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Towards bandwidth optimal temporal partitioning Attila Zabos, Alan Burns Department of Computer Science University of York, UK Abstract time intervals speciﬁed by the VRs replenishment pe- riod, this budget is replenished to its initial value. In real-time systems the available processing time is The selected VR parameter values can be considered partitioned in order to enforce separation of concerns as globally or locally optimal [4]. They are globally op- of concurrent application components. This is a neces- timal if the minimum system utilisation, including all the sary step to prevent possible side effects of an erroneous other VRs in the system, is achieved. On the other hand, application component on other components in the sys- the parameter values of a single VR, considered in iso- tem. One of the main objectives in temporal partitioning lation, are locally optimal if the obtained VR utilisation is the selection of appropriate partition parameters (e.g. is the smallest. replenishment period and budget) that minimise the re- The main challenge in the selection of locally opti- sulting resource bandwidth reservation. Currently there mal VR parameters is that the demand requirements of are only heuristic limits on the search space of band- the associated application component are satisﬁed while width optimal virtual resource (VR) parameters. We pro- the processing time reservation for the application com- vide a lower and upper bound on the period of a periodic ponent is kept at a minimum. The VR budget and period VR in order to limit the search space for bandwidth op- parameters leading to this minimal resource reservation timal parameters. We also show that the VR parameters (i.e. minimum VR utilisation) are considered in this pa- derived from the VR period upper bound provide sufﬁ- per as the bandwidth optimal parameters or simply just ciently good results if the time for VR parameter calcu- optimal parameters. lation is limited and an exhaustive search for optimal Various deﬁciencies of existing approaches inhibit parameter values is not an option. the calculation of locally optimal VR parameters. The origin of these deﬁciencies are either: 1. Introduction and motivation • the application of a linear VR supply model [11, 1] for VR parameter calculation, Due to the increasing processing power of complex • the requirement of additional VR parameter con- embedded real-time systems, there is a trend to integrate straints (e.g. the initial delay of the VR supply real-time application components, that were initially de- function [11] or the speciﬁcation of the VR pe- veloped and veriﬁed independently of each other and riod [6]) that need to be speciﬁed by the application also executed on distinct hardware, on a single system. designer, or Independently developed and veriﬁed application • the deﬁnition of a search space that might not con- components imply that each of them is associated with tain the optimal VR parameters [12, 7]. a different system function. Hence the diversity of ap- In contrast to previous approaches we utilise the exact plication components on a single integrated platform re- supply bound function of the VR and avoid inaccuracy quires isolation of various application components in or- due to a linear VR model. Furthermore, our analysis re- der to avoid interference among each other. The require- quires only the taskset temporal speciﬁcation, enabling ment for separation of concerns in multi-application sys- the calculation of optimal VR parameters without the in- tems makes the temporal partitioning of the available tervention of the application designer. processing time essential. One method to determine optimal VR parameters for A common approach to implement the required tem- an application component is to provide a VR period poral partitioning is by periodic virtual resources (VRs). for which the smallest possible VR budget can be ef- VRs act as a resource reservation mechanism and man- ﬁciently determined (similar to the approach introduced age the processing time budget that is assigned to ap- for the EDP resource model [6]). This approach implies plication components. Characteristic parameters of pe- a brute-force search over an interval of possible VR pe- riodic VRs are their budget and replenishment period. riod values. To our best knowledge there is no deﬁnition The VR budget is consumed while the associated ap- of an interval that ensures the existence of a VR period plication component’s tasks are executed and at regular from which the optimal VR parameters can be derived. 1 We approach the problem of optimal VR parame- supply function and the demand bound function) in or- ter selection by providing a lower and upper bound on der to determine whether an application component’s the VR period, thus limiting the search space. It shall taskset is schedulable or not is speciﬁed by Lipari and be noted that the optimal VR period is not necessarily Bini [12] and Easwaran et al. [7]. In order to use the smaller than the smallest task period or its deadline. schedulability condition to determine the optimal VR In this paper we examine two level hierarchical parameters implies a brute force search over the inter- real-time systems with a ﬁxed-priority (FP) pre-emptive val of possible VR periods. scheduler at both the operating system and the applica- As a reasonable upper bound for the search space of a tion component level. VR period (leading to sufﬁciently good VR utilisation), The paper is structured as follows: Section 2 gives an the least common multiple of task periods or the task overview of related work in the area of VR parameter se- deadlines were suggested by Easwaran et al. [7], and by lection. The model that is assumed in order to determine Lipari and Bini [12], respectively. We will show in an the VR parameters is deﬁned in Section 3. The analyti- example that for optimal parameters the VR period may cal tools that are used in order to derive the optimal VR be larger than the smallest task period or deadline, and period upper bound are presented in Section 4. Our con- consequently also larger than the least common multiple tribution, the calculation of an upper and lower bound on of task periods. the optimal VR period is presented in Section 5 and Sec- The selection of periodic VR parameters based on the tion 6, respectively. Section 7 demonstrates with a nu- task demand function was analysed by Almeida and Pe- merical example how to calculate the VR period bounds dreiras [1]. They examined the demand at each priority using the algorithms and equations that are presented in level and determined relevant demand points that were this paper. The evaluation of the quality of the VR pa- used as input for the VR parameter