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Found 13 papers, 4 papers with code
Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems
To address this gap, this paper evaluates the power consumption and performance characteristics of a single processing node within an edge cluster using a synthetic microbenchmark by varying the workload size and CPU frequency.
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation
Results show that our method reduces the energy consumption between 23-50 % on average for code generation tasks without significantly affecting accuracy.
Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings
To that end, in this work, we investigate the effect of important parameters on the performance and energy efficiency of LLMs during inference and examine their trade-offs.
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task.
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using Machine Learning (ML) algorithms, e. g., Time Series Classification (TSC).
A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments
However, traditional monitoring systems have a centralized architecture for both data plane and control plane, which increases latency, creates a failure bottleneck, and faces challenges in providing quick and trustworthy data in volatile edge environments, especially where infrastructures are often built upon failure-prone, unsophisticated computing and network resources.
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols.
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
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.
Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency.
Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning
Specifically, we propose a gradient boosting machine learning model for temperature prediction.
Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks
The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources.
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries.
