Worst case end-to-end response times for non-preemptive FP/DP* scheduling

In this paper, we are interested in real-time flows requiring quantitative and deterministic Quality of Service (QoS) guarantees. We focus more particularly on two QoS parameters: the worst case end-to-end response time and jitter. We consider a non-preemptive scheduling of flows, called FP/DP*, combining fixed priority and dynamic priority, where the dynamic priority of a flow packet is assigned on the first node visited by the packet in the network. Examples of such a scheduling are FP/FIFO* and FP/EDF*. With any flow is associated a fixed priority denoting the importance of the flow from the user point of view. The arbritation between packets having the same fixed priority is done according to their dynamic priority. A packet can be transmitted only if (i) there is no packet having a higher fixed priority and (ii) there is no packet having a higher dynamic priority. A classical approach used to compute the worst case end-to-end response time is the holistic one, but it leads to pessimistic upper bounds. We propose the trajectory approach to improve the accuracy of the results. Indeed, the trajectory approach only considers worst case scenarios experienced by a flow along its trajectory. It then eliminates scenarios that cannot occur in the network

Non-premptive Fixed Priority schedulingwith FIFO arbitration:uniprocessor and distributed cases

In this paper, we focus on non-preemptive Fixed Priority scheduling. We are interested in the worst case response time of flows, both in uniprocessor and distributed cases. On a processor, the number of available priorities is generally limited. If this number is less than the number of flows to be considered, several flows have to share the same priority. Such flows are assumed to be scheduled arbitrarily in the classical approach. We assume in this paper that these flows are scheduled FIFO. This assumption leads us to revisit classical results in the uniprocessor case. As we obtain response times less than or equal to the classical results, any flow set feasible with the classical approach is feasible with our approach. The converse is false, as shown by an example. Moreover, we determine the conditions leading to shorter response times. We then establish new results in a distributed context. We show how to compute an upper bound on the end-to-end response time of any flow. For this, we use a worst case analysis based on the trajectory approach

A DiffServ-MPLS solution offering real-time end-to-end guarantees

In this paper, we are interested in providing deterministic end-to-end guarantees to real-time applications in the Internet. We focus on two QoS (Quality of Service) parameters: the end-to-end response time and the end-to-end jitter, parameters of the utmost importance for such applications. We propose a solution, very simple to deploy, based on a combination of DiffServ and . The Expedited Forwarding (EF) class of the Differentiated Services (DiffServ) model is well adapted for real-time applications as it is designed for flows with end-to-end real-time constraints. Moreover MultiProtocol Label Switching (MPLS), when applied in a DiffServ architecture, is an efficient solution for providing QoS routing. The results of our worst case analysis enable to derive a simple admission control for the class. Resources provisioned for the EF class but not used by this class are available for the other classes

FP/EDF, a non-preemptive scheduling combiningfixed priorities and deadlines:uniprocessor and distributed cases

In this paper, we focus on a non-preemptive scheduling of sporadic flows, combining fixed priorities and deadlines. This scheduling is called FP/EDF. With any flow are associated a fixed priority denoting the importance of the flow and a delivery deadline. A packet m can be transmitted only if there is no waiting packet with a fixed priority higher than m and no waiting packet with the same fixed priority as m but with a smaller deadline. We are interested in the worst case response time of flows, both in uniprocessor and distributed cases. In the uniprocessor case, we prove that any sporadic flow set feasible with the classical Fixed Priority scheduling is feasible with FP/EDF. The converse is false, as shown by an example. Moreover, we show that when all flows sharing the same fixed priority have the same processing time, any sporadic flow set feasible with FP/FIFO is feasible with FP/EDF, but the converse is false. We then establish new results with FP/EDF in a distributed context, when all flows follow the same sequence of nodes. The absolute deadline of a packet that is considered by any scheduler is computed on the first node visited and then left unchanged by any other node. We show in such conditions how to compute an upper bound on the end-to-end response time of any flow. For this, we use a worst case analysis based on the trajectory approach. Results obtained for some configurations are exact. In all configurations, these results are compared with those provided by the classical holistic approach. We show that our results are largely better

Real-time end-to-end guarantees for the EF classwith and without traffic shaping

In this paper, we are interested in providing deterministic end-to-end guarantees to the Expedited Forwarding () class of the Differentiated Services (DiffServ) model. We focus on two Quality of Service (QoS) parameters: the end-to-end response time and the end-to-end jitter. As packets of any flow can experience variable network delays and sojourn times on each visited node, the inter-arrival times can be shorter than those on the source node and burst arrivals are possible. This flow distortion increases with the number of visited nodes. To cope with this distortion, traffic shaping has been introduced. We focus more particularly on two techniques of traffic shaping: jitter cancellation and token bucket. We then study the influence of traffic shaping on these two QoS parameters, independently of the scheduling policy for THEEF class. In this paper, we show how to compute the worst case end-to-end response time and jitter of any flow in the EF class with and without traffic shaping, assuming that the class has the highest priority and packets in this class are served FIFO. We then determine when each one of the three techniques (no traffic shaping, jitter cancellation and token bucket) is the most appropriate

Deterministic and probabilistic QoS guarantees for the EF class in a DiffServ/MPLS domain

In the Differentiated Services (DiffServ) architecture, the Expedited Forwarding EF class has been proposed for applications requiring low end-to-end packet delay, low delay jitter and low packet loss (e.g. voice and video applications that are delay and jitter sensitive). In this paper, we focus on the quantitative Quality of Service (QoS) guarantee that can be granted to an EF flow in terms of end-to-end delay. Two approaches are presented. The deterministic one is based on a worst case analysis and leads to a deterministic bound which is infrequently reached. The probabilistic approach, based on the probability density function of the response time, is introduced to evaluate the probability of missing a given deadline. This study shows that delays much smaller than the deterministic bound can be guaranteed with probabilities close to one. An admission control derived from these results is then proposed, providing a probabilistic QoS guarantee to EF flows

Schedulability analysis of flows scheduled with FIFO: application to the expedited forwarding class

In this paper, we are interested in real-time flows re-quiring quantitative and deterministic QoS (Quality of Service) guarantees. We focus more particularly on two QoS parameters: the worst case end-to-end response time and jitter. We consider a FIFO (First In First Out) scheduling of flows. The FIFO scheduling is the simplest one to implement and very used. We first es-tablish a bound on the worst case end-to-end response time of any flow in the network, using the trajectory approach. We present an example illustrating our re-sults. Finally, we show how to apply these results to the EF (Expedited Forwarding) class in a DiffServ (Differ-entiated Services) architecture. 1. Context and motivation