514 research outputs found
Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment
In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to
empower various areas as a bridge between physical objects and the digital
world. Through virtualization and simulation techniques, multiple functions can
be achieved by leveraging computing resources. In this process, Mobile Cloud
Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key
factors to achieve real-time feedback. However, current works only considered
edge servers or cloud servers in the DT system models. Besides, The models
ignore the DT with not only one data resource. In this paper, we propose a new
DT system model considering a heterogeneous MEC/MCC environment. Each DT in the
model is maintained in one of the servers via multiple data collection devices.
The offloading decision-making problem is also considered and a new offloading
scheme is proposed based on Distributed Deep Learning (DDL). Simulation results
demonstrate that our proposed algorithm can effectively and efficiently
decrease the system's average latency and energy consumption. Significant
improvement is achieved compared with the baselines under the dynamic
environment of DTs
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
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