IRIS: A new reclaiming algorithm for server-based real-time systems

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1 IRIS: A new reclaiming algorithm for server-based real-time systems Luca Marzario and Giuseppe Lipari Scuola Sup. S.Anna, Pisa, Italy marzario, lipari@sssup.it Patricia Balbastre and Alfons Crespo Universidad Politecnica de Valencia, Valencia, Spain patricia, alfons@disca.upv.es Abstract In this paper we present a new algorithm for CPU resource reservation in real-time systems that allows the coexistence of hard, soft and non real-time tasks. The proposed algorithm is specifically designed to handle computational overload. A task that needs more CPU-time than reserved can re-use the spare bandwidth, without interfering with the others tasks. With respect to other reclamation schemes, the novelty of the proposed algorithm is that the spare bandwidth is fairly distributed among the needing servers. The effectiveness of the algorithm is demonstrated with an extensive set of experiments. We also propose a methodology to set scheduling parameters depending on the type of the task and on the time constraints needed. 1. Introduction General purpose operating systems are not well suited for executing soft real-time applications, like multimedia applications or monitoring systems, because of their particular requirements in terms of delay and jitter limitation. On the other hand, hard real-time operating systems generally offer support only for hard real-time processes, providing a best effort service to non real-time processes. Abeni and Buttazzo [2] list the main e reasons why scheduling such applications in hard real-time systems is unpractical. In recent years, many researchers studied the problem of integrating hard, soft and non-real-time tasks. One of the best approaches is the resource reservation framework [10]. For soft real-time tasks, the notion of reservation guarantee has been introduced. This reservation ensures that an amount É of a resource will be available to the reserved task every period È Reservation guarantees for soft real-time tasks can also be applied to other kind of applications besides multime- This work has been partially supported by the OCERA project (IST ), funded by the European Commission dia systems. Mobile robotic systems are performed combining deliberative goal-oriented planning with reactive sensor driven operations. This results in an heterogeneous system with reactive activities and deliberative processes. Usually, the first ones have hard real-time constrains, while the second ones are implemented as soft real-time tasks. Hassan, Simo and Crespo [5] presented a real-time architecture to cope with the dynamic and flexible environment of behavior-based mobile robotic applications. The combination of hard and soft activities is also found in industrial control applications, where the control action of the regulator can be obtained in a first step by a hard real-time task, and if there is enough time, the control action is refined using the remaining time of the system (by a soft real-time task) [3]. Balbastre et al. proposed a framework in which tasks are divided into a mandatory part and in a optional computation, and the amount of this extra computation is guaranteed off-line. The main advantage is the ease of implementation of the system, since in this case the task model is simpler than imprecise computation models or multiple version tasks. Algorithms such as CBS [1] or GRUB [6, 4] fit perfectly to implement this kind of applications. However, as explained in the following sections, both CBS and GRUB suffer from certain shortcomings. This paper presents a new work-conserving algorithm called IRIS (Idle-time Reclaiming Improved Server), that presents significant improvements respect to CBS and GRUB. The main property of IRIS is that it guarantees a minimum budget in a fixed interval of time. This is a key issue for the applications explained before, and leads to a significant improvement in its behavior. 2. System model and notation In this research, we consider two types of tasks in the system: cyclic tasks and non cyclic tasks. A cyclic task consists of a main loop. At the end of every loop instance the task blocks waiting for some event. Each execution between two blockings is called job  We indicate with the worst-case execution time (WCET) of Cyclic tasks can be divided into periodic tasks that wait for a periodic

