scholarly journals Dual Threshold Adaptive Dynamic Migration Strategy of Virtual Resources Based on BBU Pool

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 314 ◽  
Author(s):  
Cheng Wang ◽  
Yushi Cao ◽  
Zhili Zhang ◽  
Weidong Wang
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Mobile Communications ◽  
Resource Sharing ◽  
Rapid Development ◽  
Base Stations ◽  
Maintenance Cost ◽  
Migration Strategy ◽  
Continuous Growth ◽  
Adaptive Dynamic

The rapid development of mobile communications and the continuous growth of service needs lead to an increase in the number of base stations (BSs). Through virtualization and cloud technology, virtual Baseband Units (BBUs) are deployed on a virtual machine (VM) to build a BBU pool to achieve hardware resource sharing, which not only saves BS construction costs but also facilitates management and control. However, too high or too low server resource utilization in the pool not only affects the performance of the virtual BBU but also increases the maintenance cost of the physical equipment. In this paper, BBUs are virtualized to construct a virtual BBU pool based on the OpenStack cloud architecture and a dual threshold adaptive dynamic migration strategy is proposed in this scenario. Establish upper and lower threshold of resource utilization of the servers in the pool and the strategy determines whether the dynamic migration is triggered according to the resource utilization of each compute node. If the migration is triggered, the strategy selects the virtual resource to be moved out and the target node to realize the dynamic migration to achieve the purpose of balancing the server load and saving energy consumption. The migration strategy proposed in this paper is simulated on Cloudsim and the experimental results show that the strategy can effectively reduce the number of migrations and migration time on the basis of reducing energy consumption and SLA violations. This paper successfully deployed the strategy on the OpenStack platform, which implements dynamic migration autonomously to save the overall energy consumption of the BBU pool, instead of manual operations.

Study on Carbon Emission Calculation Method of Concrete

2018 ◽  
Vol 768 ◽  
pp. 293-305 ◽  
Author(s):  
Chun Zhi Zhao ◽  
Yi Liu ◽  
Shi Wei Ren ◽  
Jiang Quan
Keyword(s):  
Energy Consumption ◽  
Calculation Method ◽  
Carbon Emission ◽  
Rapid Development ◽  
Reference Value ◽  
Calculation Model ◽  
Emission Data ◽  
Continuous Growth ◽  
Boundary Definition ◽  
Concrete Production

along with the rapid development of commercial concrete industry and the continuous growth of concrete demand, the commercial concrete production has brought large energy consumption and mineral resource consumption; cement calcination and direct/indirect energy consumption within the boundary of ready-mixed concrete system have become the main source of concrete greenhouse gas. This paper mainly settles key problems such as boundary definition, data collection, calculation model, data acceptance/rejection and data calculation method concerned with concrete carbon emission calculation, establishes the national uniform concrete carbon emission calculation method and emission factor within the same cultural boundary, and provides theoretical and data calculation basis for determining the reference value and grade of concrete carbon emission. As for other products, the carbon emission of unit product may also be calculated by reference to this paper; therefore, inherent carbon emission data of buildings are accumulated, providing quantized data support for taking measures to reduce the carbon emission intensity.

The Wind and Light Power Supply System Controller in the Mobile Base Stations

2014 ◽  
Vol 989-994 ◽  
pp. 2778-2781
Author(s):  
Hong Ma
Keyword(s):  
Power Supply ◽  
Power Supply System ◽  
Rapid Development ◽  
Base Station ◽  
Supply System ◽  
Base Stations ◽  
Maintenance Cost ◽  
Light Power ◽  
System Controller ◽  
Mobile Base

With the rapid development of economy, the consumption of energy increasing year by year, the conventional energy is facing increasingly draining.The wind and light power supply system controller in the mobile base stations is a kind of power supply management system,used the wind and light, which have the biggest and the most development potential renewable energy and new energy technology at present.That especially suitable for mobile communication base station,and remote sensing, remote control base station power supply and operation environment. The controller can reduce run maintenance cost, improving the quality of communication and system management level,and the efficiency of the whole.

