5 research outputs found
Network Coding Over SATCOM: Lessons Learned
Satellite networks provide unique challenges that can restrict users' quality
of service. For example, high packet erasure rates and large latencies can
cause significant disruptions to applications such as video streaming or
voice-over-IP. Network coding is one promising technique that has been shown to
help improve performance, especially in these environments. However,
implementing any form of network code can be challenging. This paper will use
an example of a generation-based network code and a sliding-window network code
to help highlight the benefits and drawbacks of using one over the other.
In-order packet delivery delay, as well as network efficiency, will be used as
metrics to help differentiate between the two approaches. Furthermore, lessoned
learned during the course of our research will be provided in an attempt to
help the reader understand when and where network coding provides its benefits.Comment: Accepted to WiSATS 201
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile
applications, uniformly sustaining high-performance inference on mobile has
been elusive due to the excessive computational demands of modern CNNs and the
increasing diversity of deployed devices. A popular alternative comprises
offloading CNN processing to powerful cloud-based servers. Nevertheless, by
relying on the cloud to produce outputs, emerging mission-critical and
high-mobility applications, such as drone obstacle avoidance or interactive
applications, can suffer from the dynamic connectivity conditions and the
uncertain availability of the cloud. In this paper, we propose SPINN, a
distributed inference system that employs synergistic device-cloud computation
together with a progressive inference method to deliver fast and robust CNN
inference across diverse settings. The proposed system introduces a novel
scheduler that co-optimises the early-exit policy and the CNN splitting at run
time, in order to adapt to dynamic conditions and meet user-defined
service-level requirements. Quantitative evaluation illustrates that SPINN
outperforms its state-of-the-art collaborative inference counterparts by up to
2x in achieved throughput under varying network conditions, reduces the server
cost by up to 6.8x and improves accuracy by 20.7% under latency constraints,
while providing robust operation under uncertain connectivity conditions and
significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile
Computing and Networking (MobiCom), 202