Application performance variability caused by network contention
is a major issue on dragonfly based systems. This work-in-progress
study makes two contributions. First, we analyze real workload
logs and conduct application experiments on the production system
Theta at Argonne to evaluate application performance variability.
We find a strong correlation between system utilization and performance
variability where a high system utilization (e.g., above 95%)
can cause up to 21% degradation in application performance. Next,
driven by this key finding, we investigate a scheduling policy to mitigate
workload interference by leveraging the fact that production
systems often exhibit diurnal utilization behavior and not all users
are in a hurry for job completion. Preliminary results show that
this scheduling design is capable of improving system productivity
(measured by scheduling makespan) as well as improving user-level
scheduling metrics such as user wait time and job slowdown.
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A Scheduler for Smart Homes with Probabilistic User Preferences
Scheduling appliances is a challenging and interesting problem aimed at reducing energy consumption at a residential level. Previous work on appliance scheduling for smart homes assumes that user preferences have no uncertainty. In this paper, we study two approaches to address this problem when user preferences are uncertain. More specifically, we assume that user preferences in turning on or off a device are represented by Normal distributions. The first approach uses sample average approximation, a mathematical model, in computing a schedule. The second one relies on the fact that a scheduling problem could be viewed as a constraint satisfaction problem and uses depth-first search to identify a solution. We also conduct an experimental evaluation of the two approaches to investigate the scalability of each approach in different problem variants. We conclude by discussing computational challenges of our approaches and some possible directions for future work.
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- Award ID(s):
- 1757207
- PAR ID:
- 10123209
- Date Published:
- Journal Name:
- International Conference on Principles and Practice of Multi-Agent Systems
- Page Range / eLocation ID:
- 138-152
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-33792-6_9
