Instantaneous Utilization Based Scheduling Algorithms for Real Time Systems Radhakrishna Naik 1, R.R.Manthalkar 2 Pune University 1, SGGS Nanded 2

Transcription

1 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, Instantaneous tlzaton Based Schedulng Algorthms for Real Tme Systems Radhakrshna Nak, R.R.Manthalkar une nversty, SGGS Nanded Abstract Ths paper proposes a new novel schedulng algorthms to schedule perodc tasks for soft real tme system. Ths s a plannng based offlne scheduler where tasks are scheduled on the bass of ts nstantaneous utlzaton. Here after every quantum of executon, nstantaneous utlzaton of each task s calculated. Task whch s havng hghest nstantaneous utlzaton s scheduled to the processor. Snce Instantaneous utlzaton factor(if) s temporarly varant factor, the prorty of each task wll vary contnuously. Also It s often more desrable to complete some portons of every task rather than gvng up completely the processng of some tasks. The Imprecse omputaton Model was ntroduced to allow for the tradeoff of the qualty of computatons n favor of meetng the deadlne constrants. It s observed that schedulng performance metrcs such as schedulablty, utlzaton, context swtchng, response tme and relablty are mproved by ths approach as compared to schedulng algorthms such as RM, EDF, LLF, MF schedulng algorthms. KEYWORDS: RM: Rate monotonc, EDF: Earlest deadlne frst, LLF: Least laxty frst, MF: Maxmum urgency frst, IF: Instantaneous utlzaton frst, IRIS: Increased reward wth ncreased servce. MIF: Modfed nstantaneous schedulng algorthm. I. INTRODTION As soft real-tme systems are less restrctve n nature, t s requred that the crtcal processes receve prorty over less crtcal ones. In a soft real-tme system, mssng some deadlnes wll lower the performance of the system, but system wll stll contnue to operate. reemptve schedulers usually offer better overall system utlzaton. So they are preferred over non preemptve schedulers. Deadlne parameter of a request s the amount of tme gven to the system to execute and complete the request after t s arrved. reemptve real-tme schedulng algorthms can be broadly classfed nto two categores: statc prorty and dynamc prorty. Ths classfcaton s based on the manner n whch prortes are assgned to tasks. A schedulng algorthm s sad to be statc f prortes are assgned to tasks a pror and do not change durng run-tme, for example Rate Monotonc (RM) Algorthm. A schedulng polcy s sad to be dynamc f the prortes of a task mght change from request to request, for example Earlest Deadlne Frst (EDF) algorthm. Statc prorty and dynamc prorty schedulng has receved tremendous attracton after poneerng work done by Lu and Layland []. RM Assgns prorty of each task accordng to ts perod, so that the shorter perods get the hgher prorty. Lu and Layland have also found stronger utlzaton for a dynamc prorty assgnment polcy called EDF. A task s assgned hghest prorty f ts deadlne s nearest and wll be assgned lowest prorty f deadlne s farthest. The problem wth RM schedulng algorthms s that- nfortunately, wth statc schedulng, resources must be allocated pessmstcally and scheduled under the assumpton that nterrupts occur at the maxmum rate. When they do not, utlzaton s effectvely reduced because unused resources cannot be reallocated. As the prortes cannot be changed easly at run-tme, allocatons must be based on worst-case condtons. Thus, f an operaton requres 8 msec, statc schedulng analyss must assume that 8 msec wll be requred for every nvocaton. Agan, utlzaton s effectvely penalzed because the resource wll be dle for msec n the usual case. From a schedulng perspectve, the man advantage of EDF and least laxty frst (LLF) s that they overcome the utlzaton lmtatons of RM. In partcular, the utlzaton phasng penalty. Ths s because EDF and LLF prortze operatons accordng to ther dynamc run-tme characterstcs. They handle harmonc and non-harmonc perods comparably, and respond