************************************************************************ ATM Forum Document Number: ATM_Forum/98-0407 ************************************************************************ TITLE: Effect of RM cell interval on ABR feedback: A simulation study using OPNET ************************************************************************ SOURCE: Rohit Goyal, Sonia Fahmy, Raj Jain, Bobby Vandalore The Ohio State University, Department of Computer and Information Science, 2015 Neil Ave, DL 395, Columbus, OH 43210 Phone: 614-688-4482 {goyal,fahmy,jain}@cse.wustl.edu Shobana Narayanaswamy MIL3 INC, 3400 International Drive, NW Washington, DC 20008 Phone: 202-364-4700 snaraya@mil3.com The presentation of this contribution at the ATM Forum is sponsored by NASA. ************************************************************************ DISTRIBUTION: ATM Forum Technical Committee Traffic Management Working Group ************************************************************************ DATE: July, 1998 (Portland) ************************************************************************ ABSTRACT: In this contribution, we present an analysis of the effect of changing RM cell intervals on ABR performance. We describe our newly developed ABR model in OPNET. This OPNET ATM model contains enhanced features to support the QoS capabilities of ATM, and a comprehensive ABR feedback model. We describe the various features of the ATM model, and use it for our simulation and analysis. ************************************************************************ NOTICE: This document has been prepared to assist the ATM Forum. It is offered as a basis for discussion and is not binding on the contributing organization, or on any other member organizations. The material in this document is subject to change in form and content after further study. The contributing organization reserves the right to add, amend or withdraw material contained herein. ************************************************************************ Effect of RM cell interval on ABR feedback: A simulation study using OPNET Rohit Goyal, Sonia Fahmy, Raj Jain, Bobby Vandalore The Ohio State University, Department of Computer and Information Science, 2015 Neil Ave, DL 395, Columbus, OH 43210 Phone: 614-688-4482 {goyal,fahmy,jain}@cse.wustl.edu Shobana Narayanaswamy MIL3 INC, 3400 International Drive, NW Washington, DC 20008 Phone: 202-364-4700 Snaraya@mil3.com ABSTRACT In this contribution, we present an analysis of the effect of changing RM cell intervals on ABR performance. We describe our newly developed ABR model in OPNET. This OPNET ATM model contains enhanced features to support the QoS capabalities of ATM, and a comprehensive ABR feedback model. We describe the various features of the ATM model, and use it for our simulation and analysis. 1 Introduction The ATM Forum has been investigating how to transport real-time multimedia applications over ATM networks. The SAA (Service Aspects and Applications) group at ATM Forum has approved specifications for transporting the MPEG-2 service [MPEG2] over ATM networks [VOD]. It is well known that bandwidth demands of video can be easily adjusted to meet the available bandwidth. In several situations, it may be cost-effective to adjust video quality to match the available bandwidth. There have been a limited number of studies that addressed the problem of transporting real-time video with feedback control. [LAKSH] shows how ABR explicit rate feedback can be used to transport compressed video. The video sources adapt to the required rate by modifying the quantization value of an MPEG compression algorithm. [KANAK1] discusses transporting packet video adaptively by using binary feedback. [KANAK2] proposes an adaptive congestion control scheme to transport packet video. Distributed feedback control can also be used to achieve fair bandwidth sharing among video sources. Recently, in [DUFF] an algorithm for transporting smoothed compressed video over explicit rate networks is given. In this study, a small number of frames are stored at the source and are used for smoothing the traffic. The rate adaptation is performed by using adaptive video encoding. In [VICKERS], multi-layered video source traffic is transported over a multicast network. The sources adapt to network congestion based on the feedback by adding or dropping video layers. In spite of these limited studies, video over feedback controlled networks is not a fully solved problem and a number of ideas remain to be explored. ABR sources send an RM cell after every Nrm-1 (usually Nrm = 32) cells. The sources adjust their rate when they receive these backward RM (BRM) cells. At high data rates, a low RM cell interval can result in a high frequency rate variations in the ABR feedback. One of the goals of transporting video over ABR is to minimize the rate variations, which in turn will reduce variations in the quality of service. Users want a constant quality of service in a real-time application such as real-time video. Hence, it is necessary to reduce the rate variations to provide low variations in quality of service. One way of reducing the ABR rate changes is to send RM cells less frequently, i.e., Nrm should be large, instead of 32. Sending RM cells at end of each video frame is one possible option. Another method to reduce variation is to increase the length of the averaging interval which some switch algorithms, such as the ERICA algorithm, use. This contribution has two main goals: 1. To present a preliminary study of the impact of varying the Nrm values on ABR performance. 