A New Quality of Service Metric for Hard/Soft Real-Time Applications

Transcription

1 A New Qualty of Servce Metrc for Hard/Soft Real-Tme Applcatons Shaoxong Hua and Gang Qu Electrcal and Computer Engneerng Department and Insttute of Advanced Computer Study Unversty of Maryland, College Park, MD 2742 {shua, Abstract Real-tme applcatons often have mxed hard and soft deadlnes, can be preempted subject to the cost of context swtchng or the restart of computaton, and have varous data dependency. The smple but wdely used task completon rato, as the Qualty of Servce (QoS) metrc, does not capture these characterstcs and can not reflect user perceved QoS well. In ths paper, we propose a new quanttatve QoS metrc, whch s based on task completon rato but dfferentates hard and soft deadlnes and models data dependency as well. Bascally, t assgns dfferent weghts to hard and soft deadlne tasks, penalzes late soft task completon, and measures the tasks affected by any dropped tasks. We apply popular onlne schedulers, such as (earlest deadlne frst), FCFS (frst come frst serve), and (least executon tme frst), on a set of smulated MPEG moves at the frame level and for each applcaton compare the new QoS measurement, tradtonal completon rato wth the real completon rato whch consders the number of correctly decoded frames and has been mapped to the user perceved QoS well. Expermental results show that our proposed QoS metrc can reflect real lfe QoS much better than the tradtonal one.. Introducton Wth the ncreasng popularty of real-tme multmeda and wreless communcaton applcatons, qualty of servce (QoS) attracts a lot of attenton n a number of research and development communtes, n partcular the network routng and multmeda delvery. Provdng the requred QoS guarantees are vtal for desgn of the embedded systems that carry out such applcatons. The most popular way to specfy tme-related QoS requrements, such as synchronzaton and latency, s deadlne. Hard deadlnes are the deadlne constrants that must be satsfed n order to provde QoS guarantees. However, as the applcaton-drven system desgn keeps on pushng for hgh performance, lght weght, low energy consumpton, better portablty, and so on, t becomes tough to meet these more and more system resource demandng QoS requrements. For example, one would lke to vew hgh-resoluton moves one after another on a DVD player, but t cannot be done wthout rechargng the battery. Consequently, many real-tme applcatons have soft deadlnes. Falure to meet the soft deadlnes wll degrade the QoS wthn an acceptable range. For nstance, many MPEG vdeo applcatons such as vdeo conferences requre relable communcaton and consstently hgh throughput, whle beng able to tolerate reasonable amount of packet error, jtter, or unsynchronzaton. Mssng some frame s (soft) processng deadlne wll also be acceptable. Soft deadlnes can also be found n many other applcatons such as web browsng and fle transfer. In the other hand data dependency exsts n many applcatons. The smple but wdely used task completon rato, as the Qualty of Servce, assumes that the tasks are ndependent and equally mportant. It cannot capture the hard/soft deadlne and data dependency and does not reflect the user perceved QoS well. These observatons lead us to a new measure for QoS. Any task completon before ts deadlne should contrbute to the QoS; any task completon after ts soft deadlne may also contrbute to the QoS, but subject to a penalty for mssng the deadlne. Furthermore, the task drops wll affect some other tasks based on data dependency. Therefore, we defne QoS as a weghted sum of the percentage of completed tasks, the penalty for completng tasks after ther soft deadlnes and the penalty for task drops. The paper s structured as follows. In Secton 2 we brefly survey the exstng work on QoS modelng. Secton 3 proposes a novel quanttatve measurement for QoS based on task completon rato wth consderaton of penaltes for task drops and soft deadlne msses. Secton 4 presents and analyzes the experment results. The paper s concluded and some future envsoned research ssues are presented n Secton 5. Proceedngs of the Internatonal Conference on Informaton Technology: Computers and Communcatons (ITCC 3)

