The Schedulability Region of Two-Level Mixed-Criticality Systems based on EDF-VD

Size: px
Start display at page:

Download "The Schedulability Region of Two-Level Mixed-Criticality Systems based on EDF-VD"

Transcription

1 The Schedulablty Regon of Two-Level Mxed-Crtcalty Systems based on EDF-VD Drk Müller and Alejandro Masrur Department of Computer Scence TU Chemntz, Germany Abstract The algorthm Earlest Deadlne Frst wth Vrtual Deadlnes (EDF-VD was recently proposed to schedule mxedcrtcalty task sets consstng of hgh-crtcalty ( and lowcrtcalty ( tasks. EDF-VD dstngushes between and mode. In mode, the tasks may requre executng for longer than n mode. As a result, n mode, EDF- VD assgns vrtual deadlnes to tasks (.e., t unformly downscales deadlnes of tasks to account for an ncrease of workload n mode. Dfferent schedulablty condtons have been proposed n the lterature; however, the schedulablty regon to fully characterze EDF-VD has not been nvestgated so far. In ths paper, we revew EDF-VD s schedulablty crtera and determne ts schedulablty regon to better understand and desgn mxed-crtcalty systems. Based on ths result, we show that EDF-VD has a schedulablty regon beng around 85% larger than that of the Worst-Case Reservatons (WCR approach. Keywords-real-tme schedulng; mxed crtcalty; EDF-VD; resource effcency I. INTRODUCTION In real-tme systems, deadlnes need to be guaranteed. To ths end, a task model s used based on whch a schedulablty analyss s performed. Usually, ths task model conssts of a mnmum nter-arrval tme and a worst-case executon tme (WCET []. However, t s a challenge to determne the WCET of a task [2]. For the known two basc approaches, there s a trade-off between safety and resource utlzaton. Statc methods yeld safe results [2]. However, they are often pessmstc n terms of resource utlzaton. On the other hand, measurement-based methods allow for a better resource utlzaton, but do not provde any guarantees on the safety of results snce t s nearly mpossble to cover all executon paths by measurement. Recently, technques have been proposed combnng statc and measurement-based approaches [3]. Nevertheless, the problem of fndng a proper trade-off between safety and resource utlzaton remans an open challenge. Mxed-crtcalty (MC systems are characterzed by schedulng tasks of dfferent mportance (.e., dfferent crtcalty. In ths paper, we consder two such levels: lowcrtcalty ( level, and hgh-crtcalty ( level. All jobs of tasks must be certfed to meet ther deadlnes under all possble crcumstances, whereas those of tasks are not subject to such a certfcaton [4]. Clearly, to certfy a task, statc methods should be used to determne the safe WCETs of tasks. However, as mentoned before, ths leads to a conservatve estmaton and tends to overdesgn. To allow for an ncreased resource effcency, optmstc (measurement-based WCET estmatons can be used. In the latter case, addtonal provsons have to be made n order to take potental WCET overruns of jobs of tasks nto account. Towards ths, the semnal algorthm Earlest Deadlne Frst wth Vrtual Deadlnes (EDF-VD was recently proposed [5]. EDF-VD dstngushes between two modes: and mode. In mode, tasks are scheduled together wth tasks, where optmstc WCETs are used for the tasks. In mode, only the tasks are allowed to run usng ther conservatve WCETs. Here, the algorthm EDF-VD dscards all tasks n order to accommodate an ncrease n the executon demands of tasks. Dfferent schedulablty condtons have been publshed for EDF-VD [5] [6]. However, a full characterzaton of ts schedulablty regon has not been proposed so far. In ths paper, we revew EDF-VD s schedulablty crtera and determne ts schedulablty regon to fully characterze ts behavor. Schedulablty regons have been already used n the lterature n order to better understand and compare schedulablty crtera [7] [8]. Smlarly, n ths work, we compare EDF-VD wth the well-known Worst-Case Reservatons (WCR approach both graphcally and analytcally based on schedulablty regons. A. Contrbutons In ths secton, we state the contrbutons of ths paper. These can be summarzed n the followng manner: We revew the schedulablty crtera of EDF-VD from the lterature and derve a new, more ntutve, schedulablty condton. We determne EDF-VD s schedulablty regon whch allows us to better understand MC systems. We compare schedulablty regons of EDF-VD and WCR both n an analytcal and graphcal manner. We show that EDF-VD s schedulablty regon s around 85% larger than that of WCR /DATE4/ c 204 EDAA

