Using Bioinspired Meta-heuristics to Solve Reward-Based Energy-Aware Mandatory/Optional Real-Time Tasks Scheduling

2015 ◽  
Author(s):  
Matias Micheletto ◽  
Rodrigo Santos ◽  
Javier Orozco
Keyword(s):  
Real Time ◽  
Energy Aware ◽  
Tasks Scheduling

scholarly journals An Adaptive Scheduler for Real-Time Operating Systems to Extend WSN Nodes Lifetime

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Roberto Rodriguez-Zurrunero ◽  
Ramiro Utrilla ◽  
Elena Romero ◽  
Alvaro Araujo
Keyword(s):  
Operating System ◽  
Real Time ◽  
Operating Systems ◽  
Research Area ◽  
Rechargeable Batteries ◽  
Wireless Sensor ◽  
Portable Devices ◽  
Wireless Devices ◽  
Changing Environments ◽  
Tasks Scheduling

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource-constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real-time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.

scholarly journals Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

2020 ◽  
Author(s):  
Pooja Gupta ◽  
Volkan Dedeoglu ◽  
Kamran Najeebullah ◽  
Salil S. Kanhere ◽  
Raja Jurdak
Keyword(s):  
Real Time ◽  
Energy Aware ◽  
Demand Selection

scholarly journals DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

2021 ◽  
Vol 11 (1) ◽  
pp. 377
Author(s):  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh ◽  
Sima Sarv Ahrabi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Future Internet ◽  
Operating Conditions ◽  
Graphic User Interface ◽  
Energy Aware ◽  
Internet Applications ◽  
The Real ◽  
Delay Performance ◽  
Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

Energy-Aware Scheduling for Real-Time Systems

2016 ◽  
Vol 15 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Mario Bambagini ◽  
Mauro Marinoni ◽  
Hakan Aydin ◽  
Giorgio Buttazzo
Keyword(s):  
Real Time ◽  
Real Time Systems ◽  
Energy Aware ◽  
Energy Aware Scheduling ◽  
Time Systems
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