N N T. Under ths defnton, tuples that are fltered out durng query processng do not contrbute to the overall response tme metrc (Tan & DeWtt 2003). The Rate-based (RB) polcy has been shown to mprove the average response tme of a sngle multstream query wth jon operators (Urhan & Frankln 2001). In the basc RB polcy, each operator path wthn a query s assgned a prorty that s equal to ts producton rate. The path wth the hghest prorty s the one scheduled for executon. In our prevous work (Sharaf et al. 2006, 2008), we generalze the basc Rate-based strategy for schedulng multple contnuous queres wth the objectve of mnmzng the average response tme. That s, multple contnuous queres are scheduled for executon based on ther output rates. In ths paper, we call that extended verson of RB as Rate-based for Multple Contnuous Queres (). Under, at each schedulng pont we select for executon the CQ wth the hghest prorty (.e., output rate). Specfcally, under, each query Q has a value called the global output rate (GR ) whch s defned n terms of the parameters of the CQ operators. The output rate of a query Q, composed of the operators < O 1, O 2, O 3,..., O r >, s bascally the expected number of tuples produced per tme unt due to processng one tuple by the operators along the query all the way to the root O r. Formally, or, equvalently, GR = S C avg GR = 1 (1 S ) C avg (13) (14) where S and C avg are the CQ s expected selectvty and expected cost as defned n Secton Balancng the Trade-off between QoD and QoS In ths secton, we present our approach for balancng the trade-off between QoS and QoD. In partcular, we propose a tunable verson of our FAS-MCQ scheduler that balances the trade-off between the provded response tme and data freshness n a data stream management system. Towards tunng the trade-off n the perceved performance, we argue that the dstncton between schedulng for QoD and QoS s easly dentfed by comparng the prorty functons used by FAS-MCQ (Equaton 11) versus the one used by (Equaton 14). Specfcally, the schedulng decson made by FAS-MCQ consders three factors: (1) cost (2), selectvty, and (3) number of pendng tuples, whereas consders only the frst two factors. As a result, FAS-MCQ mght favor a query wth a relatvely expensve cost and very few pendng tuples as opposed to whch mght favor an nexpensve query wth a large number of pendng tuples. In such case, may be appendng tuples faster to the output data streams (.e., provdng low response tme), however, the appended tuples would be stale most of the tme. On the other hand, FAS- MCQ mght be relatvely slower n appendng tuples to the output data streams (.e., provdng hgh response tme) yet would mantan most of those output data streams as fresh as possble. Gven the above observatons, we propose a parametrzed verson of FAS-MCQ that balances the trade-off between the acheved QoD and QoS. We wll refer to ths polcy as FAS-MCQ(β), where β s a parameter that specfes the weght gven to the number of pendng tuples (.e., N) when computng the prorty of a query. Formally, under FAS-MCQ(β) each query Q s assgned a prorty value V whch s computed as follows: V = 1 (1 S ) Nβ N β Cavg (15) 108

7 The parameter β takes values n the range [0.0,1.0] and t acts as a knob for shapng the system s behavor. For nstance, for β = 0.0, FAS-MCQ(0.0) behaves lke the polcy descrbed above, whereas for β = 1.0, reverts to the orgnal FAS-MCQ descrbed n Secton 4. For settngs where 0.0 < β < 1.0, the system acheves the desred balance between QoD and QoS. 6 Evaluaton Testbed We have conducted several experments to compare the performance of our proposed schedulng polcy and ts senstvty to dfferent parameters. Specfcally, we compared the performance of our proposed FAS-MCQ polcy to a two-level schedulng scheme from Aurora where Round Robn s used to schedule queres and ppelnng s used to process updates wthn the query. Collectvely, we refer to the Aurora scheme n our experments as. We also ncluded the polcy (Sharaf et al. 2006) descrbed n Secton 5 as well as a polcy where updates are processed accordng to ther arrval tmes. Queres: We smulated a DSMS that hosts 250 regstered contnuous queres. The structure of the query s adapted from (Chen et al. 2002, Madden et al. 2002) where each query conssts of three operators: two predcates and one projecton. Real Data Streams: We use the LBL-PKT-4 traces from the Internet Traffc Archve 1. The traces