Energy consumption analysis for two embedded Java virtual machines

2007 ◽  
Vol 53 (5-6) ◽  
pp. 328-337 ◽  
Author(s):  
Sébastien Lafond ◽  
Johan Lilius
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Energy Consumption Analysis ◽  
Java Virtual Machines

scholarly journals Evaluating the Impact of Java Virtual Machines on Energy Consumption

2021 ◽  
Author(s):  
Zakaria Ournani ◽  
Mohammed Chakib Belgaid ◽  
Romain Rouvoy ◽  
Pierre Rust ◽  
Joël Penhoat
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Java Virtual Machines ◽  
The Impact

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Better state pictures facilitating state machine characteristic conjecture

2021 ◽  
Author(s):  
Dang Duy Bui ◽  
Kazuhiro Ogata
Keyword(s):  
Virtual Machines ◽  
Mutual Exclusion ◽  
State Machine ◽  
Formal Proof ◽  
The State ◽  
Lessons Learned ◽  
Java Virtual Machines ◽  
Core Part ◽  
Proximity Principle

AbstractThe mutual exclusion protocol invented by Mellor-Crummey and Scott (called MCS protocol) is used to exemplify that state picture designs based on which the state machine graphical animation (SMGA) tool produces graphical animations should be better visualized. Variants of MCS protocol have been used in Java virtual machines and therefore the 2006 Edsger W. Dijkstra Prize in Distributed Computing went to their paper on MCS protocol. The new state picture design of a state machine formalizing MCS protocol is assessed based on Gestalt principles, more specifically proximity principle and similarity principle. We report on a core part of a formal verification case study in which the new state picture design and the SMGA tool largely contributed to the successful completion of the formal proof that MCS protocol enjoys the mutual exclusion property. The lessons learned acquired through our experiments are summarized as two groups of tips. The first group is some new tips on how to make state picture designs. The second one is some tips on how to conjecture state machine characteristics by using the SMGA tool. We also report on one more case study in which the state picture design has been made for the mutual exclusion protocol invented by Anderson (called Anderson protocol) and some characteristics of the protocol have been discovered based on the tips.

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

scholarly journals Energy Consumption Analysis and Characterization of Healthcare Facilities in the United States

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3775 ◽  
Author(s):  
Khaled Bawaneh ◽  
Farnaz Ghazi Nezami ◽  
Md. Rasheduzzaman ◽  
Brad Deken
Keyword(s):  
United States ◽  
Energy Consumption ◽  
Energy Intensity ◽  
The United States ◽  
High Energy ◽  
Total Energy Consumption ◽  
Healthcare Facilities ◽  
Energy Consumption Analysis ◽  
Consumption Data ◽  
End Use

Healthcare facilities in the United States account for 4.8% of the total area in the commercial sector and are responsible for 10.3% of total energy consumption in this sector. The number of healthcare facilities increased by 22% since 2003, leading to a 21% rise in energy consumption and an 8% reduction in energy intensity per unit of area (544.8 kWh/m2). This study provides an analytical overview of the end-use energy consumption data in healthcare systems for hospitals in the United States. The energy intensity of the U.S. hospitals ranges from 640.7 kWh/m2 in Zone 5 (very hot) to 781.1 kWh/m2 in Zone 1 (very cold), with an average of 738.5 kWh/m2. This is approximately 2.6 times higher than that of other commercial buildings. High energy intensity in the healthcare facilities, particularly in hospitals, along with energy costs and associated environmental concerns make energy analysis crucial for this type of facility. The proposed analysis shows that U.S. healthcare facilities have higher energy intensity than those of most other countries, especially the European ones. This necessitates the adoption of more energy-efficient approaches to the infrastructure and the management of healthcare facilities in the United States.

Model Based Energy Consumption Analysis of Wireless Cyber Physical Systems

2017 ◽  
Vol 90 (8-9) ◽  
pp. 1191-1204 ◽  
Author(s):  
Ping Wang ◽  
Jing Liu ◽  
Jinlong Lin ◽  
Chao-Hsien Chu
Keyword(s):  
Energy Consumption ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Model Based ◽  
Energy Consumption Analysis
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