calculation algo- rameters derived from the VR period upper bound and rithm. The information about the taskset demand at the the length of the optimal VR period search interval is time instant of task deadlines was used in order to de- provided in Section 8. The conclusion and the summary termine the required processing time of the correspond- of our contribution is presented in Section 9. ing taskset. This is a pessimistic assumption since there might be a lower taskset demand at an earlier time in- 2. Related work stant than at the task deadline. Furthermore, their ap- In the past, various research work [5, 9, 15, 3] pro- proach also used a linear VR supply function to deter- vided in-depth analysis of hierarchical real-time sys- mine the ﬁnal VR budget and period. tems, leaving the question of efﬁcient VR parameter se- The selection of optimal VR parameters was also in- lection open. vestigated by Davis and Burns [4]. For a set of periodic Mok et al. [14] presented the bounded-delay resource VRs, the parameter selection was examined if two out of partition model that makes use of the linear supply func- the three VR parameters (i.e. budget, period or priority) tion deﬁned by an availability factor and a partition de- were given. lay. Since it is a general resource model there may exist In this work we utilise the idea of demand func- many periodic VRs that satisfy a given bounded-delay tion based VR parameter selection [1] and the observa- model speciﬁcation. Feng and Mok [8] extended the tion that the task demand increases only at task release bounded-delay model by an additional constraint that times [6]. speciﬁes the smallest scheduling quantum of a VR. This constraint shall limit the number of VR context switches 3. System model and reduce the resulting context switch overhead. In this paper we investigate uniprocessor real-time The application of the bounded-delay resource systems. It is also assumed that these systems use a two- model [14] on the selection of periodic VR parameters level hierarchical ﬁxed-priority pre-emptive scheduling. was investigated by Shin and Lee [16] but without the At the operating system level, the global scheduler de- option to determine the optimal parameters. termines the processor allocation to multitasking appli- A linear model to represent the VR supply is also cation components (in the following only referred to as used by Lipari and Bini [11]. The VR model is char- application component). Within a scheduled application acterised by an initial delay that occurs before the VR component the local scheduler then determines which of supply starts at a uniform rate. This model has the same the application component’s ready tasks shall execute. disadvantage as the bounded-delay model. In order to enforce temporal partitioning among the Using an Explicit Deadline Periodic model, application components in the system, the periodic VR Easwaran et al. [6] presented an algorithm to determine model is used as a resource reservation mechanism. the optimal VR budget for a given VR period. But Each application component Ai is associated with a vir- without the knowledge of the optimal value or the upper tual resource (VR) Si that manages the predeﬁned bud- bound of the VR period, this algorithm can generate an get for this application component and the correspond- inﬁnite set of VR parameters. ing taskset. That means there is a one to one mapping Exact schedulability conditions (based on the VR between application components and VRs. 2 In this paper we are concerned with periodic VRs, A demand point DPi for priority level i is a pair where each VR Si is deﬁned by its budget Θi and re- (qi , ti ), containing the information about the priority plenishment period Πi . The budget Θi is decreased with level demand qi at a certain time instant ti . the same rate as time progress, when ready tasks of the The demand bound function for a priority level i is corresponding application component Ai execute. If the deﬁned in Equation 2 with hep(i) denoting priority lev- VR’s budget is exhausted, the VR is suspended until its els equal or higher than i. budget is replenished to its full capacity. The time in- stants of budget replenishment are determined by the t dbfi (t) = · Cj (2) VR’s replenishment period Πi . The behaviour of the pe- Tj j∈hep(i) riodic VR is identical to a classical periodic server with the exception that the server’s budget is not exhausted The analysis of Equation 2 shows that the demand instantaneously if there are no tasks in the ready queue bound function has local minima at time instants that waiting for execution. In the case that there are no tasks are multiples of task periods. This was also observed by ready to execute, the periodic VR’s budget is continu- Easwaran et al. [6]. We exploit this fact and determine ously consumed by an idle task until it is exhausted [3]. a speciﬁc demand point DPi for each priority level i. Within a time interval between two VR budget re- This enables us to represent the demand requirements of plenishment events, the maximum time without process- an entire taskset by a set of demand points. ing time supply is deﬁned as δ = Π − Θ. Algorithm 1 is used to determine the demand points The supply bound function s(t) determines for a pe- that represent the minimum demand requirements of a riodic VR and a time instant t the cumulative process- given taskset. ing time available to execute the associated application In : Priority ordered taskset component’s tasks. The supply bound function for peri- Out : Set of demand points odic VRs is a piecewise deﬁned function and it can be expressed as in Equation 1 [1]. 1 foreach priority level i do 2 At each priority level i determine a set of 0 :t≤∆ (qm , tm ) pairs. Each (qm , tm ) pair represents a s(t) = t − (∆ + mδ) : ∆ + mΠ ≤ t < ∆ + mΠ + Θ demand point DPm with the cumulative (m + 1)Θ : ∆ + mΠ + Θ ≤ t < ∆ + (m + 1)Π demand qm of the tasks τj : j ∈ hep(i) at task (1) release times tm ∈ Υi = {x | x = n · Tj Di } ; (t − ∆) m= 3 For priority level i let DPi denote Π (qi , ti ) = (qm , tm ) : minm (qm /tm ) with the ∆ = 2 · δ = 2 · (Π − Θ) smallest ratio (qm /tm ). If multiple (qm , tm ) Within an application component, each task τj is de- pairs satisfy this condition, then choose the one ﬁned by the tuple (Cj , Dj , Tj , Pj ), with Cj denoting the with the largest tm ; task’s worst-case execution time, Dj its deadline and Tj 4 Remove redundant demand points; its period. The task’s deadline may be less than or equal to its period (i.e. Dj <= Tj ). Additionally, each task Algorithm 1: Demand point calculation has a unique priority Pj within its associated application component. The priority Pj is determined according to In summary, the algorithm determines for each pri- the deadline-monotonic