contan an hour s worth of all wde-area traffc between the Lawrence Berkeley Laboratory and the rest of the world. In our experments, we use the TCP and UDP packet traces as 2 nput data streams to the system where the regstered queres are unformly assgned to any of the 2 data streams. Synthetc Data Streams: In ths settng, we generate 10 nput data streams each of length 10K tuples. Intally, we generate the updates for each stream accordng to a Posson dstrbuton, wth ts mean nter-arrval tme set accordng to the smulated system utlzaton (or load). For a utlzaton of 1.0, the nter-arrval tme s equal to the expected tme requred for executng the queres n the system, whereas for lower utlzatons, the mean nter-arrval tme s ncreased proportonally. Moreover, to stress test the system, we generate a back-log of updates where we traverse the Posson stream and group together every 10 consecutve tuples n a burst settng the arrval tme of all tuples that belong to the same burst to be equal to that of the frst tuple n the burst. In the default settng, 5 out of the 10 data streams are bursty. Selectvtes: In any query, the selectvty of the projecton s set to 1, whle the two predcates have the same value for selectvty, whch s selected usng a Zpf dstrbuton from the range [0.1, 1.0]. The Zpf dstrbuton s defned usng a Zpf parameter whch determnes the degree of skewness. In our settng, the skewness s toward queres wth selectvty equal to 1.0 and n the default settng the Zpf parameter s set to 0.0 (.e., unform dstrbuton). Costs: All operators that belong to the same query have the same cost, whch s unformly selected from three possble classes of costs. The cost of an operator n class s equal to: K 2 tme unts, where [0 2] and K s the scalng factor whch s used to scale the costs of operators to meet the desred utlzaton. For syntheszed data, K s equal to 1. For the network traces, we measure the nterarrval tme of the data trace, then we set K so that the rato between the total expected costs of queres 1 Proc. 21st Australasan Database Conference (ADC 2010), Brsbane, Australa Average Staleness 60% 50% 40% 30% 20% 10% 0% System Utlzaton Fgure 3: Average staleness vs. system utlzaton Average Response Tme System Utlzaton RB FAS Fgure 4: Average response tme vs. system utlzaton and the nter-arrval tme s equal to the smulated utlzaton. Fnally, the cost of each of the calendar queue operatons s equal to the cost of the cheapest operator n the system. Table 1 summarzes our smulaton parameters and settngs. 7 Experments In ths secton, we present a representatve sample of the expermental results obtaned under the settngs descrbed n Secton Impact of Utlzaton Fgure 3 depcts the average total staleness over all output data streams as the utlzaton of the DSMS ncreases. In ths settng, we use the basc verson of FAS-MCQ whch s equvalent to settng β to 1.0 n FAS-MCQ(β). The fgure shows that, n general, the staleness of the output data streams ncreases wth ncreasng load. It also shows that the FAS-MCQ polcy provdes the lowest staleness for all values of utlzaton wth beng the closest contender. Addtonally, the relatve mprovement provded by FAS-MCQ compared to ncreases wth ncreasng utlzaton. For nstance, at 0.1 utlzaton, FAS-MCQ acheves 30% reducton n staleness compared to, whereas at 0.95 utlzaton, RB- MCQ provdes a 16% staleness whle FAS-MCQ reduces the staleness to 10% (.e., a 40% mprovement). As expected, the reducton n staleness provded by FAS-MCQ comes at the expense of an ncrease n response tme whch s llustrated n Fgure 4. The fgure shows that, lke staleness, the response tme ncreases wth ncreasng load. Moreover, t shows FAS RB 109

8 CRPIT Volume Database Technologes 2010 Parameter Value Polces FAS-MCQ,,, Number of Queres 250 Number of Operators per Query 3 Operators Costs 1K, 2K, 4K Operators Selectvtes Utlzaton Data Streams real and synthetc Number of Data Streams 2 (real) and 10 (synthetc) Number of Bursty Streams 0 10 Table 1: Smulaton Parameters Average Response Tme Average Staleness % 18% 16% 14% 12% 10% 8% 6% 4% 2% FAS-MCQ(0.0) FAS-MCQ(0.25) FAS-MCQ(0.5) FAS-MCQ(0.75) System Utlzaton Fgure 5: Response tme for dfferent βs FAS-MCQ(0.0) FAS-MCQ (0.25) FAS-MCQ (0.5) FAS-MCQ (0.75) FAS-MCQ (1.0) Average Response Tme FAS-MCQ(0.75) FAS-MCQ(0.5) % 12% 14% 16% 18% 20% Average Staleness Decreasng β FAS-MCQ(0.25) FAS-MCQ(0.0) Fgure 7: Trade-off between staleness and response tme at