2 timer event with period Ì ; sporadic tasks that wait for external events with a minimum inter-arrival time, also called Ì ; aperiodic tasks that wait for external events with an unbounded inter-arrival time. We assume that the relative deadline of cyclic tasks is equal to Ì Each job  has associated an arrival time and a deadline We denote with Í Ì the utilization factor of task Moreover, cyclic tasks can be divided into two classes, depending on their criticality: hard real-time and soft real-time. For the hard real-time tasks, it must be guaranteed that every job completes before its deadline while for soft real-time task a deadline miss could result in a performance degradation but not in a critical fault. Acyclic tasks modelbatch activities that are continuously active for large intervals of time, like for example the process of compiling a program, or a long scientific simulation or calculation, or a control tasks polling an external sensor. Usually, they are not associated any temporal constraint. However, in some case it may be desirable to execute them at a certain minimum rate. A server is an abstract entity used by the scheduler to reserve a fraction of CPU-time to a task. Each server Ë is characterized by the period of the reservation È and by the reserved execution time per period É ;wedefineí É È the fraction of CPU-time reserved by server Ë, also called utilization factor. In addition, each server maintains its own internal variables that are updated by the scheduler depending on the server rules. One of these variables is the server priority. The servers are inserted in the priority queue of the scheduler. When a server is selected by the scheduler, the corresponding task is executed. While a task is executing, the budget of the server is decremented accordingly. We denote by Õ the current budget of server Ë When the budget is exhausted, the task is stopped until the budget is replenished. We denote with Ö the time at which the budget is replenished. Each server has a dynamic deadline that is used by the scheduler to order tasks in the priority queue, accordingly with EDF algorithm [7]. 3. Problem description Algorithm IRIS that we present in this paper is based on the Constant Bandwidth Server (CBS) of Abeni and Buttazzo [1]. The basic idea of the CBS algorithm is that, when the budget is exhausted, it is immediately recharged to Õ É and its dynamic deadline is postponed to È The CBS presents some drawbacks when serving acyclic tasks. In Figure 1a we show an example of such problems. Consider a system consisting of two acyclic tasks ½ and ¾ served by servers Ë ½ e Ë ¾, respectively. Suppose that at a certain instant ½ is the only active task in the system and executes without stopping itself. The associated server Ë ½ consumes all its budget, postponing its deadline several times so that it goes far away in the future. When task ¾ ar- τ 1 τ 2 τ 1 τ 2 Figure 1. (a) Problem of CBS when scheduling acyclic tasks. (b) Another problem of CBS: the server parameters are not respected. rives, server Ë ¾ is assigned a short deadline and, according to the EDF scheduler, it executes. When budget of Ë ¾ is entirely consumed, its deadline is postponed. However, since the deadline of server Ë ½ is far away, it is still the earliest deadline and continues to execute. Only after its own deadline becomes greater than the deadline of Ë ½ Ë ¾ is preempted and the first task can continue to execute. As you can see in Figure 1a, the servers do not execute É every È Moreover, the amount of execution in every period for server Ë ½ depends on the arrival time and on the period of server Ë ¾ We say that both servers suffer from deadline aging. Another problem can be highlighted by the following example. Consider a system scheduled by the CBS algorithm consisting of two tasks. Task ½ is acyclic it is served by server Ë ½ with budget É ½ ½and period È ½ The second task ¾ is periodic with period Ì ¾ ½and it is served by a server Ë ¾ with budget É ¾ ½¾and È ¾ ½ The resulting schedule is shown in Figure 1b. Task ½ is not scheduled as we expect. In particular, it executes as it was served by a server with budget É ¼ ½ and period Ƚ ¼ ½ Notice also that this behavior depends on the parameters of the other servers in the system. For example, if the second server had a budget of É ¾ ¾and a period of È ¾ ¾ the first task would be scheduled as it was served by a server with budget É ¼ ½ and period È ½ ¼ ¾ This problem is more subtle and depends on the fact that CBS algorithm immediately recharges the budget without suspending the tasks. This second problem can be solved by using the technique called hard reservation [10]. In a hard reservation, when the budget is exhausted, the corresponding task is suspended until a recharging time. Nevertheless, adding only this rule to the CBS algorithm makes it a non-work-conserving algorithm: the system is forced to be in idle state even if there are pending jobs. Summarizing, the main drawback of CBS algorithm is that it does not guarantee a minimum execution time to a task in a fixed interval of time. This is particularly negative for multimedia or interactive tasks, since the period of