2 B. Structure of the Paper The rest of ths paper s structured as follows. Related work s dscussed n Secton II. Next, Secton III explans the task and system model. We shortly revst WCR n Secton IV. Secton V ntroduces EDF-VD and proposes a new upper bound on the desgn parameter deadlne scalng factor. Next, EDF-VD s necessary and suffcent schedulablty condtons are dscussed n Sectons VI and VII. Based on ths, we study the EDF-VD schedulablty regon n Secton VIII and compare t to the one of WCR. Fnally, some examples and concludng remarks wll be gven n Sectons IX and X. II. RELATED WORK Mxed-crtcalty schedulng was frst proposed under ths name by Vestal [4]. Baruah et al. later analyzed per-task prorty assgnments and the resultng response tmes for sporadc task sets [9]. In [5], Baruah et al. proposed the EDF-VD algorthm to schedule MC sporadc task sets wth prorty promoton by scalng deadlnes of tasks. The speed-up factor of.69 (by roundng up the golden rato 5+ 2 proposed n [5] was later mproved to 4/3 n [6]. For multprocessor MC schedulng, a parttoned and a global schedulng approach both based on EDF-VD are proposed n [0]. Accordng to ths work, parttoned schedulng behaves better than the global approach for MC task sets. Further, n [], Pathan studes global MC schedulng wth task-level fxed prortes and gves a schedulablty test based on response tme analyss for > 2 crtcalty levels. A more flexble approach wth per-task deadlne scalng factors was presented by Ekberg and Y [2]. However, ths approach makes t hard to derve utlzaton and speed-up bounds due to the bg number of scalng factors,.e., tunng parameters, nvolved. Recently, other mprovements compared to EDF-VD were proposed. In [3], Su and Zhu used an elastc task model [4] [5] to mprove resource utlzaton n MC systems. Fnally, n [6], Zhao et al. appled preempton thresholds [7] n MC schedulng n order to better utlze the processor. III. TASK AND SYSTEM MODEL We consder n ndependent sporadc tasks τ wth ther nstances or jobs separated by a mnmum nter-arrval tme T under preemptve, mplct-deadlne (.e., : D T where D s a task s relatve deadlne, unprocessor schedulng. There s no self-suspenson, and context-swtch overheads are assumed to be zero. In ths paper, only dual-crtcalty systems wth two levels of crtcalty are examned. In ths MC settng, an addtonal task parameter s requred: crtcalty denoted by χ {, }. For tasks, there s one WCET C. Opposed to ths, tasks are provded wth an optmstc WCET C and a conservatve WCET C beng C C. As mentoned before, the system operates n two modes: and mode. Intally, we assume the mode m of the system to be m where all tasks are scheduled. As soon as a job of a task executes for longer than ts C, the system swtches to mode where only the subset of tasks are subject to ther deadlnes. All actve jobs of tasks are mmedately dscarded and no further such jobs wll be allowed to run. In a short notaton, a task s then specfed as (T, C whle a task s expressed as (T, (C, C. We defne the utlzaton parameters wth χ, m {, } where χ denotes task crtcalty and m mode. u m χ : χ χ C m T ( Note that among the four potental crtcalty-to-mode combnatons, only u, u and u are defned. u does not exst snce there s no C specfed for tasks. Snce C C holds for all for whch χ,.e., the optmstc WCET s always less than or equal to the conservatve WCET of all tasks, we obtan the followng constrant on utlzatons u and u. u u (2 IV. WORST-CASE RESERVATIONS In the smple strategy of WCR, the optmstc C of tasks n mode s replaced by the conservatve C. It then apples the well-known EDF schedulng strategy. Ths way, we obtan (3 as an exact schedulablty condton for the WCR approach usng []. u + u (3 The WCR approach assumes that tasks always requre ther maxmum WCETs. Ths s pessmstc (wth respect to EDF-VD snce t gnores the fact that, n mode, the optmstc WCETs could be used. V. THE EDF-VD ALGORITHM The algorthm EDF-VD [5] s an adapton of the classc EDF [] to MC schedulng. Its basc dea s to promote jobs n mode by shortenng ther relatve deadlnes n order to reserve processor tme for the mode. The swtch from to mode s trggered by a job exceedng ts C. The EDF-VD approach s to scale these relatve deadlnes homogeneously n mode. That s, D x T wth x [0, ] for all. D s referred to as vrtual deadlne and s used to schedule tasks n mode. Opposed to ths, there s no deadlne scalng for tasks, they keep ther orgnal deadlnes D. In mode, agan the orgnal deadlnes D are used to schedule tasks accordng to EDF. As mentoned above, tasks are dscarded n mode. Note that EDF reles on absolute deadlnes such that usng vrtual deadlnes n mode mght lead to nconsstences.

3 Based on these consderatons, Baruah et al. [6] found a utlzaton bound of 3/4, see (4 below. max ( u + u, u 3/ (4 A lower bound (5 of the scalng factor x can be used as a suffcent condton for meetng all deadlnes n mode. u u x (5 An upper bound (6 of x can be found by consderng the transton between and mode [6]. Ths serves as a suffcent schedulablty condton for the mode. x u u A. A Straghtforward Upper Bound on the Scalng Factor Let us assume that the system swtches from to mode at a tme t recall ths happens when one of the tasks executes for C tme wthout sgnalng end of executon. As a result, no tasks wll be executed after t. In addton, snce (5 holds, u + u x also holds and t can be guaranteed that all tasks execute for C tme wthn ther correspondng vrtual deadlnes (.e., D x T. Let us assume that only one task causes the change to mode,.e., t has executed for C at t wthout sgnalng ts end. Now, snce (5 holds guaranteeng the schedulablty of all tasks n mode, t has to be less than or equal to the vrtual deadlne of the job trggerng the mode swtch. Otherwse (5 would not hold. In worst case, t s exactly equal to the deadlne of that job. For ths job to fnsh before ts deadlne n mode, t has to execute C tme wthn T x T,.e., wthn an nterval equal to the dfference between ts orgnal and vrtual deadlne. As a result, the followng nequaltes must hold. C T xt C ( xt The worst-case stuaton happens when all tasks swtch to mode smultaneously 2 at tme t and hence the followng must hold for all of them to be schedulable, cf. [5]. u x (6 x u (7 On the other hand, a job has already executed for C t and, hence, t only has to execute for addtonal C tme wthn T x T (nstead of C at C as assumed n [5]. As a result, the followng must hold. 2 Even f only one task trggers the mode at a tme, other tasks mght also be arbtrarly close to trggerng t. C C C T xt C ( xt Agan, the worst-case stuaton happens when all tasks swtch to mode smultaneously at tme t. As a result, we obtan the followng expresson. u u x x ( u u VI. NECESSARY SCHEDULABLITY CRITERIA FOR EDF-VD Snce EDF-VD apples tradtonal EDF n both and mode, two necessary EDF-VD schedulablty condtons (9 and (0 can be drectly derved usng the exact EDF unprocessor utlzaton bound []. (8 u + u (9 u (0 VII. SUFFICIENT SCHEDULABLITY CRITERIA FOR EDF-VD Unfortunately, condtons (9 and (0 are not suffcent for EDF-VD to be schedulable snce these dsregard the schedulablty of mode transtons. The exstence of a lower and an upper bound on the scalng factor x allows us to derve a thrd condton to guarantee schedulablty of mode transtons. Note that ths s possble snce the upper bound on x see (8 s obtaned takng mode transtons nto account. If a vald x exsts, ( must hold from (5 and (8. u u u + u ( Condton ( can be easly shown to be equvalent (.e., f one of them holds, then all of them hold to (2 as gven n [6] and to (3 as proposed n [0] see Appendx A. u u u u (2 u u (u u (3 Note that (2 s based on (5 and (6, whle (3 results from solvng (2 for u. Another suffcent schedulablty condton (4 results from combnng the more conservatve upper bound of (7 wth (5 as proposed n [5]. u u u (4