utlzaton 0.95 Average Staleness 60% 50% 40% 30% 20% 10% 0% System Utlzaton 0% Selectvty Skewness Fgure 6: Staleness for dfferent βs that reduces the response tme compared to FAS-MCQ. For example, at 0.95 utlzaton, the response tme provded by FAS-MCQ s 23% hgher than that of (at a 40% mprovement n staleness compared to as prevously shown n Fgure 3). The trade-off between QoD (.e., freshness) and QoS (.e., response tme) s further llustrated usng Fgures 5 and 6 as explaned next. 7.2 Staleness vs. Response Tme Fgures 5 and 6 show the average staleness and average response tme for the same smulaton settngs used n the prevous experment. In addton to llustratng the dfference n behavor between and FAS-MCQ, the fgures also show the performance of the parametrzed FAS-MCQ(β) polcy. Fgure 5 shows how the response tme of FAS-MCQ decreases by decreasng the value of β down to β = 0.0. Notce that at β = 0.0, the response tme of FAS-MCQ(0.0) s just slghtly hgher than whch s due to the schedulng overheads. On the other hand, Fg- Fgure 8: Staleness vs. skewness n selectvty ure 5 shows the reducton n staleness wth ncreasng values of β. To better assess the magntude of the trade-off, we plot the performance of the dfferent polces at utlzaton 0.95 n Fgure 7. For nstance, the fgure shows that reduces the staleness by 40% whle ncreasng the response tme by 23%, whereas FAS-MCQ(0.25) reduces the staleness by 20% and ncreases the response tme by 14%. 7.3 Impact of Selectvty Fgure 8 shows the average staleness for an experment where all operators have the same cost, utlzaton s set to 95%, and the skewness of selectvty s varable. Recall that we control the degree of skewness usng a Zpf parameter. Specfcally, settng the Zpf parameter to 0.0 results n a unform dstrbuton of selectvty, whereas by the ncreasng ts value the dstrbuton s skewed towards hgh values. That s, most of the regstered CQs are productve. 110

9 Staleness of FAS-MCQ normalzed to Number of Bursty Streams Fgure 9: Staleness vs. number of bursty streams (out of 10) Average Staleness 100% 80% 60% 40% Proc. 21st Australasan Database Conference (ADC 2010), Brsbane, Australa At that pont, FAS-MCQ reduces the staleness by 40%. After that pont, the performance of the two polces gets closer. The explanaton s that at a moderate number of bursty streams (up to 5 streams for ths settng), FAS-MCQ has a better chance to fnd a query wth a short queue of pendng updates to schedule for executon. As the number of bursty streams ncreases, the chance of fndng such a query decreases, and as such, s performng reasonably well. For nstance, at 10 bursty streams, FAS-MCQ reduces the staleness by only 22% compared to. 7.5 Real Data Fgure 10 shows the results for our fnal experment where we use real network traces. The selectvtes and costs of operators are the same as n the frst experment. In Fgure 10, the behavor of the dfferent schedulng algorthms s consstent wth the prevous experments, where FAS-MCQ provdes the lowest staleness followed by, then and. Addtonally, t shows the relatvely hgh values of staleness exhbted by all polces, whch s explaned by the fact that the two traces are hghly bursty, reflectng an ON/OFF traffc pattern. 8 Related Work 20% 0% System Utlzaton Fgure 10: Staleness vs. system utlzaton (real data traces) Fgure 8 shows that by ncreasng the skewness, the staleness provded by all polces ncreases. Ths s because when most of the queres have hgh selectvty, then the arrval of new updates wll render the output data streams stale most of the tme. The fgure also shows that the gans provded by FAS-MCQ compared to ncrease wth ncreasng the skewness. For nstance, at 0.0, FAS-MCQ reduces the staleness by 40% compared to. Ths reducton goes up to 55% when the dstrbuton s hghly skewed. The reason s that at a hghly skewed dstrbuton, all queres wll have the same cost and most of them wll have the same selectvty, hence, for RB- MCQ most queres wll have the same prorty. That s n contrast wth FAS-MCQ whch wll utlze the extra nformaton provded by the number of pendng tuples to dfferentate between queres and to assgn hgher prortes to queres that feed a stale stream or a stream that could be quckly brought to freshness. 