priority assignment policy. ority level i a set of time instants Υi when a task with Furthermore, the task release time is considered to priority j ∈ hep(i) is released. At the examined priority be independent of the VR’s replenishment period. This level i, for each time instant tm ∈ Υi the corresponding notion of task releases has an impact on the analysis and demand qm is calculated. From the set of (qm , tm ) pairs, it will be further described in Section 5. the one with the lowest ratio is selected and denoted as the representative demand point DPi for priority level i. 4. Analysis Before the algorithm terminates, redundant elements are In this section we introduce the tools that will be removed from the set of demand points. A demand point utilised in Section 5 to determine the optimal VR period (qr , tr ) is considered to be a redundant element if there upper bound. is another element (qi , ti ) such that tr = ti and qr < qi . That means, for a speciﬁc time instant t only the demand 4.1. Demand points point with the largest demand is retained. The removal In order to simplify the analysis of the task require- of redundant points from the set of demand points (last ments and the VR parameter calculation, the demand re- step in Algorithm 1) also implies that every time instant quirements for each priority level i (expressed by the ti is associated to only one demand requirement qi . demand bound function [10]) are mapped to a represen- A demand point DPi can also be considered as a con- tative demand point DPi . straint on the slowest processor speed that is required in 3 order to satisfy the taskset demand at priority level i. Table 2. Required processor speed It should be noted that line 2 of Algorithm 1 may Priority Demand point Required slowest require a lot of iterations if in a multitasking system at level (i) (DPi ) processor speed least one of the tasks has a very short or very long period 1 (400,1300) 30.77% in contrast to the other tasks. However, it is expected 2 (2000,3900) 51.28% that for most embedded real-time systems the range of 3 (4600,6500) 70.77% task periods will be relatively small (e.g. in avionic [13] or automotive system, from a few milliseconds to a sec- initial processing speed, without rendering the taskset ond). Thus for these systems, line 2 of Algorithm 1 gen- unschedulable. erates at most 1000 demand points and constrains the 4.2. Optimal VR utilisation number of iterations of Algorithm 1 within reasonable limits. Plotting the minimum VR utilisation as a function To demonstrate Algorithm 1 on an example, we ex- of the VR period, reveals a sawtooth shape graph (see amine the taskset deﬁned in Table 1 and assume that task Figure 2). The minimum VR utilisation for a VR pe- deadlines are equal to their periods. riod (further referred to as the optimal VR utilisation) is determined by calculating the smallest VR budget such Table 1. Taskset example that the associated taskset is still schedulable. Task Priority Ci Ti The problem of efﬁcient optimal VR parameter cal- A 1 (high) 400 1300 culation due to the sawtooth shape of the optimal B 2 (medium) 800 4600 VR utilisation graph was also observed by Davis and C 3 (low) 1000 6800 Burns [3]. Figure 2 shows the minimum VR utilisation as a In Figure 1 the demand function of each priority level function of the VR period. For one line on the graph, is rendered along with its representative demand point. the VR utilisation values were determined without con- The line of the slowest processor speed that supplies just sidering VR context switch overhead, and for the second sufﬁcient capacity to satisfy the corresponding priority line on the graph a context switch overhead of 100 ticks level demand, is rendered as well. was speciﬁed. 7000 Priority level low For the VR with 100 ticks context switch overhead, demand function Figure 2 shows that the rightmost trough on the minimal 6000 Priority level low VR utilisation graph also represents the optimal VR pa- Priority level mid processor speed rameters. However, this is not always the case as we will 5000 demand function DP3 =(q3 ,t3 ) see in the following sections. Demand 4000 Priority level mid 100 processor speed VR utilisation with 95 VR period 100 ticks VR context VR utilisation (%) 3000 upper bound (Πu ) switch execution time 90 DP2 =(q2 ,t2 ) 2000 85 Priority level high 1000 demand function 80 VR utilisation with Priority level high 0 tick VR context DP1 =(q1 ,t1 ) processor speed 75 switch execution time 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 70 Time (t) 0 500 1000 1500 2000 2500 3000 3500 4000 VR period (ticks) Figure 1. Demand points example Figure 2. Minimum VR utilisation Using the aforementioned taskset (deﬁned in Table 1) as an example and applying Algorithm 1 on it, pro- Furthermore, the examination of the minimum VR vides the demand point DP1 = (400, 1300), DP2 = utilisation graphs indicates that there exists a VR period (2000, 3900) and DP3 = (4600, 6500) for priority level beyond which the function of the VR utilisation is strict 1, 2 and 3, respectively. The slowest processor speed monotonically increasing. Therefore, the optimal VR that is required to satisfy the taskset demand at each pri- budget and period parameters must lie at or below this ority level is depicted in Table 2. The slower processor value that we denote as the VR period upper bound. speed can be also considered as the percentage of the initial processing speed. 5. VR period upper bound In this example, the slowest feasible processor speed In this section we use the tools presented in Section 4 is determined by the demand point DP3 at priority level to derive an equation for the VR period upper bound. 3, since it represents the maximal expected taskset de- For the calculation of the VR parameters, we assume mand per time unit. In this case, the processor speed a discrete time space. Hence in further equations, the could be slowed down approximately to 70.77% of its VR period is mapped to an integer value by applying the 4 ﬂoor function. For the same reason, the VR budget is demand point DPi experiences a single VR replenish- mapped to an integer by applying the ceiling function. ment period. Generally, these discretisation steps are not limited to integer values but could be carried out to an arbitrary VR supply s(t) precision. 