4 A. EDF-VD VIII. THE SCHEDULABILITY REGION Fg. llustrates EDF-VD s schedulablty regon. The abscssas represent values of u whereas the ordnates represent values of u. Clearly, snce (9 must hold for EDF- VD to be schedulable, the schedulablty regon of EDF-VD must be below the lne crossng by (u, u 0 and (u 0, u. As dscussed prevously, the utlzaton bound of EDF-VD s 3/4 as shown n (4. Ths s represented by the lower lne crossng by (u 3/4, u 0 and (u 0, u 3/4 n Fg.. That s, utlzaton values that are below ths lower lne are schedulable wthout needng further tests. Clearly, from (4, nothng can be concluded f the utlzaton s between 3/4 and,.e., for the secton between the lower and the upper lne n Fg.. To characterze the schedulablty regon between these 3/4. Agan, ths s the maxmum value of u by whch schedulablty s guaranteed by (4. Now, replacng ths value nto ( and solvng for u, we obtan (5 note that ths can also be obtaned by replacng 3/4 and solvng for u n (2 or (3. two lnes, let us set u u u u 4u (5 Inequalty (5 expresses how u vares wth respect to u under a fxed u 3/4. Ths s a hyperbolc functon of u as shown n Fg.. All pars of u and u below ths curve are schedulable under EDF-VD, whereas values of u and u above t cannot be guaranteed to be schedulable. Snce condton (2 must hold, the hyperbolc curve s truncated at u u 3/4. EDF-VD s schedulablty regon s gven by the area below the truncated hyperbolc curve. It should be notced that the area below the lne gven by (4 s fully contaned below the truncated hyperbolc curve. Now, consderng u 3/4 and solvng for u n (4 we obtan (6 llustrated n Fg.. B. WCR /4 u 4 u (6 Let us now draw the schedulablty regon for WCR. To ths end, we set u 3/4 and solvng for u n (3 we obtan (7, whch s a constant as llustrated n Fg.. u 3/4 (7 Ths means that all pars (u, u are schedulable under WCR f u s not greater than /4. Ths results n the area to the left of u /4 as llustrated n Fg.. Agan, snce condton (2 must hold, WCR s schedulablty regon s lmted from above by u u 3/4. u u Fgure. u WCR's schedulablty regon Border of EDF-VD's schedulablty regon Border of conservatve EDF-VD schedulablty regon Model constrant (2 +56% Suffcent condton (3 Suffcent cond. (4, Utlzaton bound Necessary condton (9 u u lne Suffcent condton (20, especally (5 Suffcent condton (6 0.5 u u Schedulablty Regons of EDF-VD & WCR for u 3/4 C. Comparng EDF-VD and WCR Clearly, EDF-VD outperforms WCR. That s, f a set of MC tasks s schedulable under WCR, t wll also be schedulable under EDF-VD; cf. Fg., where WCR s schedulablty regon s fully contaned by EDF-VD s one. In ths secton, we further make a quanttatve comparson between these two algorthms. To ths end, let us assume that u u holds. Replacng ths n (5 and reshapng we obtan a quadratc expresson n u. 4(u 2 + u 0 (8 Equalzng (8 to zero, we can compute ts roots and take the postve one as gven by (9. u (9 8 Hence, for u u and u 3/4, a set of MC tasks s schedulable under EDF-VD f u s at most On the other hand, for WCR, we obtan from (3 that u Note that ths s more than 56% more allowable utlzaton for EDF-VD compared to WCR. The above comparson s vald for the partcular case where u 3/4. To obtan a more general comparson of EDF-VD wth WCR, let us reshape (2 as follows note that ( or (3 can also be reshaped to ths form. u ( u ( u u (20 Inequalty (20 descrbes the general case for u. There, we have a scalng by u nstead of /4 as n (5. By decreasng u from 3/4 towards 0 note that must stll hold, the boundary of EDF-VD s u u

