19 research outputs found
Middleware platform for distributed applications incorporating robots, sensors and the cloud
Cyber-physical systems in the factory of the future
will consist of cloud-hosted software governing an agile
production process executed by autonomous mobile robots
and controlled by analyzing the data from a vast number of
sensors. CPSs thus operate on a distributed production floor
infrastructure and the set-up continuously changes with each
new manufacturing task. In this paper, we present our OSGibased
middleware that abstracts the deployment of servicebased
CPS software components on the underlying distributed
platform comprising robots, actuators, sensors and the cloud.
Moreover, our middleware provides specific support to develop
components based on artificial neural networks, a technique that
recently became very popular for sensor data analytics and robot
actuation. We demonstrate a system where a robot takes actions
based on the input from sensors in its vicinity
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge
Cascade systems comprise a two-model sequence, with a lightweight model
processing all samples and a heavier, higher-accuracy model conditionally
refining harder samples to improve accuracy. By placing the light model on the
device side and the heavy model on a server, model cascades constitute a widely
used distributed inference approach. With the rapid expansion of intelligent
indoor environments, such as smart homes, the new setting of Multi-Device
Cascade is emerging where multiple and diverse devices are to simultaneously
use a shared heavy model on the same server, typically located within or close
to the consumer environment. This work presents MultiTASC, a
multi-tenancy-aware scheduler that adaptively controls the forwarding decision
functions of the devices in order to maximize the system throughput, while
sustaining high accuracy and low latency. By explicitly considering device
heterogeneity, our scheduler improves the latency service-level objective (SLO)
satisfaction rate by 20-25 percentage points (pp) over state-of-the-art cascade
methods in highly heterogeneous setups, while serving over 40 devices,
showcasing its scalability.Comment: Accepted at 28th IEEE Symposium on Computers and Communications
(ISCC), 202
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers
Today's cloud service architectures follow a "one size fits all" deployment
strategy where the same service version instantiation is provided to the end
users. However, consumers are broad and different applications have different
accuracy and responsiveness requirements, which as we demonstrate renders the
"one size fits all" approach inefficient in practice. We use a production-grade
speech recognition engine, which serves several thousands of users, and an open
source computer vision based system, to explain our point. To overcome the
limitations of the "one size fits all" approach, we recommend Tolerance Tiers
where each MLaaS tier exposes an accuracy/responsiveness characteristic, and
consumers can programmatically select a tier. We evaluate our proposal on the
CPU-based automatic speech recognition (ASR) engine and cutting-edge neural
networks for image classification deployed on both CPUs and GPUs. The results
show that our proposed approach provides an MLaaS cloud service architecture
that can be tuned by the end API user or consumer to outperform the
conventional "one size fits all" approach.Comment: 2019 IEEE International Symposium on Performance Analysis of Systems
and Software (ISPASS