5.1. VR context switch excluded First, we consider the scenario in which there is no DPi qi VR context switch overhead and task release times are not synchronised with the replenishment period of the VR. ∆ = 2(Π − Θ) Θ Π Time (t) Since the task release times are not synchronised to t=0 ti the VR’s replenishment period, the worst-case task exe- cution delay occurs under the following circumstances. Figure 4. Single VR replenishment The VR budget is consumed by lower priority tasks Consequently a single VR replenishment period up starting right after the VR’s replenishment period. In to ti implies that the VR budget Θ is equal to the de- the following VR period, the budget supply experiences mand requirement qi . Furthermore, considering a single the maximum possible delay due to higher priority VRs. replenishment period, the supply bound function can be Therefore, after a task is released, its execution can be simpliﬁed to s(t) = t − ∆ and the corresponding VR delayed by ∆ = 2 · δ = 2 · (Π − Θ) in the worst-case period can be derived as: situation (see Figure 3). s(ti ) = qi ti − ∆ = qi ti − 2 · (Π − Θ) = qi Time (t) ti − 2 · (Π − qi ) = qi VR supply s(t) ti + qi Π= 2 Given a single VR replenishment up to ti , we show Θ ∆ = 2(Π − Θ) Π Time (t) that if the VR period Π is increased by an arbitrary pos- ti + qi itive value x, i.e. Π = + x, the VR budget needs t=0 2 to be increased by the same value x in order to preserve Figure 3. Maximum delay of VR supply s(ti ) = qi . Figure 2 indicates that there is a VR period Πu such that for all other VR period values Π > Πu the VR util- s(ti ) = qi isation U (Π) monotonically increases. ti − ∆ = ti − 2 · (Π − Θ) = qi In the following, we provide the necessary statements qi − ti Θ= +Π that can be used to deﬁne the algorithm for the optimal 2 VR period upper bound calculation. qi − ti ti + qi Θ= + +x 2 2 Lemma 1. (Monotonic increase of the VR utilisation) Θ = qi + x Considering a single demand point DPi = (qi , ti ), the optimal virtual resource utilisation U (Π) (satisfying the It can be concluded that for demand point DPi = demand speciﬁed by DPi ) monotonically increases with ti + qi ti + qi (qi , ti ) and a VR period Π ≥ , the VR’s initial the VR period Π ≥ . 2 2 delay ∆ remains constant if the VR budget and period are increased by the same value x. Proof. With an optimal VR utilisation, the VR is sup- ti + qi plying only as much processing time as dictated by the Π= 2 demand point DPi . Hence, the VR supply stops right Θ = qi at the demand point DPi , implying s(ti ) = qi (see Fig- ure 4). Additionally, the examination of the second case of ∆ = (Π + x) − (Θ + x) the piecewise deﬁned VR supply bound function and the ∆= Π−Θ corresponding graph (see Figure 4) shows that a strict ti + qi ti − qi monotonic increase of the VR utilisation occurs if the ∆= − qi = 2 2 5 To show that the VR utilisation U (Π) is a monoton- mini (ti − qi ) determines the maximal initial delay of a ti + qi VR. ically increasing function for Π ≥ , we deﬁne 2 Θ We are interested in the largest possible VR period U = . Considering that Π − Θ = δ is constant, the that might enable us to determine the optimal VR pa- Π Π−δ rameters. Theorem 1 provides the essential means for VR utilisation can be rewritten as U (Π) = . In an algorithm to determine a bandwidth optimal VR pe- Π order to prove that U (Π) is monotonically increasing, riod upper bound. we have to show that Π1 < Π2 ⇒ U (Π1 ) < U (Π2 ). Theorem 1. (Optimal VR period upper bound) The op- Π1 < Π2 timal VR period upper bound is Πu = Πs + x and the δ δ corresponding minimal VR budget Θu = Θs + x with: − <− Π1 Π2 DPs = (qs , ts ) = (qi , ti ) : min(ti − qi ) i δ δ 1− <1− ts + qs Π1 Π2 Πs = Π1 − δ Π2 − δ 2 < Θs = qs Π1 Π2 U (Π1 ) < U (Π2 ) and x being a positive integer number such that for all demand points DPi the condition s(ti ) ≥ qi holds. Proof. Lemma 2 provided (qs , ts ) = mini (ti −qi ) as the Furthermore, it can be observed that for optimal peri- smallest possible VR initial delay. Lemma 1 showed that odic VRs the demand point with the smallest difference ts + qs for Π ≥ the VR utilisation U (Π) monotonically (ti − qi ) has a signiﬁcant inﬂuence on the VR period up- 2 per bound calculation. It deﬁnes the maximal VR initial increases with the VR period Π, and implies that: delay 2 · (Π − Θ) (see Figure5). A supporting argument ts + qs is presented in Lemma 2. Π = Πs + x ≥ Πs = 2 5000 t2 −q2 DP3 The increment x of the initial VR parameters and 4000 t1 −q1 t3 −q3 (Θs , Πs ) can be determined by calculating the required increase for the VR budget in order to meet all task de- VR supply s(t) 3000 mand requirements. 2 · (Π − Θ) DP2 It shall be noted that in further steps, references to all 2000 demand points imply the exclusion of DPs = (qs , ts ) 1000 DP1 from the set of demand points. Now we take a counter intuitive step and ﬁrst calcu- late the minimal VR budget Θu (see Equation 3) for the } 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 VR period Πu that is yet to be determined. Θu is the Time (t) smallest VR budget that is required to satisfy the condi- Figure 5. Mapping of demand points tion ∀i : s(ti ) ≥ qi . The number of VR replenishment periods up to the demand point DPi is represented by Lemma 2. (Maximal VR initial delay) The demand hi . point DPs = (qs , ts ) = mini (ti − qi ) determines the qi VR’s maximal initial delay. Θu = max (3) i hi Proof. The equations to determine the VR parameters The time interval taken into account to determine hi under the assumption of a single replenishment period is [(Π − Θ), ti ], i.e. excluding the inactive VR time that were derived in Lemma 1. Lemma 1 also implies that originates from the VR period before the critical instant. the VR supply experiences the maximum possible delay In order to derive hi , a geometric mapping of the de- and the supply is equal to the demanded processing time, mand points onto to X-axis was carried out (see Equa- s(ts ) = qs . tion 4 and Figure 5). After the mapping, the demand Thus, among all the demand points determined by point (qi , ti ) becomes the point (ti − qi ) on the X-axis. Algorithm 1, DPs = (qs , ts ) = mini (ti − qi ) speci- The end points of the VR supply, also mapped onto the ﬁes the maximal VR initial delay of ts − qs . X-axis, reveal that the distance between these points is For a longer than ts − qs VR initial delay the Π − Θ. ′ VR could not supply sufﬁcient processing time in or- qi −1 1 0 qi ti − qi der to satisfy the requirement of the demand point DPi′ = t′ = 0 0 0 · ti = 0 (4) i DPs . Therefore the demand point DPs = (qs , ts ) = 1 0 0 1 1 1 6 After the transformation of all demand points DPi to Without loss of generality, we assume that a context DPi′ , the number of complete VR replenishment periods switch overhead C0 occurs in each replenishment period hi up to the time instant ti can be calculated as speciﬁed at the beginning of the VR supply. in Equation 5. First, the equations that calculate the initial VR pa- (ti − qi ) − (Πs − Θs ) rameter values need to be extended in order to consider hi = (5) the time consumed by the context switch (see Equa- Πs − Θs tions 8 and 9). In the presence of VR context switch overhead the ap- proach with the mapping of points to the X-axis simpli- DPs = (qs , ts ) = (qi , ti ) : min(ti − qi − C0 ) (7) i ﬁes the calculation of the number of VR supplies. ts + qs + C0 Knowing the VR budget Θu , the budget increase Πs = (8) 2 Θd over the VR’s initial value Θs can be easily de- termined. The VR budget increase Θd entails an in- Θs = qs + C0 (9) crease of the initial VR period Πs by the same value (see Since we account for one context switch overhead Lemma 1) leading to the bandwidth optimal VR period during each replenishment period the number of context upper bound. As shown in Equation 6, without consider- switches up to the time instant ti is equal to the number ing the VR context switch overhead in the analysis, the of VR replenishment periods hi . In order to determine increase of the VR budget and period, Id , is determined the number of VR processing time supplies for the de- only by the required VR budget increase Θd . In Sec- mand point DPi = (qi , ti ), we assume at ti a demand tion 5.2, considering VR context switch overhead, the of qi + hi · C0 . The mapped value of the demand point calculation of the VR budget and period increase Id will with VR context switch overhead included is equal to: be extended. ′ qi −1 1 0 qi + hi · C0 Θd = Θu − Θs DPi′ = t′ = 0 0 0 · i ti Id = Θd (6) 1 0 0 1 1 Πu = Πs + Id ti − qi − hi · C0 In summary, given Πu and Θu we can conclude that DPi′ = 0 the VR utilisation U (Π) monotonically increases for 1 Π ≥ Πu . In the following we show that by keeping the VR Using the demand points DPi′ with the incorporated budget constant and decreasing the VR period, for Π ≤ context switch information, Equation 5 can be easily Πu , the VR utilisation U (Π) again monotonically in- modiﬁed to account for the VR context switch overhead creases. (see Equation 10). With Θu = Θs + Id denoting the smallest possible (ti − qi − hi · C0 ) − (Πs − Θs ) VR budget for Πu = Πs + Id implies that Θu cannot be hi = Π s − Θs reduced by any arbitrary small positive integer value ε < Id , otherwise the solution for the optimal VR parameters (ti − qi − hi · C0 ) − (Πs − Θs ) hi ≤ would be Θu = Θs + Id − ε and Πu = Πs + Id − ε. Π s − Θs But a reduction of the optimal VR period Πu is pos- hi · C0 ti − qi hi + ≤ −1 sible without violating the demand requirements, since Π s − Θs Π s − Θs (Πu − ε) has two effects on the VR supply: hi · (Πs − Θs + C0 ) ti − qi ≤ −1 1. the initial delay ∆ = 2·((Πu − ε) − Θu ) decreases Π s − Θs Π s − Θs and, hi · (Πs − Θs + C0 ) ≤ ti − qi − (Πs − Θs ) 2. the replenishment periods are shortened, thus the ti − qi − (Πs − Θs ) hi ≤ VR still supplying the budget Θu just in shorter pe- Π s − Θ s + C0 riods. ti − qi − (Πs − Θs ) Hence, additionally to Lemma 1 the VR utilisation hi = Π s − Θ s + C0 U (Π) increases also with Π : (Πu − ε) < Π < Πu . (10) Thus, proving that Πu denotes the largest period con- tributing to a local optimum on the optimal VR utilisa- After calculating the number of VR budget supplies tion graph U (Π). hi up to the time instant ti , the VR budget and period increase Id needs to be determined. 5.2. VR context switch included One possible value for Id is the required increase of In this section we extend the analysis provided in the initial VR parameters (Θs , Πs ) by Θd . It can be de- Section 5.1 by the context switch overhead, also denoted rived analogous to Equation 3. In order to determine Θd as virtual resource context switch overhead, that occurs under consideration of the VR context switch overhead during the scheduling of application components. (see Equation 11), the demand at each demand point is 7 qi + (hi · C0 ) qi deﬁned as qi + hi · C0 . VR supply s(t) qi + hi · C0 Θd = max − Θs i hi Θd (11) qi = max + C0 − Θs i hi Θs But Θd might provide a smaller than required incre- C0 Πd Time (t) ment for the initial VR parameters, leading to a shorter Πs VR period and a higher number of VR replenishment ti periods hi , resulting in a higher than required demand hi hi + 1 at ti . Figure 6 illustrates this scenario for the demand (a) Determine VR parameter increment point DPi including the expected cumulative VR con- text switch overhead for the predetermined number of qi + ((hi + 1) · C0 ) VR supply s(t) replenishment periods hi (represented by a diamond), and the VR supply function using the initial VR param- eters (Θs , Πs ). Θd Figure 6a shows how the two possible VR parame- ter increments can be determined in order to satisfy the demand requirement of the demand point DPi . Figure 6b depicts the VR supply function if the VR Time (t) parameters are increased by Θd . The resulting VR pe- hi hi + 1 riod is too small and therefore the demand at DPi is af- (b) VR parameter increased by Θd fected by an additional VR replenishment period. Con- sequently, the demand at DPi is higher than assumed VR supply s(t) for hi number of replenishment periods due to the over- head of an additional VR context switch. The demand point with the higher demand is represented by the small Πd triangle in Figure 6b Therefore, additionally to Θd , a second possible value for Id is determined. Time (t) In summary, the accumulated VR context switch hi hi + 1 overhead at the time instant ti of the demand point DPi and the resulting required demand (qi + hi · C0 ) is af- (c) VR parameter increased by Πd fected by the number of VR replenishment periods hi . Figure 6. VR parameter increment hi on the other hand is determined by the VR period at least as large as Πi . Πi . Since the required demand (qi + hi · C0 ) is implic- itly inﬂuenced by the VR period Πi , it must be ensured ∆ + hi · Πi > ti for demand point DPi that after the initial VR param- ti − ∆ eters (Θs , Πs ) are increased, the number of VR replen- Πi > hi ishment periods hi is equal to the value determined by ti − 2 · (Πs − Θs ) (12) Equation 10. Figure 6c illustrates the VR supply func- Πi > tion after the initial VR parameters (Θs , Πs ) were in- hi creased by Πd , ensuring that the demand point experi- ti − 2 · (Πs − Θs ) Πi = +1 ences hi number of VR replenishment periods and the hi demand requirement qi at the time instant ti is satisﬁed by the VR supply. Given a VR period constraint Πi for every demand To derive the condition for the aforementioned VR point DPi , the second possible increment Πd over the period constraint, it is assumed that the hi -th VR re- initial VR parameters (Θs , Πs ) can be determined as plenishment period ends right after the time instant ti speciﬁed in Equation 13. (see ﬁrst line in Equation 12). Using this condition, a Πd = max (Πi ) − Πs VR period constraint Πi can be determined for the de- i mand point DPi (see Equation 12). That means, for the ti − 2 · (Πs − Θs ) (13) demand point DPi , the value of the VR period has to be Πd = max − Πs i hi 8 Finally, the increase Id of the initial VR parameters sation Ua . In contrast to the VR utilisation