3 forced inactivity, caused by a possible starvation after an overload situation, can result in a loss of quality of service and interactivity. To solve these problems, we decided to propose a novel algorithm called IRIS (Idle-time Reclaiming Improved Server) that overcomes the aging deadline problem, guarantees a minimum budget in a fixed interval of time, and it is work-conserving. 4. The IRIS Algorithm 4.1. Definition IRIS is based on the CBS [1]. It can be defined as follows: Each server Ë is associated to a task and is characterized by an ordered pair É È µ where É is the maximum budget and È is the relative deadline. The sum of all server bandwidths É È cannot exceed 1: È Ò ½ É È ½ Each server Ë maintains: a current budget Õ that is decreased while the served task is running; a server deadline that is used to insert the server in the EDF queue of the system scheduler; a recharging time Ö that is used to set the time to recharge the current budget when it has been exhausted. Each server can be in one of the following states: 1. Active: the served task is ready to execute or executing and the current budget Õ ¼ 2. Recharging: the served task is ready to execute but the server is waiting for replenishment of the exhausted budget; 3. Inactive: the served task completed the last job and has no more pending jobs. Inactive Active Recharging Figure 2. State transition diagram. The system maintains a ready queue, where all Active servers are ordered by deadline, and a recharging queue, where all Recharging servers are ordered by recharging time. Initially all servers are in the Inactive state. The current budget and the relative deadline associated with each server Ë are updated applying the following rules: 1. If a job  arrives at time t, (a) if the server is Inactive, È i. if Ø Õ É then Õ É Ø È ii. if Ø and Õ ¼ the server becomes Recharging and it is inserted in the recharging queue with Ö iii. otherwise the server becomes Active and it is inserted again in the ready queue with the same current budget and deadline. (b) if the server is Active or Recharging, the arrival is buffered and will be served later. 2. When the served task executes for Æ Õ Õ Æ 3. If the server is Active and Õ ¼ the server reaches the Recharging state and the recharging time is set to Ö 4. If the server is in the Recharging state and Ø Ö then the server become Active, Õ É and È 5. When the job finishes, (a) if there is another pending job, the server remains in the Active state; (b) otherwise it goes in Inactive state. 6. If at time Ø no server is in Active state and there is at least one server in Recharging state, (a) let Ë be the first server in the recharging queue (i.e. the one with the smallest recharging time), and let Æ Ö Ø for every server i in the recharging queue, Ö Ö Æ (b) every server Ë in the recharging queue with Ö Ø is removed from the recharging queue and inserted in the ready queue; its budget is recharged to Õ É and its deadline is set to Ø È The IRIS algorithm presents two main differences with respect to the original CBS algorithm. The first difference is that IRIS explicitly sets a recharging time Ö for each server, whereas the CBS algorithm immediately recharges the server that exhaust its budget. Therefore, the IRIS algorithm implements the hard reservation technique [10]. Second, and most important, the IRIS algorithm introduces a rule for advancing the recharging times of all the servers in the Recharging state when an idletime occurs. In this way, the IRIS algorithm efficiently reclaims the idle time of the system and distributes it among the needing servers.

4 4.2. Properties The IRIS algorithm maintains the original CBS schedulability property. It is also a fair algorithm in the sense that the spare time is equally distributed among the servers that need to execute more than the reserved CPU time. Before proving the theorems, we introduce a simplified scheduling algorithm that use the same rules of IRIS except Rule 6. This algorithm has the same behavior as IRIS exceptthatwhenallserversarein Recharging state, the CPU is forced to be idle until the next re-charging event. We will call it IRIS-HR (IRIS Hard Reservation). We are not going to implement IRIS-HR, but we use it only for the sake of demonstrating the properties of IRIS. Definition 1 We define the demand bound function of an IRIS-HR server Ë as Ë Øµ Ø É È We indicate with Ë Ø½µ Ë µ Ë Ø½µ Lemma 1 (Utilization factor) The demand bound function of an IRIS-HR server Ë with parameters É È µ in any intervals ؽ never exceed É È Proof The proof of this lemma is similar to the same property of the CBS algorithm (see [1]). We