Professor, CSE Dept, KSIT
Professor, CAOS, IISC
In all types of interactions with computational devices, users have grown accustomed to rapid response times. In the context of web browsing, a delay of about one second is quite likely to result in the user mentally “context switching” to another task, while a delay of ten seconds may persuade the user to abandon the web page altogether . This delay depends on the time for the network to transmit the page contents and the time for the browser to render these contents.
In this article, we will focus only on the first of these delays, known as the end to end network delay. This topic is described in popular computer network textbooks such as Peterson and Davie  (Section 1.5) and Forouzan  (Section 3.6). A more detailed explanation is given by Kurose and Ross , drawing on an analogy with a caravan of cars (corresponding to packets) traveling on a highway (link) with toll booths (switches) to illustrate the four sub-components of end to end delay, known as transmission delay, propagation delay, queuing delay and processing delay. We have found that our students find the following analogy more accessible.
Consider a group of N students who are asked to leave their classroom and attend a special lecture in the seminar hall in another building. Students then exit their classroom at a rate of R students per unit time and assemble as a group. The group walks the distance D to the seminar hall at a speed S. On the way, the group waits for M time units at the road crossing between classroom building and seminar hall on account of college vehicles travelling on the road. On reaching the seminar hall, the students take L time units to follow instructions to be able to hear the lecture. In this analogy, each student represents a single bit, and the entire group of N students represents a packet. The packet is transmitted from the source (the classroom) to the destination (the seminar hall) on a link (the path connecting their classroom to the seminar hall).
In this simplistic case, the transmission delay is the time for the entire packet (N students) to enter the link, which is N/R. The propagation delay is the time for the packet to travel the distance to the destination, which is D/S. The queuing delay is the time taken by which the packet is delayed on the way, which is M. Finally, the processing delay is the time for processing the entire packet, which is L. Hence, the end to end delay for the packet is:
This analogy can easily be made more complex, for instance by modeling switches, links with different transmission rates, multiple packets, etc. This pedagogical approach does appear to help students better understand each sub-component of end to end delay – we find that their performance on assessment items at the Familiarity level of mastery (as defined by the CS2013 reference curriculum defined by ACM-IEEE ) improves. However, students still struggle on assessment items aimed at higher levels of mastery (Usage and Assessment), such as questions that probe students on differences between these sub-components.
We believe that an experiential approach is essential for proper understanding of these concepts. In this article, we therefore describe a few experiments (consisting of a simple and minimal setup of two systems and one and/or two simple unmanaged switches), that enables readers to measure the individual sub-components of end to end delay, and compare these measurements with values predicted by the underlying theory. We have observed that students who compare measured vs. predicted values and think about causes of discrepancies (if any) gain a far better understanding of key ideas behind network delays, and display their mastery of these concepts in higher-order assessments.
Measuring Network Delays
Propagation delay corresponds to a time taken by a bit traversing on links from one end of the system to the other. If we assume that the speed S of electromagnetic/light waves in the link medium is constant, this delay depends purely upon (and is directly proportional to) the distance D (i.e., the total length of all links). This delay is only noticeable for satellite-based links (since communication satellites orbit about 36000 km above the earth’s surface.) It is miniscule in any local area network setup such as a college laboratory, or if the links are terrestrial (since D will be at most a few kilometers, whereas S will be close to the speed of light). Thus, we will assume that propagation delays are small enough to be ignored in our experiments.
Transmission delay is inversely proportional to the link bandwidth R (also called link speed) and directly proportional to the number of bits N in each packet. For dial-up links, R is typically 56kbps. For cellular 2G, R is of the order of a few kilobits per sec (kbps). For a 3G network, R varies from a few hundred kbps to 2Mbps. For a 4G network, R varies from 1Mbps to 20+ Mbps. Our experiments are based on Ethernet LAN, where R depends on the type of Ethernet: 10Mbps (Ethernet), 100Mbps (fast Ethernet) or 1000Mbps (Gigabit Ethernet). We will fix R in our experiments, and we will observe how the transmission delay changes as N changes.
Processing delay is the total time taken by every network element process and forward a data packet to the next device in the path, or to an application on the end system. In a network switch, for instance, the processing delay for a packet is the time taken by the switch to decide the outgoing interface on which this packet needs to be forwarded. This will involve processing the packet by extracting the destination address (IP or MAC), matching it with the switch’s forwarding table, and then determining the outgoing interface. Thus, this delay clearly depends on the compute power of each network element. For our experiments, we will introduce artificial delays of varying durations to simulate variations in compute power.
Queuing delay is the total time spent by a packet waiting in buffers of network elements (switches or routers) to get transmitted. This delay depends on the number of packets already in queue buffers of individual network elements, and on the queuing policy that each such element implements. Since the number of packets that are waiting will vary dynamically as per the network traffic, this delay can only be predicted accurately if the experimental setup is controlled and its traffic characteristics are known so that an appropriate queuing model can be applied.
Below we define few experiments that a reader should to do measure these delay components and compare these with observed values to develop a better understanding. At times, the measured values will differ from computed values and reader should develop a reasoning for the difference.