Tootfinder

This https://arxiv.org/abs/2307.09691 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach
Meeting the strict Quality of Service (QoS) requirements of terminals has imposed a signiffcant challenge on Multiaccess Edge Computing (MEC) systems, due to the limited multidimensional resources. To address this challenge, we propose a collaborative MEC framework that facilitates resource sharing between the edge servers, and with the aim to maximize the long-term QoS and reduce the cache switching cost through joint optimization of service caching, collaborative offfoading, and computation a…

Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks
Christoph Weilenmann, Alexandros Ziogas, Till Zellweger, Kevin Portner, Marko Mladenovi\'c, Manasa Kaniselvan, Timoleon Moraitis, Mathieu Luisier, Alexandros Emboras
https://arxiv.org/abs/2402.16628 https://arxiv.org/pdf/2402.16628
arXiv:2402.16628v1 Announce Type: new
Abstract: Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or "learning-to-learn". The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

MRSch: Multi-Resource Scheduling for HPC
Boyang Li, Yuping Fan, Matthew Dearing, Zhiling Lan, Paul Richy, William Allcocky, Michael Papka
https://arxiv.org/abs/2403.16298

MRSch: Multi-Resource Scheduling for HPC
Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancement forces cluster schedulers to consider multiple schedulable resources during decision-making. Existing scheduling studies rely on heuristic or optimization methods, which are limited by an inability to adapt to new scenarios for ensuring long-term scheduling performance. We present an intelligent scheduling agent nam…

RB5 Low-Cost Explorer: Implementing Autonomous Long-Term Exploration on Low-Cost Robotic Hardware
Adam Seewald, Marvin Chanc\'an, Connor M. McCann, Seonghoon Noh, Omeed Fallahi, Hector Castillo, Ian Abraham, Aaron M. Dollar
https://arxiv.org/abs/2402.08897

RB5 Low-Cost Explorer: Implementing Autonomous Long-Term Exploration on Low-Cost Robotic Hardware
This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor and outdoor terrains. The exploration consists of a methodology that extends frontier- and sampling-based exploration with a path-following …

MRSch: Multi-Resource Scheduling for HPC
Boyang Li, Yuping Fan, Matthew Dearing, Zhiling Lan, Paul Richy, William Allcocky, Michael Papka
https://arxiv.org/abs/2403.16298

MRSch: Multi-Resource Scheduling for HPC
Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancement forces cluster schedulers to consider multiple schedulable resources during decision-making. Existing scheduling studies rely on heuristic or optimization methods, which are limited by an inability to adapt to new scenarios for ensuring long-term scheduling performance. We present an intelligent scheduling agent nam…

This https://arxiv.org/abs/2301.00693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classificat…

This https://arxiv.org/abs/2301.00693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classificat…

This https://arxiv.org/abs/2206.04354 has been replaced.
link: https://scholar.google.com/scholar?q=a

Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing
This paper investigates a novel hybrid worker recruitment problem where the mobile crowd sensing and computing (MCSC) platform employs workers to serve MCSC tasks with diverse quality requirements and budget constraints, under uncertainties in workers' participation and their local workloads.We propose a hybrid worker recruitment framework consisting of offline and online trading modes. The former enables the platform to overbook long-term workers (services) to cope with dynamic service supply …

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal
https://arxiv.org/abs/2403.04665 http…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and pote…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal
https://arxiv.org/abs/2403.04665 http…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and pote…