Name
Abstract | ||
|---|---|---|
Heavy-tails are a continual source of excitement and confusion across disciplines as they are repeatedly "discovered" in new contexts. This is especially true within computer systems, where heavy-tails seemingly pop up everywhere -- from degree distributions in the internet and social networks to file sizes and interarrival times of workloads. However, despite nearly a decade of work on heavy-tails they are still treated as mysterious, surprising, and even controversial. The goal of this tutorial is to show that heavy-tailed distributions need not be mysterious and should not be surprising or controversial. In particular, we will demystify heavy-tailed distributions by showing how to reason formally about their counter-intuitive properties; we will highlight that their emergence should be expected (not surprising) by showing that a wide variety of general processes lead to heavy-tailed distributions; and we will highlight that most of the controversy surrounding heavy-tails is the result of bad statistics, and can be avoided by using the proper tools. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2465529.2466587 | SIGMETRICS |
Keywords | Field | DocType |
continual source,bad statistic,computer system,degree distribution,proper tool,interarrival time,general process,new context,counter-intuitive property,heavy-tailed distribution | Data science,Confusion,Social network,Computer science,Artificial intelligence,Distributed computing,The Internet | Conference |
Volume | Issue | ISSN |
41 | 1 | 0163-5999 |
Citations | PageRank | References |
4 | 0.55 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayakrishnan Nair | 1 | 72 | 20.59 |
| Adam Wierman | 2 | 1635 | 106.57 |
| Bert Zwart | 3 | 383 | 47.43 |