2. To present a new ATM model in OPNET that is used for these experiments. 2 The OPNET Model OPNET is a modeling and simulation tool [MIL31] that provides an environment for analysis of communication networks. The tool provides a three layer modeling hierarchy. The highest layer, referred to as the network domain, allows the definition of network topologies. The second layer, referred to as the node domain, allows definition of node architectures (data flow within a node). The third layer (process domain) specifies logic or control flow among components in the form of a finite state machine. 3 The OPNET ATM Model Suite The OPNET ATM model suite (AMS) described in [MIL32] supports many of the characteristics of ATM networks. The model suite provides support for signaling, call setup and tear-down, segmentation and re- assembly of cells, cell transfer, traffic management and buffer management. Standard ATM nodes such as routers, stations, bridges and switches are provided to facilitate building of common topologies used for the design and analysis of ATM networks. Traffic management within AMS incorporates functions such as call admission control, policing using a continuous-state leaky bucket implementation (GCRA), call-based queuing, priority scheduling and collection of standard statistics such as end-to-end delay and end-to-end delay variation. Reference Topology The example network topology used for the design and development of traffic management functions within AMS represents an N-source configuration shown in Figure 1. Source and destination end-systems are connected to a pair of ATM switches that communicate via a bottleneck link. The node architecture for the end-system (source/destination) consists of AAL clients sending/receiving traffic to/from the AAL/ATM/PHY protocol stack. The AAL layer is responsible for segmentation of data traffic into AAL PDUs. The ATM layer (represented as four modules: management, layer, translation and switching) segments the AAL PDU into ATM cells and transmits the cells to the network. The management module is responsible for signaling. The translation module receives incoming traffic and directs it to the higher layer or back to the network based on the destination address. Figure 1 The OPNET ATM Model The node architecture for the switch consists of the ATM layer functions modeled as four modules as described above. The switch can have several input and output ports. ABR traffic management and feedback functions are implemented within the ATM switch module in the form of a finite state machine. Specification of QoS AAL clients representing traffic sources specify their QoS requirements using the application traffic contract attribute. This requirement is a combination of service category, traffic parameters and QoS parameters that the source would like the network to provide for both incoming and outgoing directions. Traffic parameters include the PCR, MCR, SCR and MBS. QoS parameters include the CTD, CDV and CLR for both directions. Service Category Requested Traffic Parameters Requested QoS Parameters Application Traffic Contract In order to be able to provide the requested QoS for a connection, intermediate devices may be configured to support various QoS levels. The switch buffer configuration attribute allows specification of QoS levels for each buffer. Cell streams belonging to different QoS levels may be buffered and serviced according to their QoS. The buffer configuration defines the buffer size, the maximum allocated bandwidth and minimum guaranteed bandwidth. The supported traffic parameters include PCR, MCR, SCR and MBS. The supported QoS parameters include CTD, CDV and CLR. Buffer Configuration Supported Traffic Parameters Supported QoS Parameters Switch Buffer Configuration ABR Traffic Management in OPNET ABR mechanisms allow the network to divide the available bandwidth fairly and efficiently among the active traffic sources. In the ABR traffic management framework, the source end systems limit their data transmission to rates allowed by the network. The network consists of switches that use their current load information to calculate the allowable rates for the sources. These rates are sent to the sources as feedback via resource management (RM) cells. The ABR traffic management model is a rate-based end-to-end closed-loop model. There are three ways for switches to give feedback to the sources. First, each cell header contains a bit called Explicit Forward Congestion Indication (EFCI), which can be set by a congested switch. Such switches are called binary or EFCI switches. Second, RM cells have two bits in their payload, called the Congestion