5 Eff WCR to EDF VD Fgure u Effcency of WCR vs. EDF-VD dependng upon u schedulablty regon (20 approaches the necessary condton ( (9. The corner cases at (u 0, u and u u, u u reman. The advantage of EDF-VD compared to WCR worsens wth a decreasng u snce, n ths case, u approaches u and, hence, makes reservatons less wasteful. Clearly, ncreasng u from 3/4 towards mproves the advantage of EDF-VD over WCR 3. In general, the area of a schedulablty regon s a measure of performance for a gven schedulng algorthm 4. Thus, the area rato of two schedulablty regons allows quantfyng the advantage or effcency of one algorthm wth respect to another, see also [8] and [8]. Here, we determne the area rato of WCR over EDF-VD. From Fg., WCR s regon (a rectangle has an area of (2. u ( u (2 The EDF-VD schedulablty regon comprses the one of WCR plus the area under a hyperbolc curve gven by (20. Accordng to Fg. and (20, the dfference n the area can be calculated as the defnte ntegral (22. A EDF-VD u ( ( u u u du ( u ( ln( u + u (22 Fnally, (2 and (22 gve the area rato of WCR over EDF- VD as a functon of u (23, cf. Fg. 2, see Appendx B. Eff WCR-to-EDF-VD For u A EDF-VD u ln( u (23 0, WCR s as effcent as EDF-VD. Wth 3 Increasng u beyond 3/4 makes the hyperbolc curve (20 cross the utlzaton bound lne gven by (4. Hence, condtons (2, ( and (3 outperform (4. That s, a u greater than 3/4 s rendered unfeasble by (4; however, ths mght stll be feasble as per (, (3 or (4. 4 Assocated wth a schedulablty condton. ncreasng u, WCR s effcency decreases. Up to u 0.9, ths decrease s almost lnear wth a slope of 2/3. From 0.9 onwards, WCR s relatve performance rapdly falls to zero at u. Whle WCR mght be acceptable for small u values, hgh u values call for EDF-VD. For u 3/4, EDF-VD s advantage to WCR s obtaned from (23 by takng the nverse, gvng ca..85. EDF-VD has a schedulablty area ca. 85% greater than WCR s. IX. EXAMPLES Frst, let us consder, from [3], the task set {(8, 2, (30, 3, (0, (2, 4, (25, (4, 0}. Usng (, we obtan u 0.35, u 0.36 and u 0.8. The necessary condtons (9 and (0 are met wth 0.7 and 0.8. In ths example, u + u.5 > holds whch calls for EDF-VD. (5 and (6 gve an nterval of [36/65, 4/7] or ca. [0.5538, 0.574] for EDF-VD s scalng factor x. Our approach (8 easly gves the sound result of 0.56 whch s n the mddle of the nterval and, thus, ncreases tolerance to arrval tme jtter. We take {(6, 2, (0, (, 2, (20, (2, 0} as a 2nd example. Based on (, u /3, u 0.2 and u 0.7 are obtaned. The necessary condtons (9 and (0 are met wth 8/ and 0.7. But, u + u 3/ > holds, agan callng for EDF-VD. We obtan a mn. scalng factor (5 of x 0.3. The max. scalng factor (6 s x 0.9. Our new approach (8 returns 0.5 for x (n the mddle of the nterval, mprovng jtter tolerance. X. CONCLUDING REMARKS In ths paper, we obtaned the schedulablty regon of EDF-VD. Based on ths, we showed how dfferent schedulablty condtons for EDF-VD relate to each other and whch among them are the most accurate. In addton, we compared the schedulablty regons of EDF-VD and WCR n a graphcal and analytcal manner. We have shown analytcally that the area of the schedulablty regon of EDF- VD s ca. 85% larger than that of WCR. Our technque to compare schedulablty regons s general enough and can be used to analyze the performance of schedulng algorthms and schedulablty crtera n other domans. REFERENCES [] C. L. Lu and J. W. Layland, Schedulng algorthms for multprogrammng n a hard-real-tme envronment, J. ACM, vol. 20, no., pp. 46 6, Jan [2] R. Wlhelm, J. Engblom, A. Ermedahl, N. Holst, S. Thesng, D. Whalley, G. Bernat, C. Ferdnand, R. Heckmann, T. Mtra, F. Müller, I. Puaut, P. Puschner, J. Staschulat, and P. Stenström, The worst-case executon-tme problem - overvew of methods and survey of tools, ACM Trans. Emb. Comp. Sys., vol. 7, pp. 36: 36:53, May [3] M. Lv, W. Y, N. Guan, and G. Yu, Combnng abstract nterpretaton wth model checkng for tmng analyss of multcore software, n RTSS, IEEE 3st, 200, pp