Uu , the appli- (Θs , Πs ) is the maximum of either Θd or Πd (see Equa- cation component’s utilisation Ua does not include the tion 14). VR context switch overhead. Id = max(0, Θd , Πd ) (14) qi Ua = max (16) u i ti The optimal VR period upper bound Π and the cor- responding budget Θu , with VR context switch consid- Considering the bounds of the optimal VR utilisation, ered in the analysis, can be determined by the Equa- it can be concluded that Ua Uo Uu . tions 15. Given the VR’s and application component’s utilisa- Πu = Πs + Id tion, Uu and Ua , the maximum processor utilisation of (15) the VR context switch overhead can be determined as Θu = Θs + Id C0 Π = Uu − Ua . Considering the expected maximum 5.3. Algorithm VR context switch execution time C0 , a lower bound Πl on the optimal VR period can be derived as shown in This section presents a simple 6-step algorithm to de- Equation 17. termine the bandwidth optimal VR period upper bound for a given set of demand points. The analysis and equa- C0 = Uu − Ua tions presented in the previous sections are used to build Πl up Algorithm 2. (17) C0 Πl = max 1, In : Set of demand points and C0 Uu − Ua Out : VR period upper bound Πu and budget Θu 7. Example 1 Determine the demand point leading to the longest feasible VR initial delay (see Equation 7); This section demonstrates the application of Algo- 2 Determine the initial VR parameters (Θs , Πs ) (see rithm 2 and Equation 17 in order to derive the optimal Equation 8 and 9 ); VR period lower and upper bound from a given taskset 3 For each demand point DPi , calculate the number speciﬁcation. of VR supplies hi (see Equation 10); As an example, the taskset deﬁned in Table 1 is used 4 Calculate Θd and Πd (see Equation 11 and 13); for the illustration. The presented values are expressed 5 Determine the initial VR parameter increase Id (see in time ticks. Additionally, for the VR context switch Equation 14); overhead C0 , we assume a maximal execution time of 6 Calculate VR period upper bound Πu = Πs + Id 100 ticks. and the corresponding VR budget Θu = Θs + Id ; Given the taskset speciﬁcation (i.e. the worst-case execution time, period and deadline of tasks), for each Algorithm 2: Virtual resource period upper bound priority level a representative demand point is deter- mined. The time instants where the demand bound func- 6. VR period lower bound tion dbf (t) is examined, is presented in Table 3. For each priority level, the row with the time instant and the This section provides the deﬁnition for a VR period taskset demand deﬁning the representative demand point lower bound in the presence of VR context switch over- is highlighted. head. We use the VR period upper bound, determined in Section 5.2, in order to calculate a lower bound on the Table 3. Demand points VR period. Priority Task release Demand Demand According to the optimality deﬁnition in this paper, level times (t) dbf(t) time ratio the VR parameters leading to the lowest VR utilisation 1 (high) 1300 400 0.3077 are considered as the optimal VR parameters. As we showed in Theorem 1, the VR utilisation monotonically 1300 1200 0.9231 2600 1600 0.6154 increases for Π Πu , hence the period of the opti- 2 (medium) 3900 2000 0.5128 mal VR (with the lowest utilisation) must be less than 4600 2400 0.5217 or equal Πu . Consequently, the optimal VR utilisation Uo has to be less than or equal the utilisation Uu result- 1300 2200 1.6923 ing from the VR period upper bound Πu and the corre- 2600 2600 1.0000 sponding minimal VR budget Θu . 3900 3000 0.7692 3 (low) 4600 3400 0.7391 Furthermore, the maximum utilisation of the applica- 5200 4200 0.8077 tion component Ua is determined by the demand point 6500 4600 0.7077 with the highest demand qi per time interval ti (see 6800 5000 0.7353 Equation 16). Consequently, the optimal VR utilisation Uo cannot be less than the application component’s utili- After analysing the taskset demand at each priority 9 level, the set of demand points contains the following Additionally, it can be observed that, as already indi- elements: DP1 = (400, 1300); DP2 = (2000, 3900); cated in Section 2, the optimal VR period is not neces- DP3 = (4600, 6500). This data serves as input into sarily smaller than the shortest task deadline or period. Algorithm 2. As indicated in line 1 of Algorithm 2 the demand 8. Evaluation point DPs = (qs , ts ) leading to the longest feasible VR Given a set of demand points, the optimal VR period initial delay ∆ is determined. Among the three given de- upper bound and the corresponding minimal budget can mand points, DP1 = (400, 1300) satisﬁes the condition be efﬁciently determined by Algorithm 2. mini (ti − qi ). Therefore, DPs = DP1 = (qs , ts ) = In this section we investigate the quality of the opti- (400, 1300). mal VR upper bound parameters in contrast to the opti- The following step of Algorithm 2 (see line 2) calcu- mal parameters that were determined by a more exhaus- lates the initial VR parameter values using Equation 8 tive brute-force approach. and 9. The calculated initial VR period is Πs = 900 and The brute-force approach calculated the minimal VR the budget is Θs = 500. budget for VR period values in the interval bounded Proceeding with the initial VR parameters (Θs , Πs ), by the longest task period of the examined taskset and the number of VR supplies hi is determined for each the VR context switch execution time. Due to the dis- demand point (see Equation 10). The resulting hi val- crete time space, the step size between two consecutive ues for the corresponding demand points DPi are as fol- VR period values was one. For example, if the longest lows: h1 = 1, h2 = 3 and h3 = 3. task period was equal to 6800 ticks and the VR context With the available data, the two possible candidate switch execution time was 100 ticks, then the interval values for the increase of the initial VR parameters are for VR period values was [100,6800]. Based on this in- calculated (line 4 of Algorithm 2). Using Equation 11, terval, the minimum VR budget was calculated for 6701 Θd = max(0, 267, 1134) = 1134, and using Equa- different VR period values. Finally, among the gener- tion 13, Πd = max(−400, 134, 1000) = 1000 is cal- ated VR parameter pairs, the one resulting in the lowest culated. processor utilisation was considered as the optimal VR The required increase Id of the initial VR parameters parameter. is determined as max(Θd , Πd ) = max(1134, 1000) = The evaluation focuses on the utilisation overhead 1134 (line 5 of Algorithm 2). that emerges from the use of VR parameters derived for Finally in line 6 of Algorithm 2 the optimal VR pe- the VR period upper bound in contrast to the use of op- riod upper bound is determined as Πu = Πs + Id = timal parameters. This utilisation overhead, or simply 2034, and the