flexbly to nvocaton-tonvocaton varatons n resource requrements, allowng tme one operaton does not use to be reallocated to other operatons. Thus, they can produce schedules that are optmal n terms of utlzaton. urely dynamc schedulng approaches lke LLF and EDF potentally releve the utlzaton lmtatons of the statc RM approach. However, they have a hgher cost to evaluate the schedulng algorthm at run-tme. In addton, these purely dynamc schedulng strateges offer no control over whch operatons wll mss ther deadlnes f the schedulable bound s exceeded. As operatons are added to the schedule to acheve hgher utlzaton, the margn of safety for all operatons decreases. Therefore, the rsk of mssng a deadlne ncreases for every operaton as the system becomes overloaded. We have desgned the scheduler by consderng smple utlzaton based schedulablty analyss technque. Our paper proposes dynamcally changng prorty based pre-emptve schedulng algorthm based on the nstantaneous utlzaton of the task. Instantaneous utlzaton factor (IF) s the processor utlzaton of the task at any nstant. rorty of the task s based on ths IF of each task n the gven task set. Snce the IF s the temporarly varant factor, the prorty of each task vares contnuously. Algorthm lke maxmum utlzaton frst [MF]/ maxmum urgency frst [MF] have also dwelled on the dea of 654

2 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, schedulng accordng to utlzaton of the task. But ths algorthm has consdered the statc utlzaton of the task rather than ther nstantaneous utlzaton value. IF has number of nterestng characterstcs. The IF maxmzes utlzaton bound of schedule and has the capablty to accommodate larger task set. IF has capablty of dynamc predctablty.e. we can predct the status of our scheduler at any nstant of tme n the future and whether the schedule wll stll reman feasble. Ths paper suggests two algorthms. Instantaneous utlzaton frst real tme schedulng algorthm.. Modfed IF real tme schedulng algorthm. Thus contrbuton of ths paper s to enhance context swtchng, response tme and utlzaton than that of IF schedulng algorthm. II. RELATED WORK Lu and Layland [] were perhaps the frst to formally study prorty drven algorthms. They focused on the problem of schedulng perodc tasks on a sngle processor and proposed two preemptve algorthms, RM and EDF. RM algorthm assgns prortes to tasks based on ther perod. Hgher the prorty wth shorter perods. Maxmum utlzaton bound acheved s 69 %.Ths often referred as schedulablty test. Ths test s true f perod of nvocaton of task s equal to relatve deadlne of tasks. Inverse deadlne allows a weakenng of the condton whch requres equalty between perods and deadlnes n statc-prorty schemes. The nverse deadlne algorthm [] assgns prortes to tasks accordng to ther deadlnes: the task wth the shortest relatve deadlne s assgned the hghest prorty. Inverse deadlne s optmal n the class of fxed-prorty assgnment algorthms n the sense that f any fxed-prorty algorthm can schedule a set of tasks wth deadlnes shorter than perods, than nverse deadlne wll also schedule that task set. The computaton gven n the prevous secton can be extended to the case of two tasks wth deadlnes shorter than perods, scheduled wth nverse deadlne. Wth dynamc prorty assgnment algorthms, prortes are assgned to tasks based on dynamc parameters that may change durng task executon. The most mportant algorthms n ths category are earlest deadlne frst [] and least laxty frst [,4] The Maxmum rgency Frst (MF) [5] schedulng algorthm supports both the determnstc rgor of the statc RM schedulng approach and the flexblty of dynamc schedulng approaches such as EDF and MLF. RM assgns all prorty components statcally and EDF/LLF assgns all prorty components dynamcally. In contrast, MF can assgn both statc and dynamc prorty components. rortes can be assgned statcally or dynamcally based on dfferent crtera lke deadlne, crtcalty, perodcty etc. Dynamc schedulng can