6 [4] S. Vestal, Preemptve schedulng of mult-crtcalty systems wth varyng degrees of executon tme assurance, n RTSS, 28th IEEE, 2007, pp [5] S. K. Baruah, V. Bonfac, G. D Angelo, A. Marchett- Spaccamela, S. Van Der Ster, and L. Stouge, Mxedcrtcalty schedulng of sporadc task systems, n 9th Eur. conf. on Alg., ser. ESA. Sprnger, 20, pp [6] S. Baruah, V. Bonfac, G. D Angelo, H. L, A. Marchett- Spaccamela, S. van der Ster, and L. Stouge, The preemptve unprocessor schedulng of mxed-crtcalty mplct-deadlne sporadc task systems, n ECRTS, 24th, 202, pp [7] E. Bn and G. C. Buttazzo, Schedulablty analyss of perodc fxed prorty systems, IEEE Trans. Comput., vol. 53, no., pp , Nov [8] D. Müller and M. Werner, Quantfyng the Advantage of EDF vs. RMS Schedulablty on a Unprocessor Usng a Dfferental Analyss and a Power-law Total Utlzaton Dstrbuton, n ISORC, 6th, Jun [9] S. Baruah, A. Burns, and R. Davs, Response-tme analyss for mxed crtcalty systems, n RTSS, 20, pp [0] S. Baruah, B. Chattopadhyay, H. L, and I. Shn, Mxedcrtcalty schedulng on multprocessors, Real-Tme Systems, pp. 36, 203. [] R. Pathan, Schedulablty analyss of mxed-crtcalty systems on multprocessors, n ECRTS, 202, pp [2] P. Ekberg and W. Y, Boundng and shapng the demand of mxed-crtcalty sporadc tasks, n ECRTS, 202, pp [3] H. Su and D. Zhu, An elastc mxed-crtcalty task model and ts schedulng algorthm, n DATE. EDA Consortum, 203, pp [4] T.-W. Kuo and A. K. Mok, Load adjustment n adaptve real-tme systems, n RTSS, Dec.. 99, pp [5] G. Buttazzo, G. Lpar, and L. Aben, Elastc task model for adaptve rate control, n RTSS, 9th, 998, pp [6] Q. Zhao, Z. Gu, and H. Zeng, PT-AMC: Integratng Preempton Thresholds nto Mxed-Crtcalty Schedulng, n DATE, 203, pp [7] Y. Wang and M. Saksena, Schedulng fxed-prorty tasks wth preempton threshold, n RTCSA, 999, pp [8] A. Baston, B. B. Brandenburg, and J. H. Anderson, Cacherelated preempton and mgraton delays: Emprcal approxmaton and mpact on schedulablty, n 6th Int l Workshop on OS Platforms for Emb. RT Appl., Jul. 200, pp APPENDIX A. Proof of Equvalence of Inequaltes (, (2 and (3 Frst we show by equvalent transformatons that ( mples (2 and vce versa. u u u u u u + u ( u u u u u u u : u u u u u Next, t s shown that ( and (3 are equvalent. u u u u u + u u u u u u + u u u + u ( u + u u + u + u u + u ( u ( u u u + u Fnally, by transtvty also (2 and (3 are equvalent. B. Dervaton of the WCR-to-EDF-VD-Effcency Formula Frst, the defnte ntegral of (22 s calculated usng the antdervatve. ( ( u u A EDF-VD u u ( u u u + u du du ln u + ( u u ] u [( u ( u (( u ( ln u + ( u ( u ( u ( ln( u + u Fnally, ths result and (2 lead to (23. Eff A EDF-VD + A ( EDF-VD u u ( ( u u + u ( ln( u + u ( u u ( + + ln( u ( u ln( u +u u ( u u u u ln( u

Real-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling

Real-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling Real-Tme Systems Multprocessor schedulng Specfcaton Implementaton Verfcaton Multprocessor schedulng -- -- Global schedulng How are tasks assgned to processors? Statc assgnment The processor(s) used for

More information

Partitioned Mixed-Criticality Scheduling on Multiprocessor Platforms

Partitioned Mixed-Criticality Scheduling on Multiprocessor Platforms Parttoned Mxed-Crtcalty Schedulng on Multprocessor Platforms Chuanca Gu 1, Nan Guan 1,2, Qngxu Deng 1 and Wang Y 1,2 1 Northeastern Unversty, Chna 2 Uppsala Unversty, Sweden Abstract Schedulng mxed-crtcalty

More information

Embedded Systems. 4. Aperiodic and Periodic Tasks

Embedded Systems. 4. Aperiodic and Periodic Tasks Embedded Systems 4. Aperodc and Perodc Tasks Lothar Thele 4-1 Contents of Course 1. Embedded Systems Introducton 2. Software Introducton 7. System Components 10. Models 3. Real-Tme Models 4. Perodc/Aperodc

More information

Two Methods to Release a New Real-time Task

Two Methods to Release a New Real-time Task Two Methods to Release a New Real-tme Task Abstract Guangmng Qan 1, Xanghua Chen 2 College of Mathematcs and Computer Scence Hunan Normal Unversty Changsha, 410081, Chna qqyy@hunnu.edu.cn Gang Yao 3 Sebel

More information

Improved Worst-Case Response-Time Calculations by Upper-Bound Conditions

Improved Worst-Case Response-Time Calculations by Upper-Bound Conditions Improved Worst-Case Response-Tme Calculatons by Upper-Bound Condtons Vctor Pollex, Steffen Kollmann, Karsten Albers and Frank Slomka Ulm Unversty Insttute of Embedded Systems/Real-Tme Systems {frstname.lastname}@un-ulm.de

More information

Resource Reservation for Mixed Criticality Systems

Resource Reservation for Mixed Criticality Systems Resource Reservaton for Mxed Crtcalty Systems Guseppe Lpar 1, Gorgo C. Buttazzo 2 1 LSV, ENS - Cachan, France 2 Scuola Superore Sant Anna, Italy Abstract. Ths paper presents a reservaton-based approach

