When viewing the Technical Program schedule, on the far righthand side
is a column labeled "PLANNER." Use this planner to build your own
schedule. Once you select an event and want to add it to your personal
schedule, just click on the calendar icon of your choice (outlook
calendar, ical calendar or google calendar) and that event will be
stored there. As you select events in this manner, you will have your
own schedule to guide you through the week.
You can also create your personal schedule on the SC11 app (Boopsie) on your smartphone. Simply select a session you want to attend and "add" it to your plan. Continue in this manner until you have created your own personal schedule. All your events will appear under "My Event Planner" on your smartphone.
A Similarity Measure for Time, Frequency, and Dependencies in Large-Scale Workloads
SESSION: Performance Evaluation and Analysis
EVENT TYPE: Paper
TIME: 2:30PM - 3:00PM
AUTHOR(S):Mario Lassnig, Thomas Fahringer, Vincent Garonne, Angelos Molfetas, Martin Barisits
ABSTRACT: Performance evaluations of large-scale systems require the use of representative workloads with certifiable similar or dissimilar characteristics. To quantify the similarity of the characteristics, we describe a novel measure comprising two efficient methods that are suitable for large-scale workloads. One method uses the discrete wavelet transform to assess the periodic time and frequency characteristics in the workload. The second method evaluates dependencies in descriptive attributes via association rule learning. Both methods are evaluated to find the limits of their similarity spaces. Additionally, the wavelet method is evaluated against existing similarity methods and tested for noise robustness and random bias. An empirical study using workloads from seven operational large-scale systems evaluates the measure's accuracy. The results show that our measure is highly resistant to noise, well-suited for large-scale workloads, covers 87% of the possible similarity space, and improves accuracy by 24.5% and standard deviation by 10.8% when compared to existing work.