be preemptve or non-preemptve K. Ramamrtham, J.A Stankovc [6] work on 4-schedulng paradgms. Statc table drven approaches are applcable to tasks that are perodc. Tables are constructed by usng heurstcs that dentfy the state and completon tmes of each task and tasks are dspatched accordng to ths table. Ths s hghly predctable approach, but s hghly nflexble snce any change to the tasks and ther characterstcs may requre a complete overhaul of the table. tlzaton bound tests were frst proposed Goossens et al. [7] n whch t s assumed that tasks have relatve deadlne equal to ther perod. Baker [8] slghtly modfed the assumpton such that utlzaton bound test can be performed on tasks wth relatve deadlne less than or equal to ther perod. Baker derved smple suffcent condtons for schedulablty of systems of perodc or sporadc tasks n a multprocessor preemptve schedulng envronment. Imprecse omputaton Model was ntroduced [9] to allow for the trade-off of the qualty of computatons n favor of meetng the deadlne constrants. In ths model, a task s logcally decomposed nto two subtasks, mandatory and optonal. The mandatory subtask of each task s requred to be completed by ts deadlne, whle the optonal subtask can be left unfnshed. If a task has an unfnshed optonal subtask, t ncurs an error equal to the executon tme of ts unfnshed porton. The Imprecse omputaton Model s desgned to model an teratve algorthm, where the task ntally spends some tme for ntalzaton (the mandatory subtask) and then terates to mprove the qualty of the soluton (the optonal subtask). Snce the optonal subtask corresponds to teratons to mprove the qualty of the soluton, t can be left unfnshed and stll obtan a soluton wth a somewhat nferor qualty. In ths way, we can trade off the qualty of the computaton n favor of meetng deadlnes. Therefore we consdered quantum sze equal to ts mandatory porton and scheduled accordng to nstantaneous utlzaton and optonal porton s scheduled accordng to shortest ob frst crtera. In ths paper we propose that,f n case there s fault, whle executng mandatory porton, t has been suggested to provde redundancy to the mandatory porton as the faults of nterest are those that are transent. astllo et al. [] n ther study of several systems ndcates that the occurrences of transent faults are to 5 tmes more frequent than permanent faults. In some applcatons ths frequency can be qute large; one experment on a satellte system observed 5 transent faults n a 5 mnute nterval due to cosmc ray ons []. To provde the flexblty needed to program fault tolerance, fxed prorty preemptve schedulng suggested by A. ampbell et al. [] can be used. Arshad Iqbal and Asa Zafar[] suggest that the desgn and analyss of a new schedulng algorthm. Dynamc Queue Deadlne Frst (DQDF) to handle schedulng of dynamc multple tasks n real tme systems. They provde a approach that reduced the dead-lne mssng rato but t has a hgher overhead. 655

3 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, III. INSTANTANEOS TILIZATION SHEDLING ALGORITHM FRAMEWORK A. ontrbuton of ths paper In ths paper, the problem of predctng mssng of deadlne at any nstant of tme, low overhead, ustfcaton of all tasks, no preempton and hgher schedulablty are addressed. Frst we propose a framework of IF schedulng algorthm, we run the case study for the same and fnally we perform comparatve evoluton of RM, EDF,LLF and IF. B. System model I N T T A S K TASK SELET DATER READY QEE RN TIME QEE Fgure.Archtecture of IF framework Ths s bascally a plannng based schedulng algorthm where prortes are assgned based on Instantaneous tlzaton.every task s schedule for fx quantum of tme.lannng of schedulng s consdered for frst teraton of perod of nvocaton of the tasks. The quantum for whch task s appled to s Q. The total sum of quantum of all task (for that quantum teraton only) s Q =Q. In ths framework, for gven task set,t s calculated by- T =LM{perod of nvocaton of all tasks } Now for