corresponding VR budget is Θu = Θs + the utilisation difference between the optimal VR and Id = 1634. the VR determined for the period upper bound, is ren- The next step is to determine the VR period lower dered in box-and-whisker diagrams (see example in Fig- bound Πl using Equation 16 and Equation 17. ure 7). These diagrams show the minimum, lower quar- Using Equation 16, the application component’s tile (25%), median, upper quartile (75%) and maximum maximum utilisation Ua = 4600/6500 = 0.7077 is de- value of the utilisation difference for scenarios described termined by the demand point DP3 = (4600, 6500). in the following paragraph. The maximum VR utilisation Uu is determined by the previously calculated VR period upper bound Πu and Maximum the corresponding budget Θu , i.e. Uu = 1634/2034 = 0.8033. Upper quartile Considering for this example a VR context switch Median execution time of 100 ticks, the VR period lower Lower quartile bound Πl can be calculated by Equation 17 as Πl = Minimum 100 max 1, 0.8033−0.7077 = 1046. Figure 7. Box-and-whisker bar illustration In summary, for the taskset deﬁned in Table 1, the VR parameter pair (Θ, Π), based on the optimal VR period The results of the empirical evaluation were obtained upper bound value, is equal to (1634, 2034). Figure 2 by generating 10000 random tasksets for each permuta- shows that these parameters are also the optimal values tion of the three test-case variables: for the taskset deﬁned in Table 1 and for an assumed VR • taskset utilisation (also referred to as Initial Target context switch execution time of 100 ticks. Utilisation (ITU) set to 10%, 40% and 70% of a full Although, in the presented example the VR period speed processor utilisation. upper bound and the corresponding VR budget represent • number of tasks per taskset equal to 5, 10, 20 and the optimal VR parameters, this may not always be the 30 case. In the case of a more exhaustive search for the • context switch overhead equal to 1, 10, 20, ..., 90, optimal VR parameters, the optimal VR period is bound 100 ticks by the interval [Πl , Πu ] = [1046, 1634]. The combination of the aforementioned test-cases 10 gives 132 different scenarios with 10000 taskset each, ues, an exhaustive search over the interval [Πl , Πu ] can leading to 1320000 tasksets for the entire evaluation. be carried out. Similar to the box-and-whisker diagrams, For each taskset, the task parameters were created by Figure 14, Figure 15 and Figure 16 depict the minimum, applying the generation of uniformly distributed utili- average (represented by a point) and maximum interval sation values, proposed by Bini and Buttazzo [2]. For length of [Πl , Πu ] observed among the test-cases that each utilisation value, a random task period in the in- were described at the beginning of this section. The ﬁg- terval [1, 10000] is generated. Given the task utilisation ures show that the application component’s utilisation and period, the calculation of its assumed worst-case ex- (ITU) has the largest impact on the VR period inter- ecution time is straight forward. val. The interval in which the optimal VR period can be The evaluation results are depicted in Figure 8, Fig- found increases with the application component’s ITU. ure 9 and Figure 10. They show the utilisation overhead The VR context switch overhead and the number of of the VR resulting from the VR period upper bound in tasks in the application component have just a small ef- contrast to the optimal VR, for tasksets with 10%, 40% fect on the interval [Πl , Πu ]. As the average values in- and 70% initial target utilisation (ITU). dicates, there is only a slight decrease in the VR period The results show that as the number of tasks in the ap- interval length as the VR context switch overhead and plication component and as the assumed context switch the number of tasks increase. time increases, the utilisation overhead decreases be- Figure 14, Figure 15 and Figure 16 also indicate that tween the optimal VR and the non-optimal VR deter- the interval containing the optimal VR period is signif- mined by the efﬁcient method presented in this paper. icantly reduced for application components with small Additionally, to express the processor utilisation used ITU and it increases with increasing ITU values. by the VR context switch, the assumed absolute value For the sake of clarity, the following example shall for the VR context switch execution time is compared demonstrates the information contained in the ﬁgures with the optimal VR period. The resulting processor presented in this evaluation section. utilisation denotes the percentage of the processing time We examine the test-case conﬁguration for tasksets that is required for the management of the VR. Although with 20 tasks, 40% ITU, and a VR context switch exe- the VR context switch time is inevitable, from the ap- cution time of 60 ticks. Figure 9c shows for the 10000 plication component’s point of view this percentage of tasksets that the utilisation of the VR determined by the processing time can be considered as wasted amount of Algorithm 2 was approximately 0%-18% higher than processor utilisation. the utilisation of the optimal VR (i.e. smallest possible Since the optimal VR period depends on the values VR utilisation) with the median around 3%. Figure 12c of the test-case variables and the temporal speciﬁcation shows that for the same test-case conﬁguration, the VR of the application component’s tasks, it is impossible context switch overhead consumes approximately 3%- to calculate and associate a single processor utilisation 10% of the processor time. value to an absolute VR context switch execution time The VR period interval containing the optimal VR value. Therefore, the minimum, lower quartile (25%), period may range from approximately 200 up to 6200 median, upper quartile (75%) and maximum value of the values as rendered in Figure 9. In the worst-case situa- optimal VR period is determined for the 10000 tasksets tion the interval length might be large, though in average of each test-case conﬁguration. In order to determine the interval length for the aforementioned test-case con- the processor utilisation of the VR context switch over- ﬁguration contains only approximately 1200 values. head, the absolute value of the VR context switch execu- In summary it can be stated that the presented algo- tion time is divided by each of the ﬁve aforementioned rithm to determine the VR period upper bound and the optimal VR period values. The results are rendered in corresponding budget for a given taskset, provides in the box-and-whisker diagrams. majority of cases an efﬁcient approach to calculate fea- In Figure 11, Figure 12 and Figure 13 the range of the sible VR parameters that can be used as means for tem- VR context switch