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound

Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound Fxed-Prorty Multprocessor Schedulng wth Lu & Layland s Utlzaton Bound Nan Guan, Martn Stgge, Wang Y and Ge Yu Department of Informaton Technology, Uppsala Unversty, Sweden Department of Computer Scence

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks

Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks 1 Utlzaton-Based Schedulng of Flexble Mxed-Crtcalty Real-Tme Tasks Gang Chen, Nan Guan, D Lu, Qngqang He, Ka Huang, Todor Stefanov, Wang Y arxv:1711.00100v1 [cs.dc] 29 Sep 2017 Abstract Mxed-crtcalty models

More information

The optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

Lecture 4. Instructor: Haipeng Luo

Lecture 4. Instructor: Haipeng Luo Lecture 4 Instructor: Hapeng Luo In the followng lectures, we focus on the expert problem and study more adaptve algorthms. Although Hedge s proven to be worst-case optmal, one may wonder how well t would

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems

Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems Unversty of Pennsylvana ScholarlyCommons Publcly Accessble Penn Dssertatons 2017 Resource-Effcent Schedulng Of Multprocessor Mxed-Crtcalty Real-Tme Systems Jaewoo Lee Unversty of Pennsylvana, jaewoo@cs.upenn.edu

More information

Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound

Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound Fxed-Prorty Multprocessor Schedulng wth Lu & Layland s Utlzaton Bound Nan Guan, Martn Stgge, Wang Y and Ge Yu Department of Informaton Technology, Uppsala Unversty, Sweden Department of Computer Scence

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Improving the Sensitivity of Deadlines with a Specific Asynchronous Scenario for Harmonic Periodic Tasks scheduled by FP

Improving the Sensitivity of Deadlines with a Specific Asynchronous Scenario for Harmonic Periodic Tasks scheduled by FP Improvng the Senstvty of Deadlnes wth a Specfc Asynchronous Scenaro for Harmonc Perodc Tasks scheduled by FP P. Meumeu Yoms, Y. Sorel, D. de Rauglaudre AOSTE Project-team INRIA Pars-Rocquencourt Le Chesnay,

More information

Limited Preemptive Scheduling for Real-Time Systems: a Survey

Limited Preemptive Scheduling for Real-Time Systems: a Survey Lmted Preemptve Schedulng for Real-Tme Systems: a Survey Gorgo C. Buttazzo, Fellow Member, IEEE, Marko Bertogna, Senor Member, IEEE, and Gang Yao Abstract The queston whether preemptve algorthms are better

More information

Affine transformations and convexity

Affine transformations and convexity Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Quantifying the Sub-optimality of Uniprocessor Fixed Priority Pre-emptive Scheduling for Sporadic Tasksets with Arbitrary Deadlines

Quantifying the Sub-optimality of Uniprocessor Fixed Priority Pre-emptive Scheduling for Sporadic Tasksets with Arbitrary Deadlines Quantfyng the Sub-optmalty of Unprocessor Fxed Prorty Pre-emptve Schedulng for Sporadc Tasksets wth Arbtrary Deadlnes Robert Davs, Sanjoy Baruah, Thomas Rothvoss, Alan Burns To cte ths verson: Robert Davs,

More information

Last Time. Priority-based scheduling. Schedulable utilization Rate monotonic rule: Keep utilization below 69% Static priorities Dynamic priorities

Last Time. Priority-based scheduling. Schedulable utilization Rate monotonic rule: Keep utilization below 69% Static priorities Dynamic priorities Last Tme Prorty-based schedulng Statc prortes Dynamc prortes Schedulable utlzaton Rate monotonc rule: Keep utlzaton below 69% Today Response tme analyss Blockng terms Prorty nverson And solutons Release

More information

Efficient Feasibility Analysis for Real-Time Systems with EDF scheduling*

Efficient Feasibility Analysis for Real-Time Systems with EDF scheduling* Effcent Feasblty Analyss for Real-Tme Systems wth EF schedulng* Karsten Albers, Frank Slomka epartment of omputer Scence Unversty of Oldenburg Ammerländer Heerstraße 114-118 26111 Oldenburg, Germany {albers,

More information

Real-Time Operating Systems M. 11. Real-Time: Periodic Task Scheduling

Real-Time Operating Systems M. 11. Real-Time: Periodic Task Scheduling Real-Tme Operatng Systems M 11. Real-Tme: Perodc Task Schedulng Notce The course materal ncludes sldes downloaded from:! http://codex.cs.yale.edu/av/os-book/! and! (sldes by Slberschatz, Galvn, and Gagne,

More information

Parametric Utilization Bounds for Fixed-Priority Multiprocessor Scheduling

Parametric Utilization Bounds for Fixed-Priority Multiprocessor Scheduling 2012 IEEE 26th Internatonal Parallel and Dstrbuted Processng Symposum Parametrc Utlzaton Bounds for Fxed-Prorty Multprocessor Schedulng Nan Guan 1,2, Martn Stgge 1, Wang Y 1,2 and Ge Yu 2 1 Uppsala Unversty,

More information

Quantifying the Sub-optimality of Uniprocessor Fixed Priority Non-Pre-emptive Scheduling

Quantifying the Sub-optimality of Uniprocessor Fixed Priority Non-Pre-emptive Scheduling Quantfyng the Sub-optmalty of Unprocessor Fxed Prorty Non-Pre-emptve Schedulng Robert I Davs Real-Tme Systems Research Group, Department of Computer Scence, Unversty of York, York, UK robdavs@csyorkacuk

