12 research outputs found
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
The edge computing paradigm helps handle the Internet of Things (IoT)
generated data in proximity to its source. Challenges occur in transferring,
storing, and processing this rapidly growing amount of data on
resource-constrained edge devices. Symbolic Representation (SR) algorithms are
promising solutions to reduce the data size by converting actual raw data into
symbols. Also, they allow data analytics (e.g., anomaly detection and trend
prediction) directly on symbols, benefiting large classes of edge applications.
However, existing SR algorithms are centralized in design and work offline with
batch data, which is infeasible for real-time cases. We propose SymED -
Symbolic Edge Data representation method, i.e., an online, adaptive, and
distributed approach for symbolic representation of data on edge. SymED is
based on the Adaptive Brownian Bridge-based Aggregation (ABBA), where we assume
low-powered IoT devices do initial data compression (senders) and the more
robust edge devices do the symbolic conversion (receivers). We evaluate SymED
by measuring compression performance, reconstruction accuracy through Dynamic
Time Warping (DTW) distance, and computational latency. The results show that
SymED is able to (i) reduce the raw data with an average compression rate of
9.5%; (ii) keep a low reconstruction error of 13.25 in the DTW space; (iii)
simultaneously provide real-time adaptability for online streaming IoT data at
typical latencies of 42ms per symbol, reducing the overall network traffic.Comment: 14 pages, 5 figure
CloudSim Express: A Novel Framework for Rapid Low Code Simulation of Cloud Computing Environments
Cloud computing environment simulators enable cost-effective experimentation
of novel infrastructure designs and management approaches by avoiding
significant costs incurred from repetitive deployments in real Cloud platforms.
However, widely used Cloud environment simulators compromise on usability due
to complexities in design and configuration, along with the added overhead of
programming language expertise. Existing approaches attempting to reduce this
overhead, such as script-based simulators and Graphical User Interface (GUI)
based simulators, often compromise on the extensibility of the simulator.
Simulator extensibility allows for customization at a fine-grained level, thus
reducing it significantly affects flexibility in creating simulations. To
address these challenges, we propose an architectural framework to enable
human-readable script-based simulations in existing Cloud environment
simulators while minimizing the impact on simulator extensibility. We implement
the proposed framework for the widely used Cloud environment simulator, the
CloudSim toolkit, and compare it against state-of-the-art baselines using a
practical use case. The resulting framework, called CloudSim Express, achieves
extensible simulations while surpassing baselines with over a 71.43% reduction
in code complexity and an 89.42% reduction in lines of code
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
The advent of edge devices dedicated to machine learning tasks enabled the
execution of AI-based applications that efficiently process and classify the
data acquired by the resource-constrained devices populating the Internet of
Things. The proliferation of such applications (e.g., critical monitoring in
smart cities) demands new strategies to make these systems also sustainable
from an energetic point of view.
In this paper, we present an energy-aware approach for the design and
deployment of self-adaptive AI-based applications that can balance application
objectives (e.g., accuracy in object detection and frames processing rate) with
energy consumption. We address the problem of determining the set of
configurations that can be used to self-adapt the system with a meta-heuristic
search procedure that only needs a small number of empirical samples. The final
set of configurations are selected using weighted gray relational analysis, and
mapped to the operation modes of the self-adaptive application.
We validate our approach on an AI-based application for pedestrian detection.
Results show that our self-adaptive application can outperform non-adaptive
baseline configurations by saving up to 81\% of energy while loosing only
between 2% and 6% in accuracy
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</p>