overhead is presented for the corre- poral partitioning. sponding absolute value of the VR context switch execu- tion time. The diagrams show that as the ITU of the ap- 9. Conclusion plication component’s taskset and the VR context switch In this paper we showed that a lower and upper bound execution time increases, the smaller is the the processor on the VR period can be determined efﬁciently, in or- utilisation consumed by the VR context switch overhead der to narrow down the search space for bandwidth opti- and generally the distribution of these values. mal VR parameters. On the other hand, if an exhaustive It can be further concluded that with increasing ITU search for the optimal VR parameters is not feasible, the the optimal VR period increases as well, hence reducing VR parameters based on the VR period upper bound still the impact of the VR context switch overhead on the provide sufﬁciently good results. Our contribution can processor utilisation. be summarised as: In the case that the VR parameters based on the VR • we gave a fresh view on task demand functions and period upper bound do not provide sufﬁciently good val- their applicability to the parameter selection of vir- 11 tual resources, [13] C. D. Locke, D. R. Vogel, and T. J. Mesler. Building a • for a given taskset, we provided the necessary anal- predictable avionics platform in ada: A case study. In IEEE Real-Time Systems Symposium, pages 181–189, 1991. ysis in order to determine a lower and upper bound [14] A. K. Mok, X. A. Feng, and D. Chen. Resource partition on the VR period, thus limiting the search space for for real-time systems. In IEEE Real Time Technology and optimal VR parameters, Applications Symposium, pages 75–84, 2001. [15] S. Saewong, R. R. Rajkumar, J. P. Lehoczky, and M. H. • the evaluation showed that the VR parameters de- Klein. Analysis of hierarchical ﬁxed-priority scheduling. rived from the optimal VR period upper bound Euromicro Conference on Real-Time Systems, 00:173–181, might not be the optimal VR parameters, but they 2002. [16] I. Shin and I. Lee. Periodic resource model for composi- provide sufﬁciently good parameters if a brute- tional real-time guarantees. In Proceedings of the 24th IEEE force search is not an option even for the limited International Real-Time Systems Symposium, pages 2–13, 2003. interval of VR periods. In future we will examine the effect of non- preemptable jobs (e.g. packet transmission over net- works) on the parameter selection of virtual resources. Currently we are working on a method to derive the bandwidth optimal parameters (i.e. both budget and pe- riod) of a periodic virtual resource only from the speci- ﬁcation of a given taskset. Acknowledgements This work was funded in part by the EU FRESCOR project (contract number FP6/2005/IST/5-034026). We also thank Dr. Robert I. Davis for his comments on a previous draft version of this paper. References [1] L. Almeida and P. Pedreiras. Scheduling within temporal partitions: Response-time analysis and server design. In Fourth ACM International Conference On Embedded Soft- ware, pages 95–103, 2004. [2] E. Bini and G. C. Buttazzo. Measuring the performance of schedulability tests. Real-Time Systems, 30(1-2):129–154, 2005. [3] R. I. Davis and A. Burns. Hierarchical ﬁxed priority pre- emptive scheduling. In Proceedings of the 26th IEEE Real- Time Systems Symposium, pages 389–398, 2005. [4] R. I. Davis and A. Burns. An investigation into server pa- rameter selection for hierarchical ﬁxed priority pre-emptive systems. In 16th International Conference on Real-Time and Network Systems, pages 19–28, 2008. [5] Z. Deng and J. W.-S. Liu. Scheduling real-time applica- tions in an open environment. In IEEE Real-Time Systems Symposium, pages 308–319, 1997. [6] A. Easwaran, M. Anand, and I. Lee. Compositional analysis framework using edp resource models. In Proceedings of the 28th IEEE International Real-Time Systems Symposium, pages 129–138, 2007. [7] A. Easwaran, I. Shin, O. Sokolsky, and I. Lee. Incremen- tal schedulability analysis of hierarchical real-time compo- nents. In Proceedings of the 6th ACM & IEEE International conference on embedded software, pages 272–281, 2006. [8] X. A. Feng and A. K. Mok. A model of hierarchical real- time virtual resources. In Proceedings of the 23rd IEEE Real-Time Systems Symposium, pages 26–35, 2002. [9] T.-W. Kuo and C.-H. Li. A ﬁxed-priority-driven open en- vironment for real-time applications. In IEEE Real-Time Systems Symposium, pages 256–267, 1999. [10] J. P. Lehoczky, L. Sha, and Y. Ding. The rate monotonic scheduling algorithm: Exact characterization and average case behavior. In IEEE Real-Time Systems Symposium, pages 166–171, 1989. [11] G. Lipari and E. Bini. Resource partitioning among real- time applications. In Euromicro Conference on Real-Time Systems, pages 151–158, 2003. [12] G. Lipari and E. Bini. A methodology for designing hierar- chical scheduling systems. Journal of Embedded Comput- ing, 1(2):257–269, 2005. 12 replacements 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 8. VR utilisation difference between optimal and upper bound parameters for 10% ITU 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 9. VR utilisation difference between optimal and upper bound parameters for 40% ITU 13 replacements 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 30 30 Utilisation difference (%) Utilisation difference (%) 25 25 20 20 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 10. VR utilisation difference between optimal and upper bound parameters for 70% ITU 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 11. Processor utilisation consumed by the VR context switch at 10% ITU 14 replacements 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 12. Processor utilisation consumed by the VR context switch at 40% ITU 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 20 20 VR context switch utilisation (%) VR context switch utilisation (%) 15 15 10 10 5 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 13. Processor utilisation consumed by the VR context switch at 70% ITU 15 15 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 14. The optimal VR period search interval at 10% ITU 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 15. The optimal VR period search interval at 40% ITU 16 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (a) 5 tasks (b) 10 tasks 10000 10000 Search interval (ticks) Search interval (ticks) 8000 8000 6000 6000 4000 4000 2000 2000 0 0 0 10 20 30 40 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80 90 100 110 VR context switch execution time (ticks) VR context switch execution time (ticks) (c) 20 tasks (d) 30 tasks Figure 16. The optimal VR period search interval at 70% ITU 17

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