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

Assortment Optimization under MNL

Assortment Optimization under MNL Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Overhead-Aware Compositional Analysis of Real-Time Systems

Overhead-Aware Compositional Analysis of Real-Time Systems Overhead-Aware ompostonal Analyss of Real-Tme Systems Lnh T.X. Phan, Meng Xu, Jaewoo Lee, nsup Lee, Oleg Sokolsky PRESE enter Department of omputer and nformaton Scence Unversty of Pennsylvana ompostonal

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Improving the Quality of Control of Periodic Tasks Scheduled by FP with an Asynchronous Approach

Improving the Quality of Control of Periodic Tasks Scheduled by FP with an Asynchronous Approach Improvng the Qualty of Control of Perodc Tasks Scheduled by FP wth an Asynchronous Approach P. Meumeu Yoms, L. George, Y. Sorel, D. de Rauglaudre AOSTE Project-team INRIA Pars-Rocquencourt Le Chesnay,

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

Online Appendix. t=1 (p t w)q t. Then the first order condition shows that

Online Appendix. t=1 (p t w)q t. Then the first order condition shows that Artcle forthcomng to ; manuscrpt no (Please, provde the manuscrpt number!) 1 Onlne Appendx Appendx E: Proofs Proof of Proposton 1 Frst we derve the equlbrum when the manufacturer does not vertcally ntegrate

More information

The internal structure of natural numbers and one method for the definition of large prime numbers

The internal structure of natural numbers and one method for the definition of large prime numbers The nternal structure of natural numbers and one method for the defnton of large prme numbers Emmanul Manousos APM Insttute for the Advancement of Physcs and Mathematcs 3 Poulou str. 53 Athens Greece Abstract

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

Complete subgraphs in multipartite graphs

Complete subgraphs in multipartite graphs Complete subgraphs n multpartte graphs FLORIAN PFENDER Unverstät Rostock, Insttut für Mathematk D-18057 Rostock, Germany Floran.Pfender@un-rostock.de Abstract Turán s Theorem states that every graph G

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

O-line Temporary Tasks Assignment. Abstract. In this paper we consider the temporary tasks assignment

O-line Temporary Tasks Assignment. Abstract. In this paper we consider the temporary tasks assignment O-lne Temporary Tasks Assgnment Yoss Azar and Oded Regev Dept. of Computer Scence, Tel-Avv Unversty, Tel-Avv, 69978, Israel. azar@math.tau.ac.l??? Dept. of Computer Scence, Tel-Avv Unversty, Tel-Avv, 69978,

More information

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced, FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

EDF Scheduling for Identical Multiprocessor Systems

EDF Scheduling for Identical Multiprocessor Systems EDF Schedulng for dentcal Multprocessor Systems Maro Bertogna Unversty of Modena, taly As Moore s law goes on Number of transstor/chp doubles every 18 to 24 mm heatng becomes a problem Power densty (W/cm

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 12 10/21/2013. Martingale Concentration Inequalities and Applications

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 12 10/21/2013. Martingale Concentration Inequalities and Applications MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.65/15.070J Fall 013 Lecture 1 10/1/013 Martngale Concentraton Inequaltes and Applcatons Content. 1. Exponental concentraton for martngales wth bounded ncrements.

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force. The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

Quantifying the Exact Sub-Optimality of Non-Preemptive Scheduling

Quantifying the Exact Sub-Optimality of Non-Preemptive Scheduling Quantfyng the Exact Sub-Optmalty of Non-Preemptve Schedulng Robert I. Davs 1, Abhlash Thekklakattl 2, Olver Gettngs 1, Radu Dobrn 2, and Saskumar Punnekkat 2 1 Real-Tme Systems Research Group, Unversty

More information

Appendix B: Resampling Algorithms

Appendix B: Resampling Algorithms 407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Energy-Efficient Scheduling Fixed-Priority tasks with Preemption Thresholds on Variable Voltage Processors

Energy-Efficient Scheduling Fixed-Priority tasks with Preemption Thresholds on Variable Voltage Processors Energy-Effcent Schedulng Fxed-Prorty tasks wth Preempton Thresholds on Varable Voltage Processors XaoChuan He, Yan Ja Insttute of Network Technology and Informaton Securty School of Computer Scence Natonal

More information

Partitioned EDF Scheduling in Multicore systems with Quality of Service constraints

Partitioned EDF Scheduling in Multicore systems with Quality of Service constraints Parttoned EDF Schedulng n Multcore systems wth Qualty of Servce constrants Nadne Abdallah, Audrey Queudet, Marylne Chetto, Rafc Hage Chehade To cte ths verson: Nadne Abdallah, Audrey Queudet, Marylne Chetto,

More information

AN EXTENDIBLE APPROACH FOR ANALYSING FIXED PRIORITY HARD REAL-TIME TASKS

AN EXTENDIBLE APPROACH FOR ANALYSING FIXED PRIORITY HARD REAL-TIME TASKS AN EXENDIBLE APPROACH FOR ANALYSING FIXED PRIORIY HARD REAL-IME ASKS K. W. ndell 1 Department of Computer Scence, Unversty of York, England YO1 5DD ABSRAC As the real-tme computng ndustry moves away from

More information

HMMT February 2016 February 20, 2016

HMMT February 2016 February 20, 2016 HMMT February 016 February 0, 016 Combnatorcs 1. For postve ntegers n, let S n be the set of ntegers x such that n dstnct lnes, no three concurrent, can dvde a plane nto x regons (for example, S = {3,