tme span equal to T tasks are mapped to n followng steps- Step:- Intally for gven task set, calculate utlzaton of each task usng formula = / =Intal utlzaton of th task. =Intal computaton tme. () =Intal perod of nvocaton. Based on utlzaton [ ] the task whch s havng hgher value of tlzaton s mapped for the. Step:- Now n a gven T, one task has executed for one quantum of tme. Agan calculate value of, by usng followng formula = -Q. () =-Q. () Where Q =Q. Then calculate new Instantaneous tlzaton factor for th task for usng formula and = /. (4) Where, =Instantaneous computaton tme for th task = Instantaneous perod of executon of th task. =Instantaneous tlzaton of th task. For second teraton of tme Derve the table (T,,, ) Agan the task whch s havng hghest nstantaneous utlzaton wll be havng hghest prorty of executon for second teraton quantum. Lke wse, calculate = - -Q. (5) alculate = - -Q. (6) usng equaton 4 and 5 = /. (7) Where, =Instantaneous utlzaton of th task for the th teraton of quantum = T end pont. Step : Hence task sequence n frst T s derved. It s observed that at every step we can check whether the nstantaneous utlzaton s less than ntal utlzaton.if at any gven nstant of tme, t s observed that t s greater than, t means that task s gong to mss ts deadlne. It s observed that n frst T span, there s hgher context swtchng between the task. In order to avod context swtchng we are suggestng concept of run tme data structure (run tme 656

4 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, queue).tasks are shuffled n run tme queue as shown n fgure. Fgure. Task Shufflng Intally n T span, consder tme span between T and T shufflng the quantum of tasks whch are smlar. Then consder tme span between T and T agan shuffle the quantum of tasks whch are smlar. Lastly consder tme span between T and T end and shuffle the quantum of tasks.. A ase Study: IF Schedulng Algorthm onsder followng task set T = (, 9), T = (5, ), T = (7, 8). alculate ntal utlzaton usng equaton () = / TABLE I. INITIAL TASK SET. T T 9. T 5.45 T After frst step, n Table we observed that T has the hgher ntal utlzaton so s mapped for executon. For Quantum alculate the new values of,, and usng the followng value: = =9 Q = =5 = Q = =7 =8 Q = Q= (Q ) =++= Now usng the formula, = -Q =-Q Then calculate new Instantaneous tlzaton factor for th task by usng formula and - = / = -Q / = -Q / -Q = -/9- =.7 -Q = 5-/- =.4 = -Q / -Q = 7-/8- =.8 Now we get the new task set as TABLE II. TASK SET AFTER ONE QANTM OF EXETION. T T 8.7 T 4.4 T Agan here we observed that the task T has hgher nstantaneous utlzaton so allow t for executon and recalculate the task set for the next quantum of executon tme. = =4 =7 =8 Q = = Q = =7 Q = Q= (Q ) =++= Now usng the formula, = -Q / -Q = -Q / -Q = -/8- =.4 = -Q / = -Q / -Q = 4-/- =. -Q = 7-/7- =.9 Now we get the new task set as: TABLE III. TASK SET AFTER SEOND QANTM OF EXETION. T T 7.4 T 9. T Agan task T has hgher nstantaneous utlzaton so allow t executon and recalculate the task set for the next quantum of executon tme. 5. ontnue ths process tll, we get th quantum of executon tme.e. =T_END_OINT usng formula: = - -Q / -Q Now consder run tme queue of sze T and shuffle tasks n run tme queue. After shufflng tasks n run tme queue, tasks are appled to as shown n fg.. D.RESLT ANALYSIS Gven task set s smulated usng HEDDER a real tme smulator for RM, EDF and LLF and observed context swtchng, number of preemptons and deadlne mssng possblty for each scheduler. We have also desgned smulator for our algorthm n and observed the same parameters wth our smulator we are gettng followng results

