Timely Power-Aware Data Management in Embedded Systems
Primary Investigator
- KD Kang,
Professor, Department of Computer
Science, State University of New York at Binghamton
This work is supported, in part, by National Science Foundation (Award
Number: CNS-1526932).
Project Summary
In emerging embedded and cyber physical applications like smart cars,
micro grids, medical devices, and homeland security, data amounts
are increasing fast. Database support is essential in data-intensive
embedded applications, because developing them without database support
is hard and error-prone. Ideally, a database system needs to process
real-time data service requests, such as driving route recommendations
and electricity demand/supply analysis, in a timely manner using fresh
data representing the current real-world status, e.g., the present
traffic or electric grid status. It is also important the database
should consume minimal power considering stringent power constraints
in embedded systems. Achieving this vision is challenging for several
reasons, including dynamic workloads varying in time depending
on the current real world status, severe data/resource contention,
and computational costs for updating temporal data. Moreover, the
timeliness, data freshness, and power efficiency may compete with
each other. If higher priority is given to user queries, their
timeliness can be improved at the cost of the decreased data
freshness or vice versa. Simply consuming more power to support
the desired timeliness and data freshness is not desirable.
Unfortunately, state-of-the-art database systems may fall short of
power-aware real-time data services. Non-real-time databases unaware
of timing and data freshness requirements may perform poorly in
these applications. Most existing real-time and embedded databases can
provide no guarantee on the desired timeliness and data freshness.
Neither are they power-aware. This can be a serious problem, since the
unbounded tardiness or data staleness may result in a traffic jam,
power outage, or homeland security problem. Although research on
energy-efficient databases mainly in data center contexts has
recently begun, real-time deadlines and data freshness requirements
are not considered. Despite the importance, related work on power
management in real-time databases, chiefly targeting embedded
applications, is surprisingly scarce. Due to the power ignorance,
deploying real-time databases may become increasingly difficult and
costly. To bridge the gap, in-depth research will be performed in this
work to investigate effective fundamental approaches for power-aware
real-time data services in emerging embedded and cyber physical
systems with significant broadrer impacts. This project will investigate
novel methods to support the desired timeliness and data freshness even
in the presence of dynamic workloads, while substantially
decreasing the power consumption in real-time embedded databases (RTEDBs).
Publications
Journals
-
Di Mu, Mo Sha, Kyoung-Don Kang, and Hyungdae Yi,
"Radio Selection and Data Partitioning for Energy-Efficient Wireless Data
Transfer in Real-Time IoT Applications,"
Ad Hoc Networks, Special Issue on Algorithms, Systems and Applications
for Distributed Sensing, vol. 107, pp. 1-11, October 2020.
-
K. D. Kang, L. Chen, H. Yi, B. Wang, M. Sha,
Real-Time Information Derivation from Big Sensor Data via Edge Computing,
Big Data and Cognitive Computing,
Special Issue on Cognitive Services Integrating with Big Data, Clouds and IoT,
Vol. 1, Issue 5, pp. 1-24, October, 2017.
Conference Papers
- A. Vora, P.-X. Thomas, R. Chen, K. D. Kang,
CSI Classification for 5G Via Deep Learning,
In Proceedings of the IEEE Vehicular Technology Conference,
September 22 - 25, 2019, Honolulu, Hawaii, USA.
- D. Fernando, K. D. Kang, Y. Zhou, An Adaptive Closed-Loop Approach for Timely
Data Services, In Proceedings of the 23rd IEEE International Conference on Embedded and Real-Time
Computing Systems and Applications (RTCSA '17), August 16 - 18, 2017, Hsinchu, Taiwan. (Longer version: Technical Report CS-TR-17-KD01, Department of Computer Science, State University of
New York at Binghamton)