Indication (CI) bit and the No Increase (NI) bit, that can be set by congested switches. Switches that use only this mechanism are called relative rate marking switches. Third, the RM cells also have another field in their payload called explicit rate (ER) that can be reduced by congested switches to any desired value. Such switches are called Explicit Rate switches. RM cells are generated by the sources and travel along the data path to the destination end systems. The destinations simply return the RM cells to the sources. Switches can use the virtual source/virtual destination (VS/VD) feature to segment the ABR control loop into smaller loops. In a VS/VD network, a switch can additionally behave both as a (virtual) destination end system and as a (virtual) source end system. As a destination end system, it turns around the RM cells to the sources from one segment. As a source end system, it generates RM cells for the next segment. This feature can allow feedback from nearby switches to reach sources faster, and allow hop-by-hop control. At the time of connection setup, ABR sources negotiate several operating parameters with the network. The first among these is the peak cell rate (PCR). This is the maximum rate at which the source will be allowed to transmit on this virtual circuit (VC). The source also requests a minimum cell rate (MCR) which is the guaranteed minimum rate. The network has to reserve this bandwidth for the VC. During the data transmission stage, the rate at which a source is allowed to send at any particular instant is called the allowed cell rate (ACR). The ACR is dynamically changed between MCR and PCR. At the beginning of the connection, and after long idle intervals, ACR is set to initial cell rate (ICR). Most resource management cells generated by the sources are counted as part of their network load in the sense that the total rate of data and RM cells should not exceed the ACR of the source. Such RM cells are called ``in- rate'' RM cells. Under exceptional circumstances, switches, destinations, or even sources can generate extra RM cells. These ``out-of-rate'' RM cells are not counted in the ACR of the source and are distinguished by having their cell loss priority (CLP) bit set, which means that the network will carry them only if there is plenty of bandwidth and can discard them if congested. The out-of-rate RM cells generated by the source and switch are limited to 10 RM cells per second per VC. One use of out-of-rate RM cells is for BECN from the switches. Another use is for a source, whose ACR has been set to zero by the network, to periodically sense the state of the network. Out-of-rate RM cells are also used by destinations of VCs whose reverse direction ACR is either zero or not sufficient to return all RM cells received in the forward direction. Note that in-rate and out-of-rate distinction applies only to RM cells. All data cells in ABR should have CLP set to 0 and must always be within the rate allowed by the network. Resource Management cells traveling from the source to the destination are called Forward RM (FRM) cells. The destination turns around these RM cells and sends them back to the source on the same VC. Such RM cells traveling from the destination to the source are called Backward RM (BRM) cells. Note that when there is bi- directional traffic, there are FRMs and BRMs in both directions on the VC. A direction bit (DIR) in the RM cell payload indicates whether it is an FRM or BRM. The ERICA Switch Scheme Implementation in OPNET The ERICA algorithm [SHIV] operates at each output port (or link) of a switch. The switch periodically monitors the load on each link and determines a load factor (z), the available ABR capacity, and the number of currently active virtual connections or VCs (N). A measurement or ``averaging'' interval is used for this purpose. These quantities are used to calculate the feedback which is indicated in RM cells. The feedback is given to the RM cells travelling in the reverse direction. Further, the switch gives at most one new feedback per source in any averaging interval. The key steps in ERICA are as follows: At the End of at Averaging Interval, total ABR Capacity is computed as the difference between the link capacity and the bandwidth used by higher priority traffic. The Target ABR Capacity is then computed as a fraction (typically a function of the queuing delay) of the total ABR capacity. The overload (z) and the fair share (FS) are calculated as: z ? ABR Input Rate / Target ABR Capacity FS ? Target ABR Capacity / N Where N is the number of active VCs. The maximum allocations given in the previous and current intervals are maintained as: MaxAllocPrevious ? MaxAllocCurrent MaxAllocCurrent ? FS When an FRM is received, the Current Cell Rate (CCR) in the RM cell is noted for the VC: CCR[VC] ? CCR_in_RM_Cell When a BRM is received Feedback is calculated as follows and inserted in the ER field of the cell: VCShare ? CCR[VC] / z IF z > 1+ ? THEN ER ? Max (FairShare, VCShare) ELSE ER ? Max (MaxAllocPrevious, FairShare, VCShare) MaxAllocCurrent ? Max(MaxAllocCurrent,ER) IF (ER > FairShare AND CCR[VC]