5 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, TABLE IV.OMARISON OF IF WITH OTHER SHEDLERS. Factors IF RM EDF LLF ontext Swtchng 6 9 S Rato Response tme Average Hgh Hgh Average Schedulablty Hgh Low Hgh Average No. of reemptons 4 9 It s clear from fg. 4 and 5 that, although context swtchng s hgh comparatve to other algorthms but number of preemptons are zero hence t ncreases schedulablty of tasks. No. of reemptons Before Shufflng IF RM EDF LLF Fgure 5: omparatve preemptons of IF wth other Schedulng algorthms After Shufflng Fgure.Shufflng Of Tasks sng Run Tme Queue For A Gven Tasks Set. No. of ontext Swtchng IF RM EDF LLF Fgure 4: omparatve context swtchng of IF wth other Schedulng algorthms IF schedulng algorthm [4] has the drawback that context swtchng s very hgh snce n ths algorthm, shufflng of tasks s suggested; ths loses ntenton of prorty assgnment. Therefore there s need to modfy IF schedulng algorthm so as to reduce context swtchng. In proposed framework tasks are logcally dvded nto mandatory and optonal porton. All mandatory portons are scheduled accordng to nstantaneous utlzaton lke IF. Ths paper proposes a new approach where mprecse computaton model s used,where task s logcally dvded nto two parts, mandatory and optonal part. rorty of the mandatory porton of task s based on ths IF. Snce the IF s a temporally varant factor, the prorty of each task wll vary contnuously. Optonal portons are scheduled by shortest ob frst. Expermentally t s proved that t ncreases utlzaton effectvely. It also handles harmonc and non harmonc perods comparably. Number of mssng deadlnes s less. In order to mprove relablty, the actve mandatory porton s loaded as redundant copy so that f at all mandatory porton s faled due to some reason, at least mandatory porton wll get executed. Thus by compromsng n performablty, t enhances relablty through redundancy. The man advantage of ths framework s that there s no need to nclude error recovery ponts n program whch avods system overheads. IV. MODIFIED IF REAL TIME SHEDLING ALGORITHM A block dagram of proposed framework s shown n the fgure. Ths s bascally a plannng based offlne preemptve schedulng algorthm where prortes are assgned based on 658

6 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, Instantaneous tlzaton. Every task s scheduled for fxed quantum of tme.e. for mandatory part of task. Basc assumptons of the system. Gven task set s consdered as IRIS task (Increased reward wth ncreased servce).. Intally task s dvded nto two parts: ) Mandatory porton, ) Optonal porton.. onsder arrval tme of all task =. 4. erod of task s equal to ts relatve deadlne. 5. The soft real-tme system s consdered where system wll tolerate lateness wth decreased servce qualty but no crtcal consequences. B. System Model hase : Dvde the task nto two parts onsder two porton of the task from the gven task set as ) Mandatory porton, ) Optonal porton. hase : schedulng of the mandatory porton of task usng IF Step :- Intally for gven task set, calculate utlzaton of each task usng formula (8) Step :- One task has executed for one quantum (equal to mandatory porton) of tme. Agan calculate value of Q Q, by usng followng formula- Q. (9) Q. () Where Q Q, Then calculate new Instantaneous tlzaton factor for th task for usng formula and () Where, =Instantaneous computaton tme for th task = Instantaneous perod of executon of th task. =Instantaneous tlzaton of th task. For second teraton of tme derve the table ( T,,, ) Agan the task whch s havng hghest nstantaneous utlzaton wll be havng hghest prorty of executon for second teraton quantum. Lke wse, calculate Q () alculate Where, Q () usng equaton 4 and 5 (4) Fgure 6: Archtecture of MIF Framework. =Intal utlzaton of th task. =Intal computaton tme of. =Intal perod of nvocaton. Based on utlzaton [ ] the task whch s havng hgher value of tlzaton, mandatory porton of ths task s mapped to the. =Instantaneous utlzaton of th task for the th teraton of quantum. At any gven nstant of tme, If t s observed that, nstantaneous utlzaton s greater than, t means that task s gong to mss ts deadlne. hase : schedulng the optonal porton of the task For schedulng the optonal porton, shortest optonal porton frst s employed. ALGORITHM ) Take nput of tasks contanng perod, mandatory executon tme, and optonal executon tme. ) alculate the mandatory utlzaton and optonal utlzaton for each task. 659

