[Tccc] CFP: Internet Computing Special Issue on Internet Workloads

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Internet Computing Special Issue

Internet Workloads: Measurement, Characterization and Modeling

(Mar/Apr 2011)

Final submissions due 1 July 2010

Please email the guest editors a brief description of the article you
plan to submit by 15 June 2010

Guest Editors: Virgilio A. F. Almeida (virgilio@dcc.ufmg.br) and

Jussara M. Almeida (jussara@dcc.ufmg.br)

The Internet has a number of popular applications and services
experiencing workloads with very different, non-trivial and unique
properties.

Examples are online social networking and Web 2.0, e-business, search,
streaming, malware, Peer-to-Peer, grid, and mobile applications.

Real workload characterization and modeling provide key insights
into the cost-effective design, operation, and management of Internet-
based

services and systems. Nevertheless, some of the most fundamental
concepts and methods related to the characteristics of real Internet
workloads

are largely unknown to most Internet/Web practitioners. This is
further aggravated by the limited availability of real representative
workloads

for analysis, either due to privacy restrictions imposed by service
providers or due to the inherent limitations of partial/local
workloads in face

of the global nature of Internet applications. As a result, many
Internet-based systems and services are designed and built without
exploring the

inherent properties of their workloads.

This special issue of IC seeks original articles addressing relevant
aspects of real Internet workloads, looking at measurement and

collection of workloads, characterization and modeling. Articles can
be from research, operations, management, or architecture viewpoints.

Topics of interest include, but are not restricted to:

workload sampling and measurement techniques;
workload characterization techniques and results;
workload modeling approaches;
strategies to achieve representativeness under partial workloads;
privacy preserving issues;
synthetic workload generators;
workload temporal evolution and prediction schemes; and
case studies.
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