remand the reader for the full proof to [8]. Theorem 1 (Schedulability Property) Given È a set of periodic tasks with total utilization factor Í Ô È Í and a set of IRIS servers with utilization factor Í Í the whole set is schedulable by EDF if and only if Í Ô Í ½ For space limitation, we do not report the proof of this theorem here. The proof can be found in [8]. Theorem 2 (Hard Schedulability) A hard task with period Ì and WCET is schedulable by an IRIS server with parameters É and È Ì if and only if is schedulable with EDF. Proof For any job of a hard task we have that Ö ½ Ö È and É Hence, by definition of the IRIS, each server is assigned a deadline Ö È equal to the job deadline, and it is scheduled with a budget É Moreover, since É each job finishes before the budget is exhausted, the server deadline assigned to a job is never postponed and is exactly the same as the one used by EDF; moreover, the server will never reach the Recharging state so the rule 6 will never be applied. By Theorem 1 the job will never miss its deadline. Note that Theorem 2 is valid both for IRIS and IRIS-HR since the Recharging state will never be reached and therefore Rule 6 is never applied. Property 1 (Minimal execution time guarantee) Let ؽ be an interval such that server Ë serving task is never inactive. If ؽ ¾È É then executes at least É unit of time in the interval ؽ Proof Let Ö be the first replenishing time for server Ë in ؽ We now analyze all possible case that current budget Õ can assume at time ؽ: Õ É This means that the budget has been replenished at time ؽ È É µ Ö ½ ؽ Contextually, the deadline is set to Ö ½ È In the worst case, Ö ½ ؽ so that the server deadline at time ؽ is ؽ È Ø½ ¾È É Since the server will execute at least É up to by setting the theorem holds. Õ ¼ This means that the server executed for at list É since the last recharging time Ö ½ so ؽ Ö ½ É In the worst case the budget is just exhausted (ؽ Ö ½ É ) and there will be no idle time till ½ Ö ½ È hence Ö ½ ؽ È É At instant Ö the budget is replenished and the deadline is set to Ö È Ø½ ¾È É ;theserver will execute at least É from Ö up to By setting the theorem holds. ¼ Õ É This means that ؽ Ö ½ É Õ µ and ½ ؽ È É Õ µ In this case executes for at least Õ unit of time up to ½ In the worst case, at Ö ½ Ö ½ È the budget is replenished. will be scheduled at least at the time Ø Ö È É É This descends from Theorem 1, since otherwise the server will miss its deadline. Consider the instant ؽ ¾È É Ö ½ É Õ µ ¾È É Õ Since in the worst case is scheduled at Ø and executes till it executes in the interval Ø for Ø É Õ Hence in the interval ؽ execute at least Õ É Õ µé An interesting property of IRIS server is that it solves the deadline aging problem (see Section 3). Property 2 If is the deadline associated to the server Ë at time Ø we have that: Ø Ø È Proof This is easily proved since each time the server deadline is assigned, it is set to the current time plus the period of the server. The IRIS algorithm distributes the spare

5 time among the reclaiming servers in a fair way: the spare time of the system is redistributed to the servers proportionally to the reserved bandwidths. Since the allocation of CPU time is not fluid, we show the property only in intervals large enough with respect to the periods of the servers. For simplicity we analyze the case in which the served tasks are always ready to execute. We denote by Í ¼ Ø ¾ µ the bandwidth used by server Ë in the interval Ø ¾ : Í ¼ Ø É¼ ɼ µ ؽµ ¼ ½Ø ¾ µ Ø ¾ Ø ½ where É Øµ is the amount of budget used by server Ë until time Ø Theorem 3 (Fairness) Consider two IRIS servers Ë Ë serving tasks that are always ready to execute (the servers are always in the state Active or Re-charging). The following property holds: Ø ¾ Ø ¾ ¾È ÑÜ Í Í Ø ¾ ¾È µ Ø ¾ ¾È µ Í ¼ Ø ¾ µ Í ¼ Ø ¾ µ Í Í Ø ¾ ¾È µ Ø ¾ ¾È µ (1) where È ÑÜ ÑÜ È È µ Proof We first consider the IRIS-HR algorithm and then we show that the same property is valid for IRIS. Under the hypothesis, the only rule applied to set the budget and deadline, after the first assignment when the first request is raised, is Rule 4. This implies that at each reservation s period, each server execute exactly for É unit of time. The minimum total budget É ¼ ÑÒ assignedtoserver in any interval Ø ¾ is: É ¼ ÑÒ Ø ¾ µ This minimum corresponds to the case in which the budget has just been replenished at time É and exhausted at time ; moreover, at Ø ¾ the server is going to execute and the budget will exhaust at the deadline expiration time Ø ¾ É Similarly, the maximum total budget is: É ¼ ÑÜ Ø ¾ µ ½ É È This is the case when at the server is scheduled and will exhaust its budget at the deadline expiration É and at Ø ¾ É the budget has just been replenished and is exhausted at