2 2. Related work Varous QoS requrements, such as bounded delay, mnmal throughput, guaranteed synchronzaton or resoluton, task completon rato, were frst addressed n the network and real-tme operatng systems (RTOS) communtes. Lawrence dscusses the metrcs based on the QoS attrbutes of tmelness, precson, and accuracy that can be used for system specfcaton, nstrumentaton, and evaluaton [6]. Altmann and Varaya defne QoS as a combnaton of the basc qualty metrcs for the network layer: delay, jtter, bandwdth, and relablty []. The most formally sound and practcally relevant QoS model n the networkng communty s proposed by Cruz [4]. The model s based on the demand curves and servce curves. The man conceptual result n RTOS lterature s presented by Rajkumar et al. n [8]. They ntroduce an analytcal approach for satsfyng multple QoS dmensons under a gven set of resource constrants. They show that the problem s NP-hard and develop an approxmaton polynomal algorthm for the problem by transformng t nto a mxed nteger programmng problem [9]. Comprehensve survey of QoS research n these two areas s gven n [2]. Recently, Ng et al. report some research results on the QoS of MPEG vdeo as perceved by Human bengs. They derve a new metrc QoS- Human for measurng the QoS of MPEG vdeo of three common types of contents [7]. 3. QoS model We consder that a sngle processor system serves realtme applcatons, each applcaton conssts of a sequence of tasks, and each task s characterzed by ts arrval tme a, deadlne d (whch can be ether hard or soft), and executon tme e. The lfetme of a task wth hard deadlne s the perod [a,d] between ts arrval tme and deadlne. The length of ts lfetme, d-a, s normally referred as latency. We wll represent a task by <a,d,e,h/s>. A task s completed f t receves vald servce tme n the amount of ts requred executon tme, before the deadlne for hard deadlne task. A task has a hard deadlne f t must be completed before the deadlne otherwse the system wll not get the reward for servng the task and the applcaton. A task has a soft deadlne f the system can stll beneft even f the deadlne s mssed, subjected to a deadlne-mss penalty. A task s non-preemptve means that once the task gets the CPU, t wll occupy the CPU untl ts deadlne or completon, whchever comes earler. A task s preemptve means that the task may lose control of the CPU durng ts executon, but when t gets the CPU back, t can resume the nterrupted executon. A task s sem-preemptve f t can be preempted but must be restarted nstead of resumng the remanng workload due to the hgh cost of context swtch. That s, any ncomplete computaton becomes nvald and wll be dscarded. For example, multmeda embedded systems may not have suffcent memory to store all the ntermedate results and would restart the computaton should preempton occurs. The completon rato, whch s the percentage of how much tasks have been completed by the system, does not gve an accurate measure for the QoS n that ) t does not dstngush hard deadlne tasks and soft deadlne tasks on whch the system may get dfferent amount of reward; 2) t does not dstngush tasks completed before ther soft deadlnes and those that are completed but mss the deadlnes; and 3) t does not reflect data dependency among tasks snce all deadlne msses are treated n the same way. Based on these observatons, we defne QoS as follows: Suppose that a scheduler S completes K h hard-deadlne tasks and K s soft-deadlne tasks out of a total of N tasks, the QoS provded by such scheduler s: αsks + αhkh β δ γ QS ( ) = N N d a N () where α s and α h are the weghts for soft deadlne tasks and hard deadlne tasks; β s the penalty parameter or the tolerance factor for deadlne mssng; δ s the dfference between the task s deadlne and completon tme when the (soft) deadlne s mssed; d - a s the lfe tme of the task; γ s penalty