7.4 Impact of Bursts The settng for ths experment s the same as the default one. The DSMS utlzaton, however, s set to 95% at all ponts. Addtonally, we plot the average staleness as the number of bursty nput streams ncreases as shown n Fgure 9. For nstance, at a value of 0, all the arrvals follow a Posson dstrbuton wth no bursts, whereas at 10, all nput streams are bursty. Fgure 9 shows the staleness of FAS-MCQ normalzed to that of. Hence, the smaller the value the bgger the reducton. The fgure shows that as the number of bursty streams ncreases, the reducton n staleness provded by FAS-MCQ compared to RB- MCQ ncreases up untl there are 5 bursty streams. Improvng the QoS of multple contnuous queres has been the focus of many research efforts. For example, mult-query optmzaton has been exploted n (Chen et al. 2000) to mprove the system throughput n an Internet envronment and n (Madden et al. 2002) for mprovng the throughput of a data stream management system. Mult-query schedulng has been exploted by Aurora to acheve better response tme or to satsfy applcaton-specfed QoS requrements (Carney et al. 2003). The work n (Babcock et al. 2003) employs a scheduler for mnmzng the memory utlzaton. Moreover, balancng the trade-off between multple QoS metrcs has been studed n (Sutherland et al. 2005, Ba & Zanolo 2008) To the best of our knowledge, no prevous work has proposed mult-query schedulng polces for mprovng the QoD provded by contnuous queres. Load sheddng, however, has been devsed as a technque to control the degree of degradaton n the provded QoD under overloaded condtons. Several load sheddng technques have been proposed n (Tatbul et al. 2003, Babcock et al. 2004, Tatbul & Zdonk 2006, Tatbul et al. 2007) Schedulng polces for mprovng the QoD have been studed n the context of replcated databases and n web databases. For example, the work n (Cho & Garca-Molna 2000, 2003) provdes polces for crawlng the Web n order to refresh a local database, where t made the observaton that a data tem that s updated more often should be synchronzed less often. The same observaton has been exploted n (Olston & Wdom 2002) for refreshng dstrbuted caches and n (Lam & Garca-Molna 2003) for mult-castng updates. In ths paper, we utlze the same observaton for mprovng data stream freshness. Our work, however, makes the schedulng decson based on the current status of the DSMS queues (.e., the number of pendng updates) as opposed to assumng a mathematcal model for updates as n (Cho & Garca- Molna 2000, 2003). The work n (Labrnds & Roussopoulos 2001) studes the problem of propagatng the updates to derved vews. It proposes a schedulng polcy for applyng the updates that consders the dvergence 111

10 CRPIT Volume Database Technologes 2010 n the computaton costs of dfferent vews. Smlarly, our proposed FAS-MCQ consders the dfferent processng costs of the regstered multple contnuous queres. Moreover, FAS-MCQ generalzes the work n (Labrnds & Roussopoulos 2001) by consderng updates that are streamed from multple data sources wth dfferent traffc patterns as opposed to a sngle data source. Fnally, the problem of consderng user preferences to manage the trade-off between QoS and QoD has been addressed n the context of web databases. The work n (Qu et al. 2006) provded an admsson control framework, whereas the work n (Qu & Labrnds 2007) ntroduced a two-level schedulng algorthm to address the problem. 9 Conclusons Motvated by the need to support montorng applcatons whch nvolve the processng of update streams by contnuous queres, n ths paper we studed the dfferent aspects that affect the QoD of these applcatons. In partcular, we focused on the freshness of the output data stream and dentfed that both the propertes of queres,.e., cost and selectvty, and the propertes of the nput update streams,.e., varablty of updates, have a sgnfcant mpact on freshness. Our major contrbuton s a new approach to schedulng multple queres n Data Stream Management Systems. Our approach explots the propertes of the contnuous queres as well as the nput data streams n order to maxmze the freshness of output streams. We proposed a new schedulng polcy called Freshness-Aware Schedulng of Multple Contnuous Queres (FAS-MCQ) and a weghted verson of t that supports applcatons wth mult-class queres. We also ntroduced a generalzed varant of FAS-MCQ that balances the trade-off between QoD and QoS, accordng to applcaton requrements. We have expermentally evaluated our proposed FAS-MCQ polcy aganst schedulng polces used n current DSMS prototypes as well as Web servers. 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