time Ø ¾ (the dual situation of the previous one). We now calculate the minimum ratio between the budget assigned respectively to two servers and in Ø ¾ : É ¼ ÑÒ Ø ¾ µ É ¼ Ø ¾ µ È É¼ ÑÒ Ø ¾ µ É ¼ ÑÜ Ø ¾ µ ½ Ð É È Ø ¾ È ½ Ñ ½ É É The ratio can be minimized as following: Ð È Ø ¾ È É È ¾ É Ø É ¾ È ¾ É ½ Ñ ½ Ø ¾ ¾È µ É È Ø ¾ ¾È µ É È where we defined Æ ÑÒ Ø ¾ ¾È µ Similarly to minimum ratio, maximum ratio is: É ¼ ÑÜ Ø ¾ µ É ¼ Ø ¾ µ ؽ ¾Èµ ɼ ÑÜ Ø ¾ µ É ¼ ÑÒ Ø ¾ µ The ratio can be maximized as following: Ð È È Ñ ½ É ½ É where Æ ÑÜ Finally, we have Í Í Æ ÑÒ Ð È ¾ É È ¾ É Ø ¾ ¾È µ É È Ø ¾ ¾È µ É È Ø½ ¾Èµ Ø ¾ ¾È µ É ¼ ÑÒ Ø ¾µ Ø ¾ É ¼ ÑÜ Ø½µ Ø ¾ Í ¼ Ø ¾ µ Í Ø ¼ ½Ø ¾ µ È È Í Í Æ ÑÒ Ñ ½ É ½ É Í Í Æ ÑÜ É ¼ ÑÜ Ø½µ Ø ¾ É ¼ Ø ÑÒ ½Ø ¾µ Í Æ ÑÜ Í Ø ¾ Now consider the schedule Á generated by IRIS. Applying Rule 6 the schedule is modified when the system is idle by anticipating of the same amount the activation time of all tasks that are in Recharging state. This anticipation does not modify the order and the distance between two consecutive recharging events. This means that, considering the two servers Ë and Ë we can map one-to-one every recharging instant and period of reservation of the IRIS-HR schedule Ö with the IRIS schedule Á Consider an interval Ø ¾ in Ö and the corresponding one Ø ¼ ½ ؼ ¾ in Á Since the total budget assigned in the two intervals is the same, the expression calculated above is still true if we substitute and Ø ¾ with Ø ¼ ½ and Ø ¼ ¾ Note that Equation 1 depends exclusively on the size of the interval and not on the position of the time window. The schedulability property (Theorem 1) is an important result because allow us to execute in the same system EDF, IRIS-HR and IRIS servers, assigning independently the processor utilization to each server with the only (obvious) con-

6 straint that the total utilization must be less than 1. This allows an easy design of system s bandwidth reservation (see section 5). IRIS automatically reclaims the spare time of the system. This is done applying the rule 6. The reclamation is done at a certain time Ø if and only if all active servers executed at least for the reserved time in the current period. Hence, IRIS prevents the reclamation of a server from delaying the execution of other servers or hard real-time tasks that have not yet executed in the current reservation period. Moreover, IRIS avoids the irregularities of the CBS schedules, which are caused by an early reclamation, as we have seen in Section 3. This is the main difference with respect to CBS algorithm. The regularity of the IRIS algorithm with respect to CBS has been also proved by properties 1 and System s Design All the properties mentioned in the previous section allow us to use a bandwidth reservation strategy to allocate a fraction of the CPU time to each task independently of the other tasks in the system. In particular, we can execute in the same system different types of task, cyclic and acyclic, hard and soft. In this section, we give some suggestion on how to assign the budget and the period to the different tasks in the the system. Hard real-time tasks. Hard real-time tasks can be scheduled directly by EDF or by a dedicated IRIS server. The second solution ensures that tasks executing for longer than estimated do not jeopardize the entire system. This is useful especially in the developing and debugging phase. Soft real-time tasks. In the case of periodic soft realtime tasks, we can set È to the task s period. For aperiodic tasks, the server period should be set equal to the maximum response time needed. To set É we have more freedom: since setting it to the WCET may waste the CPU utilization, we can set this parameter to some value between the task s average execution time and its WCET. The trade off is between the amount of reserved bandwidth and the number of deadline miss. Non real-time tasks. These tasks do not have time constraints. However, in many cases it could be desirable to execute them at a certain rate. In this case a server could be used to reserve a fraction of CPU time to each task. È must be set to the granularity of reservation, while É must be set proportionally to the needed bandwidth. Since we proved that that IRIS-HR servers can be scheduled together to IRIS servers, we can use a IRIS-HR server to force a task to use no more than the reserved bandwidth. This could be important in case we want to use an external control system to dynamically assign the bandwidth to a task, like in the case of feedback scheduling [9], to manage power consumption or simply to slow down a particular application. 