7 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, ) heck the schedulablty for tasks accordng to ther utlzaton 4) Generate mappng for tasks. 5) Execute the mandatory porton of tasks accordng to the hghest nstantaneous utlzaton. Meanwhle f there s nterrupt due to corrupton of task whle executng the mandatory porton of task then a backup mage s mantaned so that the task can be restored from the mage. 6) After executng mandatory porton of all tasks execute optonal porton accordng the shortest ob frst polcy. 7) If nterrupt occurs due to corrupton of task n the optonal porton of task then optonal porton does not run to ts completon and gets aborted. B. Result Analyss Varous tasks sets have been appled to MIF scheduler smulator and t has been observed that context swtchng, response tme and utlzaton s mproved.followng table I to X shows how tasks are selected and scheduled usng MIF. Here, M= Mandatory porton of task for executon, O= Optonal orton of the task for executon, = erod/deadlne of task. TABLE V: GIVEN TASK SET WITH EXETION TIME AND ERIOD Task M O T 8 T T 6 T4 5 TABLE IX: SELETION OF FORTH MANDATORY TASK Task M O (M) T.8 T. T 9. T4 8. TABLE X: SELETION OF FIRST OTIONAL TASK Task M O (M) T 9 - T - T 7 - T4 6 - TABLE XI: SELETION OF SEOND OTIONAL TASK Task M O (M) T 8 - T - T 6 - T4 5 - TABLE XII: SELETION OF THIRD OTIONAL TASK Task M O (M) T 7 - T 9 - T 5 - T4 4 - TABLE VI: SELETION OF FIRST MANDATORY TASK Task M O (M) T 5. T 7. T.5 T4.6 TABLE VII: SELETION OF SEOND MANDATORY TASK Task M O (M) T 8. T.5 T 6. T4 5. TABLE VIII: SELETION OF THIRD MANDATORY TASK Task M O (M) T.5 T 5. T.8 T4. TABLE XIII: SELETION OF FORTH OTIONAL TASK Task M O (M) T 5 - T 7 - T - T4 - TABLE XIV: FINALLY ALL TASKS ARE GETTING SHEDLED Task M O (M) T - T 5 - T - T4 - Here, the table XV ndcates the complete case study for the gven set of tasks. It also ndcates the tme nterval and the descrpton how the task get schedules. 66

8 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, TABLE XV: DESRITION OF TASK SHEDLE. Tme Executng Descrpton Interval Task 6 4 IF MIF M(T) Mandatory part of Task T has hghest nstantaneous utlzaton so t gets executed frst untl ts computaton s over. M(T4) Now, Mandatory part of Task T4 has hghest nstantaneous utlzaton so t gets executed. 5 M(T) Then, Mandatory part of Task T has hghest nstantaneous utlzaton so t gets executed. 7 M(T) Fnally, Mandatory part of Task T has hghest nstantaneous utlzaton so t gets executed. 9 O(T4) After completng mandatory part of all tasks, Task T has lowest optonal part so t gets executed. Here optonal porton are equal te s broken on FFS. O(T) Now, Task T4 has lowest Optonal part so t gets executed. O(T) Then, Task T has lowest optonal part so t gets executed. -5 O(T) At last, Task T has lowest optonal part so t gets executed. - - All tasks have completed ther mandatory as well as optonal porton. Second teraton starts. No. of ontext Swtchng ase ase ase Fgure 7: omparatve ontext Swtchng of MIF and IF No. of ontext Swtchng IF MIF The sad algorthm s smulated n Mcrosoft Vsual Basc 6 and exstng algorthms are smulated usng HEDDER smulator.. We attempt to keep the framework as general as possble by accommodatng software faults tolerated by some form of recovery block, and hardware faults dealt wth by state restoraton and re-executon. Error latences wll be assumed to be short and hence tred to enhance relablty of scheduler.. The prevous IF algorthm has more number of context swtches as the quantum consdered s too small. But n the proposed algorthm, the context swtches get mnmzed as the quantum consdered s the mandatory porton of task(fg.7) The task swtch from one to another at every unt nstance. But for proposed algorthm t s very less. The followng table shows the analyss of the results for prevous IF algorthm and proposed algorthm. TABLE XVI: OMARATIVE RESLTS OF VARIOS ASE STDIES ase study ontext swtchng Response tme (T, T, T ) tlzaton (In %) IF MIF IF MIF IF MIF 5 7 4,5,,, 7 5,, 7, 9, 8 5,, 8,, 5 ase ase ase Fgure 8: omparatve schedulablty of MIF and IF schedulng algorthm. Schedulablty of MIF s mproved than IF schedulng algorthm.