parameter for task droppng; s the number of tasks that wll be affected f the -th task s dropped( =); the frst sum s taken over all the completed tasks that mss ther soft deadlnes; and the second sum s taken over all the dropped tasks regardless of ther deadlne type. The QoS defned n () s a drect extenson of completon rato, n the case when there s no penalty of mssng soft deadlnes (β=) or droppng tasks (γ=) and hard deadlne tasks are consdered equally mportant as soft ones (α s =α h =), whch has been used for QoS measurement n many occasons. Soft deadlnes and hard deadlnes are treated dfferently by assgnng them dfferent weghts α s and α h. Soft deadlne mssng s penalzed by the relatve amount that the deadlne has been mssed wth the penalty factor β. Data dependency s captured n the last term by reducng QoS n the amount of tasks dependng on the dropped tasks wth a penalty factor γ. Proceedngs of the Internatonal Conference on Informaton Technology: Computers and Communcatons (ITCC 3)

3 Move Table. Statstcs of the MPEG streams we generated n the smulaton. ( µ s the mean and σ s the standard devaton. ) Numbers of frames I-to-I I-to-P I-frame sze P-frame sze B-frame sze µ σ µ σ µ σ Wzard of OZ 4, Star Wars 74, Scence of the Lambs 39, Goldfnger 4, Expermental results In ths secton, we report the setup of our smulaton on MPEG moves and comparson of dfferent QoS measurements such as our proposed new QoS metrc, the tradtonal completon rato and the real completon rato whch consders the number of correctly decoded frames and has been mapped to the user perceved QoS well. Our results ndcate that the new QoS metrc can reflect the user perceved QoS much better than the tradtonal completon rato. 4.. Smulaton of MPEG streams We have appled popular onlne schedulers, such as, FCFS, and (least executon tme frst), on MPEG vdeo streams decodng at the frame level. Standard MPEG encoders generate three types of compressed frames: I frames (ntra-pctures), P frames (predcted pctures) and B frames (b-drectonal predcted pctures) []. In general, encoders use a fxed GOP (Group of Pctures) pattern when compressng a vdeo sequence. A typcal GOP n dsplay order and decodng order s shown as n Fgure I P B 2 B 3 P 4 B 5 B 6 P 7 B 8 B 9 I B B 2 decodng order I B 2 B 3 P B 5 B 6 P 4 B 8 B 9 P 7 B B 2 I dsplay order Fgure. A typcal GOP pattern(i-to-i=2, I-to-P=3) On average, I frames are the largest n sze (snce they are self-contaned), followed by P frames and B frames. Krunz and Trpath present a comprehensve model for MPEG vdeo streams [5]. The model captures the bt-rate varatons at multple tme scales. Statstcally, the generated MPEG streams ft the emprcal vdeo and are suffcently accurate n predctng the queung performance for real vdeo streams. From the parameters on four moves gven n [5], we smulate ther frame nformaton that s reported n Table. (The frame sze of I-frames has a relatvely large standard devaton because t s modeled by the sum of two random components). Based on the frame sze and type, we generate the normalzed executon tme for each frame usng a lnear model of MPEG decodng [3]. In the smulaton we assume that the nter-arrval tme of frames are ndependent wth exponental dstrbuton. Its mean s approxmately equal to the recprocal of frame dsplay rate (n terms of frame per second (fps)) to generate a balanced loaded system. We smulate underloaded and overloaded systems by varyng the fps requrement. The deadlne for decodng each frame s set correspondng to the arrval tme and the frame dsplay rate. We use several standard dsplay rates n our smulaton: 5, 24, 3, 45 and 6 fps. The deadlne type s assgned to each ndvdual frame based on the dependency of dfferent frames. I frame s the most mportant, because the correct processng of all the P frame and B frame n the same GOP depends on the completon of the correspondng I frame. P frame s also mportant because t s requred by the followng P and B frames n the same GOP. We assgn I and P frames hard deadlnes rather than gvng them soft