Energy-Aware Multi-Agent Server Consolidation in Federated Clouds

2013 ◽  
pp. 72-81 ◽  
Author(s):  
Alessandro Ferreira Leite ◽  
Alba Cristina Magalhaes Alves de Melo
Keyword(s):  
Energy Aware ◽  
Server Consolidation ◽  
Multi Agent

scholarly journals Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Soyi Jung ◽  
Won Joon Yun ◽  
Joongheon Kim ◽  
Jae-Hyun Kim
Keyword(s):  
Big Data ◽  
Reinforcement Learning ◽  
Resource Sharing ◽  
Learning Algorithm ◽  
Energy Resource ◽  
Energy Aware ◽  
Aerial Surveillance ◽  
Performance Improvements ◽  
Multi Agent ◽  
Energy Sharing

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

Comprehensive survey on energy-aware server consolidation techniques in cloud computing

2021 ◽  
Author(s):  
Nisha Chaurasia ◽  
Mohit Kumar ◽  
Rashmi Chaudhry ◽  
Om Prakash Verma
Keyword(s):  
Cloud Computing ◽  
Energy Aware ◽  
Server Consolidation ◽  
Comprehensive Survey

scholarly journals Energy-Efficient Navigation of an Autonomous Swarm with Adaptive Consciousness

2021 ◽  
Vol 13 (6) ◽  
pp. 1059
Author(s):  
Jawad Naveed Yasin ◽  
Huma Mahboob ◽  
Mohammad-Hashem Haghbayan ◽  
Muhammad Mehboob Yasin ◽  
Juha Plosila
Keyword(s):  
Energy Efficient ◽  
Self Awareness ◽  
Multi Agent System ◽  
Energy Aware ◽  
Safe Navigation ◽  
Turn On ◽  
Moving Obstacles ◽  
Management Algorithm ◽  
Multi Agent ◽  
Share Information

The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents.

Sign in / Sign up

Export Citation Format

Share Document