(fg.8). Response tme IF MIF IF MIF MIF IF Task Task Task Graph for case study Fgure 9: omparatve response tme of MIF and IF schedulng algorthm. Response Tme observed n MIF s less than that of IF(fg.9) also n MIF utlzaton s also ncreased consderably. 66

9 Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, V. ONLSION Approach of IF s better than tradtonal schedulng approaches. Frst attempt of IF has better performance n terms of number of preemptons and schedulablty.second attempt does better than IF n context swtchng, response tme and schedulablty. In order to reduce context swtchng observed n IF, an approach of IRIS s used.e. task s logcally dvded nto two parts ) Mandatory porton and ) Optonal porton. Wth schedulng mandatory porton by hghest nstantaneous utlzaton frst and optonal porton by shortest optonal porton frst, results observed are encouragng. It has been observed that context swtchng, response tme and utlzaton has been ncreased as compared wth IF schedulng algorthm. Ths framework also suggests redundancy to mandatory portons so as to ncrease relablty of schedulers. Introducng ths concept, the necessty of addng error checkng bts s removed. It ncreases resource utlzaton by compromsng n the performablty. REFERENES []. L. Lu and J. W. Layland, Schedulng algorthms for multprogrammng n a hard-real-tme envronment, Journal of the AM, (), pp.47-6january 97. [] Leung J. and Merrll M., A note on preemptve schedulng of perodc realtme tasks, Informaton rocessng Letters, (): 5 8, 98. [] Dhall S.K., Schedulng perodc-tme crtcal obs on sngle processor and multprocessor computng systems, hd thess, nversty of Illnos, Aprl 977. [4] Sorenson.G., A methodology for real-tme system development, hd Thess, nversty of Toronto, anada, 974. [5] D. B. Stewart and. K. Khosla, Real-Tme Schedulng of Sensor-Based ontrol Systems, n Real-Tme rogrammng (W. Halang and K. Ramamrtham, eds.), Tarrytown, NY: ergamon ress, 99. [6] K.Ramamrtham,J.A. Stankovc,.Shah, Effcent schedulng algorthms for real tme multprocessor system, IEEE Transacton on parallel and dstrbuted system, ():pp Apr, 99. [7] J. Goossens, S. Funk, and S. K. Baruah, rorty-drven Schedulng of erodc Task Systems on Multprocessors. Real-Tme Systems, vol. 5, no. -,,, pp [8] T.. Baker, Multprocessor EDF and Deadlne Monotonc Schedulablty Analyss. n IEEE Real-Tme Systems Symposum (RTSS),, pp. 9. [9] K.J. Ln, S. Nataraan, and J. W. S. Lu, oncord, A dstrbuted system makng use of mprecse results n roc. of OMSA 87, Tokyo, Japan, 987. [] X. astllo, S.. Mconnel, and D.. Seworek. Dervaton and albraton of a Transent Error Relablty Model, IEEE Transactons on omputers,(7), July 98,pp [] H. Km, A.L.Whte, and K. G.Shn Relablty modelng of hard realtme systems, In roceedngs 8th Int. Symp. on Fault-Tolerant omputng (FTS-8),. IEEE omputer Socety ress, 998, pp 4. [] A. ampbell,. McDonald, and K. Ray. Sngle Event upset rates n space IEEE Transactons on Nuclear Scence, 9(6): December 99, pp [] Arshad Iqbal,Asa Zafar,Bushra Sddque, Dynamc Queue Deadlne frst schedulng algorthm for soft real-tme System,IEEE Internatonal onference on Emergng Technologes, sept. 7-8 Islamabad,5, pp [4] Radhakrshna Nak, Vvek Josh, R. R. Manthalkar, "IF Schedulng Algorthm for Improvng the Schedulablty, redctablty and Sustanablty of the Real Tme System," IETET-9,IEEE Second Internatonal onference on Emergng Trends n Engneerng & Technology, pp.998-, 9. [5] Radhakrshna Nak, R. R. Manthalkar, "Modfed IF Schedulng Algorthm for the Real Tme Systems," IETET-,IEEE thrd Internatonal onference on Emergng Trends n Engneerng & Technology, pp.7-76,. 66