SIGMETRICS FY'04 Annual Report

July 2003 - June 2004
Submitted by: Leana Golubchik, SIGMETRICS Chair

SIGMETRICS had a good year.

The SIGMETRICS conference continues to be a high quality conference. We continue to receive a large number of submissions, and our acceptance rate at the 2004 conference was approximately 12%. This was a joint conference with Performance 2004, jointly sponsored with IFIP Working Group 7.3. Several workshops are now included as part of the conference's tutorials/workshops program (not all of these occur every year). These workshops include: Workshop on Mathematical performance Modeling and Analysis (MAMA), Practical Aspects of Performance Analysis (PAPA), Performance and Architecture of Web Servers (PAWS), Job Scheduling Strategies for Parallel Processing (JSSPP), and Algorithms and Architectures for Self-Managing Systems (AASMS). (The last one was sponsored jointly with ISCA in 2003.) We continue to support student travel through industrial funds.

The SIG is now supporting and is also in cooperation with several other conferences, in addition to its main one, including ACM SenSys and WOSP (International Workshop on Software and Performance).

The second ACM SIGMETRICS Achievement Award was given at the 2004 Conference. The recipient was Dr. Kenneth C. Sevcik.

The SIG is also exploring approaches to improve services offered to its members; one such example is the provision of the PE Grad Student Database on its web page, which includes a database of students in performance evaluation who are graduating and looking for academic and industrial jobs.

Some of the issues the SIG will consider in the next several years include:

-- keeping the annual conference vital
-- starting a new award for more junior people
-- membership retention
-- considering use of electronic proceedings
-- expanding sponsorship of other conferences and workshops

Prediction-Serving Systems

ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today. 

ACM Case Studies

Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.