scholarly journals The Resource Mapping Algorithm of Wireless Virtualized Networks for Saving Energy in Ultradense Small Cells

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Sai Zou ◽  
Fan Yang ◽  
Yulian Tang ◽  
Lei Xiao
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Resource Utilization ◽  
Resource Sharing ◽  
Hot Spot ◽  
Small Cell ◽  
Small Cells ◽  
Saving Energy ◽  
Mapping Algorithm ◽  
Resource Mapping

As the current network is designed for peak loads, it results in insufficient resource utilization and energy waste. Virtualized technology makes it possible that intelligent energy perception network could be deployed and resource sharing could become an effective energy saving technology. How to make more small cells into sleeping state for energy saving in ultradense small cell system has become a research hot spot. Based on the mapping feature of virtualized network, a new wireless resource mapping algorithm for saving energy in ultradense small cells has been put forward when wireless resource amount is satisfied in every small cell. First of all, the method divides the virtual cells. Again through the alternate updating between small cell mapping and wireless resource allocation, least amount of small cells is used and other small cells turn into sleeping state on the premise of guaranteeing users’ QoS. Next, the energy consumption of the wireless access system, wireless resource utilization, and the convergence of the proposed algorithm are analyzed in theory. Finally, the simulation results demonstrate that the algorithm can effectively reduce the system energy consumption and required wireless resource amount under the condition of satisfying users’ QoS.

Research on Resource Scheduling Algorithm in Cloud Computing Data Center

2014 ◽  
Vol 926-930 ◽  
pp. 2050-2053 ◽  
Author(s):  
Yi Zhang ◽  
Yi Min Su
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Resource Sharing ◽  
Rapid Development ◽  
Resource Scheduling ◽  
Research Direction ◽  
Computing Resource ◽  
High Resource ◽  
Cloud Resource

In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research.

scholarly journals EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 690
Author(s):  
Muhammad Ibrahim ◽  
Muhammad Imran ◽  
Faisal Jamil ◽  
Yun Jung Lee ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Services ◽  
Migration Strategy ◽  
Cloud Data ◽  
Sla Violation ◽  
Cloud Data Centers ◽  
Vm Consolidation

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

scholarly journals Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 190
Author(s):  
Wu Ouyang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Genghua Yu ◽  
Heng Zhang
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Markov Decision Process ◽  
Decision Process ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Migration ◽  
Migration Strategy ◽  
Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

HYGIENIC ASSESSMENT OF ELECTROMAGNETIC RADIATION LEVELS OF BASE STATIONS,INSTALLED ON THE ROOFS OF BUILDINGS AND THE TERRITORY OF LAND PLOTS IN TASHKENT

2021 ◽  
Vol 2 (5) ◽  
pp. 5-11
Author(s):  
G. A. Tashpulatova ◽  
◽  
A. N. Krasavin
Keyword(s):  
Public Health ◽  
Electromagnetic Radiation ◽  
Flux Density ◽  
Energy Flux ◽  
Mobile Communications ◽  
Base Station ◽  
Base Stations ◽  
Cellular Communications ◽  
Measurement Results ◽  
Energy Flux Density

This article is about instrumental measurements of the FR EMR energy flux density. The measurement results were analyzed with the division of the data obtained by the purpose of buildings and the height of the antenna equipment placement, a hygienic assessment of the RF EMR levels created by the equipment of base stations of cellular communications, installed on the roofs of residential and public buildings and adjacent territories of Tashkent is given. A proposal is made on the rational placement of radio engineering facilities.Keywords:electromagnetic field; electromagnetic safety; base station for mobile communications; protection of public health; sanitary supervision

scholarly journals A Heuristic Evolutionary Algorithm of UAV Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhangjie Fu ◽  
Jingnan Yu ◽  
Guowu Xie ◽  
Yiming Chen ◽  
Yuanhang Mao
Keyword(s):  
Energy Consumption ◽  
Evolutionary Algorithm ◽  
Rapid Development ◽  
Optimal Path ◽  
Battery Life ◽  
Intelligent Robot ◽  
Urgent Problem ◽  
Intelligent Robots ◽  
Aerial Vehicle ◽  
The Cost

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

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