6. Simulation results In this section, we show the effectiveness of the IRIS algorithm. The goal is to highlight two important characteristics of IRIS against CBS: good performance in overload situations, and minimal temporal utilization guarantee. A massive number of tests have been run using a synthetic workload model. In the following, we describe the simulations settings and the obtained results Performance in overloaded systems The task sets for these experiments were generated with the following characteristics: Three hard real-time periodic tasks, generated with random and Ì The bandwidth consumed by hard tasks is: Í Ö È ½ Two soft real-time tasks that serve sporadic jobs, whose inter-arrival and execution times are also randomly generated around the values É and È Atotal of 300 jobs are generated for each task. The soft load is: É Í Ó Ø È The variable measured is the average response time of sporadic tasks, when task sets are scheduled under IRIS and CBS. Being ÖØ the response time of the job  the average response time of soft real-time is defined as follows: ¼¼ ÖØ Ú ÖØ Ö ½ Ö ¼ In the first experiment, soft real-time tasks consume 15% of the total utilization of the system (Í Ó Ø ¼½) while hard task parameters are specified with Í Ö ranging from 0.2 to 0.6. We assume that the computation time of the jobs ( ) is similar to the budget of the server assigned to the task (É ). In this case, average response time is very similar in IRIS and CBS, although it is slightly lower in the case of IRIS. Experiment 2 has been generated with the same characteristics than Experiment 1, except that an overload situation is forced. We can create an overloaded system if some jobs have a much greater than its budget

7 (we call it heavy jobs). In the simulations, every 10 activations É The results are shown in Figure 3. In this case, IRIS performs much better than CBS, because CBS suffers from deadline aging, and when a heavy job arrives, the budget is replenished several times and the deadline is moved away. This way, the response time of the heavy job, and also the response time of future jobs, is highly increased. However, as IRIS does not replenish the budget immediately, the jobs that arrive after the heavy one do not suffer the consequences of the overload, maintaining a reasonable response time. In Experiments 3 and 4, shows the percentage of missed deadlines with both algorithms for the task sets generated in Experiment 4. The results show that the maximum deadline misses of IRIS is less than 30%, being always greater in CBS. Figure 5. Deadline misses under overload situation 6.2. Fairness of bandwidth allocation Figure 3. Response time under overload situation (Í Ó ±) hard tasks consumes 35% of the total utilization of the system (Í Ö ¼ ) while soft real-time tasks parameters É and È are generated with Í Ó Ø ranging from 0.1 to 0.5. Experiment 3 simulates a normal execution while Experiment 4 simulate an overload situation. Figure 4. Response time under overload situation (Í Ö ±) In both experiments, the performance of IRIS is better than CBS, with a great improvement in the overloaded systems (Figure 4). The number of missed tasks deadlines is also an important parameter that must be taken into account. Figure 5 In this section, we compare the bandwidth is assigned dinamycally in IRIS and CBS in order to prove that IRIS is more fair. In the first experiment, the workload consists of 6 greedy tasks ( ½ ), with É and È randomly generated, except budget for that is É ½ Task set parameters are generated to have different utilization (from 20 to 80%). In this case, the variable measured is the distance between two consecutive executions of task As this task has É ½ we want to see if the required utilization of the task is guaranteed for both algorithms. During the simulations, the number of occurrences of every value of the distance have been counted. The distance between two consecutive executions has been normalized to the period È Figure As it 6 can shows bethe seen, results. CBS executes with a great variability, and most of the times task executions are very close. But, due to deadline aging, when the budget is exhausted, the task scheduled under CBS can stay without executing for a long time. This is the reason why under CBS, the time between two consecutive executions can be up to 440% of the task period. However, a task scheduled under IRIS executes most of the times every period, or less. And the maximum interval between two executions is only 190% (¾È É ). This property of IRIS is specially important in systems where reducing jitter is a key issue, such in control systems. The second experiment consists of measuring the utilization of greedy tasks all over the execution of the system, and see what happens to utilization when a new greedy task arrives. The goal is to demonstrate that IRIS is more fair in distributing the idle time than CBS.

