Energy Consumption Optimized Scheduling Algorithm for Clustered VLIW Architectures

2012 ◽  
Vol 8 (2) ◽  
pp. 146-157 ◽  
Author(s):  
Yang Xu ◽  
He Hu ◽  
Tang Zhizhong
Keyword(s):  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Vliw Architectures

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

scholarly journals A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment

2020 ◽  
Author(s):  
João Luiz Grave Gross ◽  
Cláudio Fernando Fernando Resin Geyer
Keyword(s):  
Energy Consumption ◽  
Cost Minimization ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Data Intensive ◽  
Battery Capacity ◽  
Cost Efficient

In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.

scholarly journals Using a vision cognitive algorithm to schedule virtual machines

2014 ◽  
Vol 24 (3) ◽  
pp. 535-550 ◽  
Author(s):  
Jiaqi Zhao ◽  
Yousri Mhedheb ◽  
Jie Tao ◽  
Foued Jrad ◽  
Qinghuai Liu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Primary Concern ◽  
Global Optimization Problem ◽  
Vm Scheduling ◽  
Operation Cost

Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

scholarly journals An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users

2015 ◽  
Vol 52 ◽  
pp. 851-857 ◽  
Author(s):  
Ihsan Ullah ◽  
Nadeem Javaid ◽  
Zahoor A. Khan ◽  
Umar Qasim ◽  
Zafar A. Khan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Optimal Energy

Study on Improving the Network Life Time Maximazation for Wireless Sensor Network using Cross Layer Approach

2016 ◽  
Vol 6 (6) ◽  
pp. 3080 ◽  
Author(s):  
Rajiv R Bhandari ◽  
K Rajasekhar
Keyword(s):  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Routing Algorithm ◽  
Life Time ◽  
Packet Transmission ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cross Layer ◽  
Main Task ◽  
Network Life Time

<p>In recent the espousal of Wireless Sensor Networks has been broadly augmented in numerous divisions. Battery operated Sensor nodes in the wireless network accomplish main task of capturing and responding to the surroundings. The lifetime of the network depends on the energy consumption of the sensor nodes. This paper contributes the survey on how the energy consumption should be managed for maximizing the life time of network and how to improve the efficiency of Network by using Cross layer architecture. The traditional MAC Layer, Network Layer &amp; Transport for WLAN having their own downsides just by modifying those we can achieve the network life time maximization goal. This paper represents analytical study for Energy efficiency by modifying Scheduling algorithm, by modifying traditional AODV routing algorithm for efficient packet transmission and by effectively using TCP for End to End Delivery of Data.</p>

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