Data networks (2nd ed.)
On the self-similar nature of Ethernet traffic
ACM SIGCOMM Computer Communication Review - Special twenty-fifth anniversary issue. Highlights from 25 years of the Computer Communication Review
M|G|Infinity Input Processes: A Versatile Class of Models for Network Traffic
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Measurement-based characterization of 802.11 in a hotspot setting
Proceedings of the 2005 ACM SIGCOMM workshop on Experimental approaches to wireless network design and analysis
Power law and exponential decay of inter contact times between mobile devices
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Measured delay distribution in a Wireless Mesh Network test-bed
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Is ALOHA causing power law delays?
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Uniform approximation of the distribution for the number of retransmissions of bounded documents
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
| Hi-index | 0.01 |
Retransmissions serve as the basic building block that communication protocols use to achieve reliable data transfer. Until recently, the number of retransmissions were thought to follow a light tailed (in particular, a geometric) distribution. However, recent work seems to suggest that when the distribution of the packets have infinite support, retransmission-based protocols may result in heavy tailed delays and even possibly zero throughput. While this result is true even when the distribution of packet sizes are light-tailed, it requires the assumption that the packet sizes have infinite support. However, in reality, packet sizes are often bounded by the Maximum Transmission Unit (MTU), and thus the aforementioned result merits a deeper investigation. To that end, in this paper, we allow the distribution of the packet size L to have finite support. This packet is sent over an on-off channel {(Ai, ui)} with alternating available Ai and unavailable Ui periods. If L ≥ Ai, the transmission fails and we wait for the next period Ai+1 to retransmit the packet. The transmission duration is thus measured from the first attempt to a point when a channel available period larger than L. Under mild conditions, we show that the transmission duration distribution exhibits a transition from a power law main body to an exponential tail with Weibull type distributions between the two. The time scale to observe the power law main body is roughly equal to the average transmission duration of the longest packet. Both the power law main body and the exponential tail could dominate the overall performance. For example, the power law main body, if significant, may cause the channel throughput to be very close to zero. On the other hand, the exponential tail, if more evident, may imply that the system operates in a benign environment. These theoretical findings provide an understanding on why some empirical measurements suggest heavy tails and light tails for others (e.g., wireless networks). We use these results to further highlight the engineering implications from distributions with power law main bodies and light tails by analyzing two cases: (1) The throughput of on-off channels with retransmissions, where we show that even when packet sizes have small means and bounded support the variability in their sizes can greatly impact system performance. (2) The distribution of the number of jobs in an M/M/∞ queue with server failures. Here we show that retransmissions can cause long-range dependence and quantify the impact of the maximum job sizes on the long-range dependence.