8 7. Conclusions and future work Figure 6. Comparison of distance between two consecutive executions Experiments have been done as follows: three acyclic tasks ½ ¾ and have been generated with server periods ranging from 10 to 30. First activation of ½ and ¾ is set to the initial instant (t=0), while first activation of is set to instant t=30. The variable measured is ͽ ؽµ ¼ Í ½ Similar results have been obtained with Í ¾ ¼ ؽµ Í ¾ but in Figure 7 are depicted results only for ½ Instants and Ø ¾ have been chosen to measure utilization in windows that moves over the total execution of the system. The size of the windows is the largest period the three tasks. This means that, in Figure 7, point 1 in X coordinates means the first window ( ¼Ø ¾ ÑÜÔÖÓ), point 2 is the second window ( ½ Ø ¾ ÑÜÔÖÓ ½), and so on. Figure Figure 7. Fairness: acyclic tasks 7 shows that, in CBS, the utilization of ½ rapidly goes to 0when arrives (t=30). And the same happens to ¾ As a consequence, CBS does not maintain the utilization required. However, IRIS maintains the required utilization of ½ even when new tasks arrives, or even in overload situations. In this paper, the IRIS algorithm has been presented. It is a new resource reservation scheduling algorithm expressly designed to deal with systems consisting of different types of tasks, cyclic and acyclic, periodic and aperiodic, hard and soft real-time tasks. IRIS enhances the CBS algorithm [1] by introducing an efficient reclamation mechanism. In addition, IRIS provides a fair distribution of the spare time to the needing tasks, and ensure a minimum execution time to each task in every interval of time. Simulations corroborated the theoretical results, showing the better performance of IRIS against CBS. Current research is focused on the implementation of IRIS in a realtime kernel. A first version has been released in RTLinux under the OCERA IST project. The goal is to use the benefits of this new algorithm with real applications. References [1] L. Abeni and G.Buttazzo. Integrating multimedia applications in hard real-time systems. In Proceedings of the 19th IEEE Real-Time Systems Symposium, Madrid, Spain, december IEEE. [2] Luca Abeni. Server mechanisms for multimedia applications. Technical Report RETIS TR98-01, Scuola Superiore S. Anna, [3] Patricia Balbastre, Ismael Ripoll, and Alfons Crespo. Schedulability analysis of window-constrained execution time tasks for real-time control. In 14th Euromicro Conference on Real-Time Systems., [4] G.Lipari and S.K. Baruah. Greedy reclaimation of unused bandwidth in constant bandwidth servers. In IEEE Proceedings of the 12th Euromicro Conference on Real-Time Systems, Stokholm, Sweden, June [5] H. Hassan, J. Simo, and A. Crespo. Flexible real-time mobile robotic architecture besed on behavioural models. Engineering applications of Artificial Inteligence, 14(2): , [6] G. Lipari. Resource Reservation in Real-Time Systems. PhD thesis, Scuola Superiore S.Anna, [7] C.L. Liu and J.W. Layland. Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the Association for Computing Machinery, 20(1), [8] Luca Marzario and Giuseppe Lipari. Properties of the iris scheduling algorithm. Technical Report ReTiS-TR04-02, Scuola Superiore S. Anna, [9] Luigi Palopoli, Tommaso Cucinotta, and Antonio Bicchi. Quality of service control in soft real-time applications. In Proc. of the IEEE 2003 conference on decision and control (CDC02), Maui, Hawai, USA, December [10] Raj Rajkumar, Kanaka Juvva, Anastasio Molano, and Shuichi Oikawa. Resource kernels: A resource-centric approach to real-time and multimedia systems. In Proceedings of the 4th Real-Time Computing Systems and Application Workshop. IEEE, November 1997.