Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [10] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given task and across multiple tasks for a given period of time. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. SRMS feasibility test ensures that it is possible for a given periodic task set to share a given resource without violating any of the statistical QoS constraints imposed on each task in the set. The SRMS scheduling algorithm consists of two parts: a job admission controller and a scheduler. The SRMS scheduler is a simple, preemptive, fixed-priority scheduler. The SRMS job admission controller manages the QoS delivered to the various tasks through admit/reject and priority assignment decisions. In particular, it ensures the important property of task isolation, whereby tasks do not infringe on each other. 1.

Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part, with which a nondecreasing reward function is associated. Imprecise computation and Increased-Reward-with-Increased-Service models fall within the scope of this framework. In this paper, we address the reward-based scheduling problem for periodic tasks. For linear and concave reward functions we show: (a) the existence of an optimal schedule where the optional service time of a task is constant at every instance and (b) how to efficiently compute this service time. We also prove that RMS (with harmonic periods), EDF and LLF policies are optimal when used with the optimal service times we computed, and that the problem becomes NP-Hard, when the reward functions are convex. Further, our solution eliminates runtime overhead, and makes possible the use of existing scheduling disciplines.

This paper describes how Dynamic WindowConstrained Scheduling (DWCS) can guarantee real-time service to packets from multiple streams with different performance objectives. We show that: (1) DWCS can guarantee that no more than x packets miss their deadlines for every y consecutive packets requiring service, as long as the minimum aggregate bandwidth requirement of all real-time packet streams does not exceed the available bandwidth, (2) using DWCS, the delay of service to realtime packet streams is bounded even when the scheduler is overloaded, (3) DWCS can ensure that the delay bound of any given stream is independent of other streams, and (4) a fast response time for best-effort packet streams, in the presence of real-time packet streams, is possible. Furthermore, if a feasible schedule exists, each stream is guaranteed a minimum fraction of available bandwidth over a finite window of time. 1. Introduction Many real-time, distributed applications, such as telemedicine, virtual env...

AbstractÐIn a hard real-time system, it is assumed that no deadline is missed, whereas, in a soft or firm real-time system, deadlines can be missed, although this usually happens in a nonpredictable way. However, most hard real-time systems could miss some deadlines provided that it happens in a known and predictable way. Also, adding predictability on the pattern of missed deadlines for soft and firm real-time systems is desirable, for instance, to guarantee levels of quality of service. We introduce the concept of weakly hard real-time systems to model real-time systems that can tolerate a clearly specified degree of missed deadlines. For this purpose, we define four temporal constraints based on determining a maximum number of deadlines that can be missed during a window of time �a given number of invocations). This paper provides the theoretical analysis of the properties and relationships of these constraints. It also shows the exact conditions under which a constraint is harder to satisfy than another constraint. Finally, results on fixed priority scheduling and response-time schedulability tests for a wide range of process models are presented.

This paper considers the scheduling of soft real-time sporadic task systems under global EDF on an iden-tical multiprocessor. Though Pfair scheduling is theoretically optimal for hard real-time task systems on multiprocessors, it can incur significant run-time overhead. Hence, other scheduling algorithms that are not optimal, including EDF, have continued to receive considerable attention. However, prior research on such algorithms has focussed mostly on hard real-time systems, where, to ensure that all deadlines are met, ap-proximately 50 % of the available processing capacity will have to be sacrificed in the worst case. This may be overkill for soft real-time systems that can tolerate deadline misses by bounded amounts (i.e., bounded tardiness). In this paper, we derive tardiness bounds under preemptive and non-preemptive global EDF on multiprocessors when the total utilization of a task system is not restricted and may equal the number of pro-cessors. Our tardiness bounds depend on per-task utilizations and execution costs — the lower these values, the lower the tardiness bounds. As a final remark, we note that global EDF may be superior to partitioned EDF for multiprocessor-based soft real-time systems in that the latter does not offer any scope to improve system utilization even if bounded tardiness can be tolerated.

This paper presents a probabilistic approach to guarantee the performance of a real-time system. While traditional real-time system analysis tends to guarantee that each task instance will complete its execution before its absolute deadline (hard guarantee), our approach permits to estimate the probability that it will happen. Such a statistical guarantee is performed based on interarrival and execution times probability distributions, rather than their worst case values. The advantage of a probabilistic approach is a more efficient usage of system resources, allowing to give a certain level of deadline guarantee to task sets that the classical schedulability analysis would reject.

This paper describes an algorithm for scheduling packets in real-time multimedia data streams. Common to these classes of data streams are service constraints in terms of bandwidth and delay. However, it is typical for realtime multimedia streams to tolerate bounded delay variations and, in some cases, finite losses of packets. We have therefore developed a scheduling algorithm that assumes streams have window-constraints on groups of consecutive packet deadlines. A window-constraint defines the number of packet deadlines that can be missed in a window of deadlines for consecutive packets in a stream.

In certain real-time applications, ranging from multimedia to telecommunication systems, timing constraints can be more flexible than scheduling theory usually permits. For example, in video reception, missing a deadline is acceptable, provided that most deadlines are met. In this paper, we deal with the problem of scheduling hybrid sets of tasks, consisting of firm periodic tasks (i.e., tasks with deadlines which can occasionally skip one instance) and soft aperiodic requests, which have to be served as soon as possible to minimize their average response time. We propose and analyze an algorithm, based on a variant of Earliest Deadline First scheduling, which exploits skips to enhance the response time of aperiodic requests. Schedulability bounds are also derived to perform off-line analysis. 1. Introduction Many real-time applications require periodic activities that have to be cyclically executed at fixed rates and within specific deadlines. Typically, each periodic instance is ass...

In this paper, we study the problem of scheduling task sets with (m,k) constraints. In our approach, jobs of each task are partitioned into two sets: mandatory and optional. Mandatory jobs are scheduled according to their pre-defined priorities, while optional jobs are assigned to the lowest priority. We show that finding the optimal partition as well as determining the schedulability of the resultant task set are both NP-hard problems. A new technique, based on the General Chinese Remainder Theorem, is proposed to quantify the interference among tasks, which is then used to derive two partitioning approaches. Furthermore, a sufficient condition is presented to predict in polynomial time the schedulability of mandatory jobs. We prove that our partitions are never worse than those obtained in previous work. Experimental results also show significant improvement achieved by our approaches.

Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [LL73] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given task and across multiple tasks for a given period of time. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. SRMS feasibility test ensures that it is possible for a given periodic task set to share a given resource without violating any of the statistical QoS constraints imposed on each task in the set. The SRMS scheduling algorithm consists of two parts: a job admission controller and a scheduler. The SRMS scheduler is a simple, preemptive, fixed-priority scheduler. The SRMS job admission controller manages the QoS delivered to the various tasks through admi...