The Cost of Mitigating Power Law Delay in Random Access Networks

TL;DR

This paper analyzes the delay distribution in random access networks, revealing a fundamental tradeoff between delay tail decay and throughput stability, and proposes polynomial backoff as a practical solution for delay-sensitive applications.

Contribution

It provides a systematic analysis of backoff functions, establishing conditions for finite delay moments and stable throughput, and advocates polynomial backoff as an effective alternative to exponential backoff.

Findings

Exponential backoff causes heavy-tailed delay distributions.

Polynomial backoff achieves finite delay moments and good throughput.

Tradeoff exists between delay tail decay and throughput stability.

Abstract

Exponential backoff (EB) is a widely adopted collision resolution mechanism in many popular random-access networks including Ethernet and wireless LAN (WLAN). The prominence of EB is primarily attributed to its asymptotic throughput stability, which ensures a non-zero throughput even when the number of users in the network goes to infinity. Recent studies, however, show that EB is fundamentally unsuitable for applications that are sensitive to large delay and delay jitters, as it induces divergent second- and higher-order moments of medium access delay. Essentially, the medium access delay follows a power law distribution, a subclass of heavy-tailed distribution. To understand and alleviate the issue, this paper systematically analyzes the tail delay distribution of general backoff functions, with EB being a special case. In particular, we establish a tradeoff between the tail decaying rate of medium access delay distribution and the stability of throughput. To be more specific, convergent delay moments are attainable only when the backoff functions $g(k)$ grows slower than exponential functions, i.e., when $g(k)\in o(r^k)$ for all $r>1$. On the other hand, non-zero asymptotic throughput is attainable only when backoff functions grow at least as fast as an exponential function, i.e., $g(k)\in\Omega(r^k)$ for some $r>1$. This implies that bounded delay moments and stable throughput cannot be achieved at the same time. For practical implementation, we show that polynomial backoff (PB), where $g(k)$ is a polynomial that grows slower than exponential functions, obtains finite delay moments and good throughput performance at the same time within a practical range of user population. This makes PB a better alternative than EB for multimedia applications with stringent delay requirements.