deadlnes. We also assgn soft deadlnes to B frames to create tasks wth mxed type of deadlnes. Each GOP can be vewed as one applcaton ndependent of others as the correct decodng of all the frames n one GOP depends on the leadng I frame. Each applcaton conssts of a set of tasks (frame decodng) and the drop of hard deadlne I and P frames wll cause the ncorrect decodng of the remanng frames n ths applcaton. To better model the data dependency among tasks, we assgn dfferent values I and P,, whch are correspondng to the number of frames that wll not be decoded correctly because of a dropped frame, to frames wth hard deadlnes. For example f I-to-I, the number of frames between two Proceedngs of the Internatonal Conference on Informaton Technology: Computers and Communcatons (ITCC 3)

4 consecutve I frames (see Fgure ), s, then we assgn I =; P, are assgned, 7, and 4 for the three P frames n the GOP pattern based on Fgure ; and B = because there s no frame depends on B frame. As a result, I frames have hgher prorty than P and B frames, P frames have hgher prorty than B frames. Ths exactly matches the MPEG decodng mechansm. In sum, we use the followng QoS, based on formula () wth consderaton of MPEG applcaton s characterstcs, n our smulaton: ( I I,, ) K n K + K β s δ γ P s h Q ( s) = m + m MPEG P P N N = T N = d Where T the recprocal of frame dsplay rate; d, the number of tasks that wll be affected I p, P, f the I frame or P frame s dropped ; m, m the number of droppng I, P frames; n I P the number of P frames n a GOP pattern; K, K, β, γ, δ, N are same as n (). s 4.2. Smulaton results h (2) We have mplemented popular onlne schedulng algorthms, such as, FCFS and, and appled them to the smulated MPEG moves. For each move, we smulate underloaded, balanced, and overloaded systems by changng the frame rate from 5 fps, to 24, 3, 45, and 6 fps. And for each case, we consder the case of non-preemptve, preemptve, and sem-preemptve. Now we detal the smulaton results. For underloaded system wth a frame rate of 5fps and/or 24fps, the deadlnes are relatvely loose and we observe that almost all the algorthms acheve the maxmal QoS n the amount of wthout task drop and deadlne mssng. In ths case, our proposed QoS metrc s same as the tradtonal completon rato. However, when the computaton load ncreases, the system becomes balanced and overloaded eventually. Fgure 2-4 shows the relatonshp of tradtonal completon rato, our proposed new QoS metrc and real completon rato, whch consders the actual number of correctly decoded frames and has been mapped to the user perceved QoS well under dfferent onlne schedulng polces ( and ) on dfferent moves n the case of nonpreemptve, preemptve and sem-preemptve for overeloaded system. In the smulaton we assume that the frames arrve n the decoder order, so and FCFS have the same results. From these fgures we can see that the tradtonal completon rato doesn t reflect the real completon Tradtonal completon rato New QoS metrc "real" completon rato Fgure 2. Comparson of three dfferent QoS measurements under and on move Slence of the Lambs n the frame rate of 45 fps n the case of, from left to rght, nonpreemptve, preemptve, and sem-preemptve Tradtonal completon rato New QoS metrc "real" completon rato Fgure 3. Comparson of three dfferent QoS measurements under and on move Wzard of OZ n the frame rate of 3 fps n the case of, from left to rght, non-preemptve, preemptve, and sem-preemptve Tradtonal completon rato New QoS metrc "real" completon rato Fgure 4: Comparson of three dfferent QoS measurements under and on move Goldfnger n the frame rate of 3 fps n the case of, from left to rght, non-preemptve, preemptve, and sem-preemptve. Proceedngs of the Internatonal Conference on Informaton Technology: Computers and Communcatons (ITCC 3)