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

11 Tail Inequalities Markov s Inequality. Lecture 11: Tail Inequalities [Fa 13]

11 Tail Inequalities Markov s Inequality. Lecture 11: Tail Inequalities [Fa 13] Algorthms Lecture 11: Tal Inequaltes [Fa 13] If you hold a cat by the tal you learn thngs you cannot learn any other way. Mark Twan 11 Tal Inequaltes The smple recursve structure of skp lsts made t relatvely

More information

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Physics 5153 Classical Mechanics. Principle of Virtual Work-1 P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Fundamental loop-current method using virtual voltage sources technique for special cases

Fundamental loop-current method using virtual voltage sources technique for special cases Fundamental loop-current method usng vrtual voltage sources technque for specal cases George E. Chatzaraks, 1 Marna D. Tortorel 1 and Anastasos D. Tzolas 1 Electrcal and Electroncs Engneerng Departments,

More information

COS 521: Advanced Algorithms Game Theory and Linear Programming

COS 521: Advanced Algorithms Game Theory and Linear Programming COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton

More information

Global EDF Scheduling for Parallel Real-Time Tasks

Global EDF Scheduling for Parallel Real-Time Tasks Washngton Unversty n St. Lous Washngton Unversty Open Scholarshp Engneerng and Appled Scence Theses & Dssertatons Engneerng and Appled Scence Sprng 5-15-2014 Global EDF Schedulng for Parallel Real-Tme

More information

Parallel Real-Time Scheduling of DAGs

Parallel Real-Time Scheduling of DAGs Washngton Unversty n St. Lous Washngton Unversty Open Scholarshp All Computer Scence and Engneerng Research Computer Scence and Engneerng Report Number: WUCSE-013-5 013 Parallel Real-Tme Schedulng of DAGs

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

E Tail Inequalities. E.1 Markov s Inequality. Non-Lecture E: Tail Inequalities

E Tail Inequalities. E.1 Markov s Inequality. Non-Lecture E: Tail Inequalities Algorthms Non-Lecture E: Tal Inequaltes If you hold a cat by the tal you learn thngs you cannot learn any other way. Mar Twan E Tal Inequaltes The smple recursve structure of sp lsts made t relatvely easy

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Joint Statistical Meetings - Biopharmaceutical Section

Joint Statistical Meetings - Biopharmaceutical Section Iteratve Ch-Square Test for Equvalence of Multple Treatment Groups Te-Hua Ng*, U.S. Food and Drug Admnstraton 1401 Rockvlle Pke, #200S, HFM-217, Rockvlle, MD 20852-1448 Key Words: Equvalence Testng; Actve

More information

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Mixed-Criticality Scheduling with I/O

Mixed-Criticality Scheduling with I/O Mxed-Crtcalty Schedulng wth I/O Erc Mssmer, Katherne Mssmer and Rchard West Computer Scence Department Boston Unversty Boston, USA Emal: mssmer,kzhao,rchwest}@cs.bu.edu Abstract Ths paper addresses the

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Edge Isoperimetric Inequalities

Edge Isoperimetric Inequalities November 7, 2005 Ross M. Rchardson Edge Isopermetrc Inequaltes 1 Four Questons Recall that n the last lecture we looked at the problem of sopermetrc nequaltes n the hypercube, Q n. Our noton of boundary

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

Solving Nonlinear Differential Equations by a Neural Network Method

Solving Nonlinear Differential Equations by a Neural Network Method Solvng Nonlnear Dfferental Equatons by a Neural Network Method Luce P. Aarts and Peter Van der Veer Delft Unversty of Technology, Faculty of Cvlengneerng and Geoscences, Secton of Cvlengneerng Informatcs,

More information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there

More information

Endogenous timing in a mixed oligopoly consisting of a single public firm and foreign competitors. Abstract

Endogenous timing in a mixed oligopoly consisting of a single public firm and foreign competitors. Abstract Endogenous tmng n a mxed olgopoly consstng o a sngle publc rm and oregn compettors Yuanzhu Lu Chna Economcs and Management Academy, Central Unversty o Fnance and Economcs Abstract We nvestgate endogenous

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

How Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists *

How Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists * How Strong Are Weak Patents? Joseph Farrell and Carl Shapro Supplementary Materal Lcensng Probablstc Patents to Cournot Olgopolsts * September 007 We study here the specal case n whch downstream competton

More information

Sampling Theory MODULE VII LECTURE - 23 VARYING PROBABILITY SAMPLING

Sampling Theory MODULE VII LECTURE - 23 VARYING PROBABILITY SAMPLING Samplng heory MODULE VII LECURE - 3 VARYIG PROBABILIY SAMPLIG DR. SHALABH DEPARME OF MAHEMAICS AD SAISICS IDIA ISIUE OF ECHOLOGY KAPUR he smple random samplng scheme provdes a random sample where every

More information

Worst-case response time analysis of real-time tasks under fixed-priority scheduling with deferred preemption

Worst-case response time analysis of real-time tasks under fixed-priority scheduling with deferred preemption Real-Tme Syst (2009) 42: 63 119 DOI 10.1007/s11241-009-9071-z Worst-case response tme analyss of real-tme tasks under fxed-prorty schedulng wth deferred preempton Render J. Brl Johan J. Lukken Wm F.J.

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information