5 Tradtonal completon rato New QoS metrc "real" completon rato Fgure 5. Comparson of three dfferent QoS measurements under and on move Star Wars n the frame rate of 45 fps n the case of, from left to rght, non-preemptve, preemptve, and sem-preemptve. rato well. For example, n the Fgure 2, although the tradtonal completon rato s hgh, around 8%, the real completon rato s less than 2% under and only around 4% under. Compared wth tradtonal completon rato, our proposed new QoS metrc s much closer to the real completon rato and reflects the user perceved QoS well. So t s necessary to develop low overhead onlne scheduler to maxmze ths new QoS metrc n order to eventually mprove the user perceved QoS wthout usng extra hardware. From these fgures we also can observe that the real completon rato under s consstently better than that under. The reason s that, n general, the executon tme of B frame s shorter than that of I or P frame, therefore, for the polcy t prefers to select B frame whch actually s the least mportant task. But the tradtonal completon rato under may be better than that under, so the concluson s that t s crucal to fnsh mportant tasks as many as possble, not the raw counter of task completons. For new QoS metrc there s not clear relatonshp between and because t s affected by several factors such as the number of completed soft and hard tasks, the soft deadlne mssng, the task drop and the selecton of penalty parameter β and γ. Fnally we can see that the QoS acheved n the case of non-preemptve s mostly hgher than that acheved n the preemptve case and much hgher than that acheved n the sem-preemptve case. 5. Conclusons Wth the ncreasng popularty of real-tme multmeda and wreless communcaton applcatons, qualty of servce (QoS) attracts a lot of attenton. In ths paper we present a new metrc on how to measure the QoS provded by an embedded system to real-tme applcatons wth mxed hard and soft deadlnes. It captures the mxed hard and soft deadlne nature of such applcaton, consders the nneglgble preempton cost, and models data dependency. We apply popular onlne schedulers, such as, FCFS and, on a set of smulated MPEG moves at the frame level and fnd that for each applcaton compared wth the tradtonal completon rato, our new QoS metrc s much closer to the real completon rato whch consders the number of correctly decoded frames. And ths ndcates the new QoS metrc can reflect user perceved QoS much better than the tradtonal completon rato. Further work s requred to develop some new onlne schedulers wth low runtme overhead to maxmze the proposed new QoS n order to acheve better user perceved QoS wthout usng extra hardware. We also antcpate desgnng systems that provde the same QoS guarantees wth less system resources such as CPU, power, and memory. 6. References [] J. Altmann and P. Varaya, INDEX project: user support for buyng QoS wth regard to user's preferences, Sxth Internatonal Workshop on Qualty of Servce (IWQoS'98), 998, pp. -4. [2] C. Aurrecoechea, A.T. Campbell, and L. Hauw, A survey of QoS archtectures, Multmeda Systems, Vol.6, No.3, May 998, pp [3] A. C. Baver, A. B. Montz, and L. L. Peterson, Predctng MPEG Executon Tmes, ACM SIGMETRICS 98, June 998, pp [4] R. L. Cruz, Qualty of Servce Guarantees n Vrtual Crcut Swtched Networks, IEEE Journal on Selected Areas n Communcatons, Vol.3, No.6, August 995, pp [5] M. Krunz and S.K. Trpath, On the characterzaton of VBR MPEG streams, SIGMETRICS 97, 997, pp [6] T. F. Lawrence, The qualty of servce model and hgh assurance, Proceedngs Hgh-Assurance Engneerng Workshop, 997, pp [7] J. K. Ng, K. Leung et al., A scheme on measurng MPEG vdeo QoS wth human perspectve, Proceedngs of the 8 th Internatonal Conference on RTCSA, 22, pp [8] R. Rajkumar, C. Lee, J. Lehoczky, and D. Seworek, A resource allocaton model for QoS management, Proceedngs. IEEE Real-Tme Systems Symposum, 997, pp [9] R. Rajkumar, C. Lee, J. Lehoczky, and D. Seworek. Practcal Solutons for QoS-based Resource Allocaton Problems, Proceedngs. The 9th IEEE Real-Tme Systems Symposum, 998, pp [] L. Texera and M. Martns, Vdeo compresson: The MPEG Standards, Proceedngs ECMAST 96, 996, pp Proceedngs of the Internatonal Conference on Informaton Technology: Computers and Communcatons (ITCC 3)